Saturday Morning Videos: AutoML Workshop at ICML 2019

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Katharina Eggensperger, Matthias Feurer, Frank Hutter, and Joaquin Vanschoren organized the AutoML workshop at ICML, and there are videos of the event that took place yesterday. Awesome.! Here is the intro for the workshop:

Machine learning has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for off-the-shelf machine learning methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. The workshop targets a broad audience ranging from core machine learning researchers in different fields of ML connected to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and learning to learn, to domain experts aiming to apply machine learning to new types of problems.

All the videos are here.

• Keynote by Peter Frazier: Grey-box Bayesian Optimization for AutoML

Bayesian optimization is a powerful and flexible tool for AutoML. While BayesOpt was first deployed for AutoML simply as a black-box optimizer, recent approaches perform grey-box optimization: they leverage capabilities and problem structure specific to AutoML such as freezing and thawing training, early stopping, treating cross-validation error minimization as multi-task learning, and warm starting from previously tuned models. We provide an overview of this area and describe recent advances for optimizing sampling-based acquisition functions that make grey-box BayesOpt significantly more efficient.

• Keynote by Rachel Thomas: Lessons Learned from Helping 200,000 non-ML experts use ML

The mission of AutoML is to make ML available for non-ML experts and to accelerate research on ML. We have a very similar mission at fast.ai and have helped over 200,000 non-ML experts use state-of-the-art ML (via our research, software, & teaching), yet we do not use methods from the AutoML literature. I will share several insights we've learned through this work, with the hope that they may be helpful to AutoML researchers.

• Contributed Talk 1: A Boosting Tree Based AutoML System for Lifelong Machine Learning, Zheng Xiong, Wenpeng Zhang, Jiyan Jiang and Wenwu Zhu

AutoML aims at automating the process of designing good machine learning pipelines to solve different kinds of problems. However, existing AutoML systems are mainly designed for isolated learning by training a static model on a single batch of data; while in many real-world applications, data may arrive continuously in batches, possibly with concept drift. This raises a lifelong machine learning challenge for AutoML, as most existing AutoML systems can not evolve over time to learn from streaming data and adapt to concept drift. In this paper, we propose a novel AutoML system for this new scenario, i.e. a boosting tree based AutoML system for lifelong machine learning, which won the second place in the NeurIPS 2018 AutoML Challenge. 

• Keynote by Jeff Dean: An Overview of Google's Work on AutoML and Future Directions

In this talk I'll survey work by Google researchers over the past several years on the topic of AutoML, or learning-to-learn. The talk will touch on basic approaches, some successful applications of AutoML to a variety of domains, and sketch out some directions for future AutoML systems that can leverage massively multi-task learning systems for automatically solving new problems.

• Contributed Talk 2: Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents, Zalán Borsos, Andrey Khorlin and Andrea Gesmundo

Recent advances in Neural Architecture Search (NAS) have produced state-of-the-art architectures on several tasks. NAS shifts the efforts of human experts from developing novel architectures directly to designing architecture search spaces and methods to explore them efficiently. The search space definition captures prior knowledge about the properties of the architectures and it is crucial for the complexity and the performance of the search algorithm. However, different search space definitions require restarting the learning process from scratch. We propose a novel agent based on the Transformer that supports joint training and efficient transfer of prior knowledge between multiple search spaces and tasks.

• Contributed Talk 3: Random Search and Reproducibility for Neural Architecture Search, Liam Li  and Ameet Talwalkar

Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) NAS is a specialized hyperparameter optimization problem; and (ii) random search is a competitive baseline for hyperparameter optimization. Leveraging these observations, we evaluate both random search with early-stopping and a novel random search with weight-sharing algorithm on two standard NAS benchmarks—PTB and CIFAR-10. Our results show that random search with early-stopping is a competitive NAS baseline, e.g., it performsat least as well as ENAS, a leading NAS method, on both benchmarks. Additionally, random search with weight-sharing outperforms random search with early-stopping, achieving a state-of-the-art NAS result onPTB and a highly competitive result on CIFAR-10. Finally, we explore the existing reproducibility issues of published NAS results.

• Keynote by Charles Sutton: Towards Semi-Automated Machine Learning

The practical work of deploying a machine learning system is dominated by issues outside of training a model: data preparation, data cleaning, understanding the data set, debugging models, and so on. What does it mean to apply ML to this “grunt work” of machine learning and data science? I will describe first steps towards tools in these directions, based on the idea of semi-automating ML: using unsupervised learning to find patterns in the data that can be used to guide data analysts. I will also describe a new notebook system for pulling these tools together: if we augment Jupyter-style notebooks with data-flow and provenance information, this enables a new class of data-aware notebooks which are much more natural for data manipulation.

Panel Discussion

• Rachel Thomas (fast.ai & USF Data Institute)
• Charles Sutton (Google)
• Liam Li (Carnegie Mellon University)
• Erin LeDell (H20.ai)
• Jeff Clune (Uber)

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Saturday Morning Videos: ICML 2016 Plenary, Tutorials and more....

  

 

The videos for ICML 2016 are out ! They are all here: http://techtalks.tv/icml/2016/orals/

Thank you to the organizing committee to making these videos available. Here are the plenary and tutorial talks

Plenary

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  Causal Inference for Policy Evaluation Authors: Susan Athey, Stanford Graduate School of Business
  • Watch Video
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  A Quest for Visual Intelligence in Computers Authors: Fei-Fei Li, Stanford University
  • Watch Video
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  Dynamic topic models Authors: David Blei and John Lafferty
  • Watch Video
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  Laplacian Matrices of Graphs: Algorithms and Applications. Authors: Daniel Spielman, Yale University
  • Watch Video
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  Mining Large Graphs: Patterns, Anomalies, and Fraud Detection Authors: Christos Faloutsos, Carnegie Mellon University
  • Watch Video

 

 Tutorials

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  Causal inference for observational studies Authors: David Sontag and Uri Shalit (New York University)
  
  
  • Watch Video
  
  
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  Memory Networks for Language Understanding Authors: Jason Weston (Facebook)
  • Watch Video 
  
   
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  Recent Advances in Non-Convex Optimization Authors: Anima Anandkumar (University of California Irvine)
  • Watch Video
  
  
  
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  Deep Residual Networks: Deep Learning Gets Way Deeper Authors: Kaiming He, Facebook
  • Watch Video
  
  
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  Stochastic Gradient Authors:
  • Watch Video
  
  
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  Deep Reinforcement Learning Authors: David Silver (Google DeepMind)
  • Watch Video
  
  
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  The convex optimization, game-theoretic approach to learning Authors: Elad Hazan (Princeton University) and Satyen Kale (Yahoo Research)
  • Watch Video
  
  
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  Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis Authors: Moritz Hardt (Google) and Aaron Roth (University of Pennsylvania)
  • Watch Video
  
  
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  Graph Sketching, Streaming, and Space-Efficient Optimization Authors: Sudipto Guha (University of Pennsylvania) and Andrew McGregor (University of Massachusetts Amherst)
  

  • Watch Video

   

 
 
 
 
 
 
 

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Slides, papers some videos: ICML, CVPR, NIPS

 

From Aaron's twitter feed

As you all know the proceedings for the NIPS 2016 is here. At CVPR, Yann LeCun will present the following  What's Wrong Deep Learning?. He also posted a video of Larry Jackel at the "Back to the Future" workshop at ICML.

All the papers at ICML are here. You can also follow Hugo Larochelle who streams some talks through periscope.

All ICML tutorials can be found here: They include the following:  Causal Inference for Policy Evaluation, Susan Athey  but also there:

Deep Reinforcement Learning

David Silver (Google DeepMind)

A major goal of artificial intelligence is to create general-purpose agents that can perform effectively in a wide range of challenging tasks. To achieve this goal, it is necessary to combine reinforcement learning (RL) agents with powerful and flexible representations. The key idea of deep RL is to use neural networks to provide this representational power. In this tutorial we will present a family of algorithms in which deep neural networks are used for value functions, policies, or environment models. State-of-the-art results will be presented in a variety of domains, including Atari games, 3D navigation tasks, continuous control domains and the game of Go.
[slides1] [slides2]

Memory Networks for Language Understanding

Jason Weston (Facebook)

There has been a recent resurgence in interest in the use of the combination of reasoning, attention and memory for solving tasks, particularly in the field of language understanding. I will review some of these recent efforts, as well as focusing on one of my own group’s contributions, memory networks, an architecture that we have applied to question answering, language modeling and general dialog. As we try to move towards the goal of true language understanding, I will also discuss recent datasets and tests that have been built to assess these models abilities to see how far we have come.

Deep Residual Networks: Deep Learning Gets Way Deeper

Kaiming He (Facebook, starting July, 2016)

Deeper neural networks are more difficult to train. Beyond a certain depth, traditional deeper networks start to show severe underfitting caused by optimization difficulties. This tutorial will describe the recently developed residual learning framework, which eases the training of networks that are substantially deeper than those used previously. These residual networks are easier to converge, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with depth of up to 152 layers—8x deeper than VGG nets but still having lower complexity. These deep residual networks are the foundations of our 1st-place winning entries in all five main tracks in ImageNet and COCO 2015 competitions, which cover image classification, object detection, and semantic segmentation.

In this tutorial we will further look into the propagation formulations of residual networks. Our latest work reveals that when the residual networks have identity mappings as skip connections and inter-block activations, the forward and backward signals can be directly propagated from one block to any other block. This leads us to promising results of 1001-layer residual networks. Our work suggests that there is much room to exploit the dimension of network depth, a key to the success of modern deep learning.
[slides]

Recent Advances in Non-Convex Optimization

Anima Anandkumar (University of California Irvine)

Most machine learning tasks require solving non-convex optimization.  The number of critical points in a non-convex problem grows exponentially with the data dimension. Local search methods such as gradient descent can  get stuck in one of these critical points, and therefore, finding the globally optimal solution is computationally hard. Despite this hardness barrier, we have seen many advances in guaranteed non-convex optimization.  The focus has shifted to characterizing transparent conditions under which the global solution can be found efficiently. In many instances, these conditions turn out to be mild and natural for machine learning applications. This tutorial will provide an overview of the recent theoretical success stories in non-convex optimization. This includes learning latent variable models, dictionary learning, robust principal component analysis, and so on. Simple iterative methods such as spectral methods, alternating projections, and so on, are proven to learn consistent models with polynomial sample and computational complexity.  This tutorial will present main ingredients towards establishing these results. The tutorial with conclude  with open challenges and possible path towards tackling them.

Stochastic Gradient Methods for Large-Scale Machine Learning

Leon Bottou (Facebook AI Research), Frank E. Curtis (Lehigh University), and Jorge Nocedal (Northwestern University)

This tutorial provides an accessible introduction to the mathematical properties of stochastic gradient methods and their consequences for large scale machine learning.  After reviewing the computational needs for solving optimization problems in two typical examples of large scale machine learning, namely, the training of sparse linear classifiers and deep neural networks, we present the theory of the simple, yet versatile stochastic gradient algorithm, explain its theoretical and practical behavior, and expose the opportunities available for designing improved algorithms.  We then provide specific examples of advanced algorithms to illustrate the two essential directions for improving stochastic gradient methods, namely, managing the noise and making use of second order information.
[slides1] [slides2] [slides3]

The convex optimization, game-theoretic approach to learning

Elad Hazan (Princeton University) and Satyen Kale (Yahoo Research)

In recent years convex optimization and the notion of regret minimization in games have been combined and applied to machine learning in a general framework called online convex optimization. We will survey the basics of this framework, its applications, main algorithmic techniques and future research directions.

Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis

Moritz Hardt (Google) and Aaron Roth (University of Pennsylvania)

Reliable tools for inference and model selection are necessary in all applications of machine learning and statistics. Much of the existing theory breaks down in the now common situation where the data analyst works interactively with the data, adaptively choosing which methods to use by probing the same data many times. We illustrate the problem through the lens of machine learning benchmarks, which currently all rely on the standard holdout method. After understanding why and when the standard holdout method fails, we will see practical alternatives to the holdout method that can be used many times without losing the guarantees of fresh data. We then transition into the emerging theory on this topic touching on deep connections to differential privacy, compression schemes, and hypothesis testing (although no prior knowledge will be assumed).

Graph Sketching, Streaming, and Space-Efficient Optimization

Sudipto Guha (University of Pennsylvania) and Andrew McGregor (University of Massachusetts Amherst)

Graphs ae one of the most commonly used data representation tools but existing algorithmicapproaches are typically not appropriate when the graphs of interest are dynamic, stochastic, ordo not fit into the memory of a single machine. Such graphs are often encountered as machinelearning techniques are increasingly deployed to manage graph data and large-scale graph opti-mization problems. Graph sketching is a form of dimensionality reduction for graph data that isbased on using random linear projections and exploiting connections between linear algebra andcombinatorial structure. The technique has been studied extensively over the last five years andcan be applied in many computational settings. It enables small-space online and data streamcomputation where we are permitted only a few passes (ideally only one) over an input sequence ofupdates to a large underlying graph. The technique parallelizes easily and can naturally be appliedin various distributed settings. It can also be used in the context of convex programming to enablemore efficient algorithms for combinatorial optimization problems such as correlation clustering. One of the main goals of the research on graph sketching is understanding and characterizing thetypes of graph structure and features that can be inferred from compressed representations of the relevant graphs.
[slides1] [slides2]

Causal inference for observational studies

David Sontag and Uri Shalit (New York University)

In many fields such as healthcare, education, and economics, policy makers have increasing amounts of data at their disposal. Making policy decisions based on this data often involves causal questions: Does medication X lead to lower blood sugar, compared with medication Y? Does longer maternity leave lead to better child social and cognitive skills? These questions have to be addressed in practice, every day, by scientists working across many different disciplines.

The goal of this tutorial is to bring machine learning practitioners closer to the vast field of causal inference as practiced by statisticians, epidemiologists and economists. We believe that machine learning has much to contribute in helping answer such questions, especially given the massive growth in the available data and its complexity. We also believe the machine learning community could and should be highly interested in engaging with such problems, considering the great impact they have on society in general.

We hope that participants in the tutorial will: a) learn the basic language of causal inference as exemplified by the two most dominant paradigms today: the potential outcomes framework, and causal graphs; b) understand the similarities and the differences between problems machine learning practitioners usually face and problems of causal inference; c) become familiar with the basic tools employed by practicing scientists performing causal inference, and d) be informed about the latest research efforts in bringing machine learning techniques to address problems of causal inference.

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Training Neural Networks Without Gradients: A Scalable ADMM Approach

Training Neural Networks Without Gradients: A Scalable ADMM Approach by Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conventional optimization algorithms rely on stochastic gradient methods that don't scale well to large numbers of cores in a cluster setting. Furthermore, the convergence of all gradient methods, including batch methods, suffers from common problems like saturation effects, poor conditioning, and saddle points. This paper explores an unconventional training method that uses alternating direction methods and Bregman iteration to train networks without gradient descent steps. The proposed method reduces the network training problem to a sequence of minimization sub-steps that can each be solved globally in closed form. The proposed method is advantageous because it avoids many of the caveats that make gradient methods slow on highly non-convex problems. The method exhibits strong scaling in the distributed setting, yielding linear speedups even when split over thousands of cores.

 
 
 
 

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ICML 2016 accepted papers are out !

And here they are:

 from the main page:

Accepted Papers
• No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing ,Nihar Shah UC Berkeley, Dengyong Zhou Microsoft ResearchPaper | Abstract
• Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues ,Nihar Shah UC Berkeley, Sivaraman Balakrishnan CMU, Aditya Guntuboyina UC Berkeley, Martin Wainwright UC BerkeleyPaper | Abstract
• Uprooting and Rerooting Graphical Models ,Adrian Weller University of CambridgePaper | Abstract
• A Deep Learning Approach to Unsupervised Ensemble Learning ,Uri Shaham Yale University, Xiuyuan Cheng , Omer Dror , Ariel Jaffe , Boaz Nadler , Joseph Chang , Yuval Kluger Paper | Abstract
• Revisiting Semi-Supervised Learning with Graph Embeddings ,Zhilin Yang Carnegie Mellon University, Ruslan Salakhudinov U. of Toronto, William Cohen CMUPaper | Abstract
• Inverse Optimal Control with Deep Networks via Policy Optimization ,Chelsea Finn UC Berkeley, Sergey Levine , Pieter Abbeel BerkeleyPaper | Abstract
• Diversity-Promoting Bayesian Learning of Latent Variable Models ,Pengtao Xie Carnegie Mellon University, Jun Zhu Tsinghua, Eric Xing CMUPaper | Abstract
• Additive Approximations in High Dimensional Regression via the SALSA ,Kirthevasan Kandasamy Carnegie Mellon University, Yaoliang Yu Paper | Abstract
• Hawkes Processes with Stochastic Excitations ,Young Lee NICTA, Kar Wai Lim ANU, Cheng Soon Ong NICTAPaper | Abstract
• Data-driven Rank Breaking for Efficient Rank Aggregation ,Sewoong Oh UIUC, Ashish Khetan UIUCPaper | Abstract
• Dropout distillation ,Samuel Rota Bulò FBK, Lorenzo Porzi FBK, Peter Kontschieder Microsoft Research CambridgePaper | Abstract
• Metadata-conscious anonymous messaging ,Giulia Fanti UIUC, Peter Kairouz UIUC, Sewoong Oh UIUC, Pramod Viswanath UIUC, kannan ramchandran Paper | Abstract
• The Teaching Dimension of Linear Learners ,Ji Liu University of Rochester, Xiaojin Zhu University of Wisconsin, Hrag Ohannessian University of Wisconsin-MadisonPaper | Abstract
• Truthful Univariate Estimators ,Ioannis Caragiannis University of Patras, Ariel Procaccia Carnegie Mellon University, Nisarg Shah Carnegie Mellon UniversityPaper | Abstract
• Why Regularized Auto-Encoders learn Sparse Representation? ,Devansh Arpit SUNY Buffalo, Yingbo Zhou SUNY Buffalo, Hung Ngo SUNY Buffalo, Venu Govindaraju SUNY BuffaloPaper | Abstract
• k-variates++: more pluses in the k-means++ ,Richard Nock Nicta & ANU, Raphael Canyasse Ecole Polytechnique and The Technion, Roksana Boreli Data61, Frank Nielsen Ecole Polytechnique and Sony CS Labs Inc.Paper | Abstract
• Multi-Player Bandits — a Musical Chairs Approach ,Jonathan Rosenski Weizmann Institute of Science, Ohad Shamir Weizmann Institute of Science, Liran Szlak Weizmann Institute of SciencePaper | Abstract
• The Information Sieve ,Greg Ver Steeg Information Sciences Institute, Aram Galstyan Information Sciences InstitutePaper | Abstract
• End-to-End Speech Recognition in English and Mandarin ,Sanjeev Satheesh Baidu SVAIL, Dario Amodei , Rishita Anubhai , Eric Battenberg , Carl Case , Jared Casper , Bryan Catanzaro , Mike Chrzanowski Baidu USA, Adam Coates , Greg Diamos Baidu USA, Erich Elsen Baidu USA, Jesse Engel , Christopher Fougner , Awni Hannun Baidu USA, Billy Jun , Patrick LeGresley , Sharan Narang , Andrew Ng , Sherjil Ozair , Ryan Prenger , Jonathan Raiman , David Seetapun , Shubho Sengupta , Chong Wang , Yi Wang , Libby Lin , Tony Han , zhenyao Zhu , Dani Yogatama , Bo Xiao , JingDong Chen , Zhiqian Wang , Jun Zhan , Linxi Fan Paper | Abstract
• On the Consistency of Feature Selection With Lasso for Non-linear Targets ,Yue Zhang Case Western Reserve University, Weihong Guo Case Western Reserve University, Soumya Ray Paper | Abstract
• Minimum Regret Search for Single- and Multi-Task Optimization ,Jan Hendrik Metzen University BremenPaper | Abstract
• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy ,Nathan Dowlin Princeton, Ran , Kim Laine Microsoft Research, Kristin Lauter Microsoft Research, Michael Naehrig Microsoft Research, John Wernsing Microsoft ResearchPaper | Abstract
• The Variational Nystrom method for large-scale spectral problems ,Max Vladymyrov Yahoo Labs, Miguel Carreira-Perpinan UC MercedPaper | Abstract
• MBA: Multi-Bias Non-linear Activation in Deep Neural Networks ,Hongyang Li The Chinese Univ. of HK, Wanli Ouyang , Xiaogang Wang Paper | Abstract
• Asymmetric Multi-task Learning based on Task Relatedness and Confidence ,Giwoong Lee UNIST, Eunho Yang IBM Research, Sung ju Hwang UNISTPaper | Abstract
• Accurate Robust and Efficient Error Estimation for Decision Trees ,Lixin Fan Nokia TechnologiesPaper | Abstract
• Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity ,Ohad Shamir Weizmann Institute of SciencePaper | Abstract
• Convergence of Stochastic Gradient Descent for PCA ,Ohad Shamir Weizmann Institute of SciencePaper | Abstract
• Dealbreaker: A Nonlinear Latent Variable Model for Educational Data ,Andrew Lan Rice University, Tom Goldstein University of Maryland, Christoph Studer Cornell University, Richard Baraniuk Rice UniversityPaper | Abstract
• A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation ,qiang liu , Jason Lee UC Berkeley, Michael Paper | Abstract
• Variable Elimination in the Fourier Domain ,Yexiang Xue Cornell University, Stefano Ermon , Ronan Le Bras Cornell University, Carla , Bart Paper | Abstract
• Low-Rank Matrix Approximation with Stability ,Dongsheng Li IBM Research – China, Chao Chen , Qin Lv , Junchi Yan , Li Shang , Stephen Chu Paper | Abstract
• Linking losses for density ratio and class-probability estimation ,Aditya Menon National ICT Australia, Cheng Soon Ong NICTAPaper | Abstract
• Stochastic Variance Reduction for Nonconvex Optimization ,Sashank J. Reddi Carnegie Mellon University, Ahmed Hefny , Suvrit Sra , Barnabas Poczos , Alex Paper | Abstract
• Hierarchical Variational Models ,Rajesh Ranganath , Dustin Tran Columbia University, Blei David ColumbiaPaper | Abstract
• Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams ,Roy Adams Univ. of Massachusetts-Amherst, Nazir Saleheen , Edison Thomaz , Abhinav Parate , Santosh Kumar , Ben Marlin Paper | Abstract
• Binary embeddings with structured hashed projections ,Anna Choromanska Courant Institute, Krzysztof Choromanski Google Research NYC, Mariusz Bojarski NVIDIA, Tony Jebara Columbia, Sanjiv Kumar , Yann Paper | Abstract
• A Variational Analysis of Stochastic Gradient Algorithms ,Stephan Mandt Columbia University, Matthew Hoffman Adobe Research, Blei David ColumbiaPaper | Abstract
• Adaptive Sampling for SGD by Exploiting Side Information ,Siddharth Gopal Paper | Abstract
• Learning from Multiway Data: Simple and Efficient Tensor Regression ,Rose Yu University of Southern Cal, Yan Liu Paper | Abstract
• A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models ,Trong Nghia Hoang NUS, Quang Minh Hoang NUS, Bryan Kian Hsiang Low NUSPaper | Abstract
• Online Stochastic Linear Optimization under One-bit Feedback ,Lijun Zhang Nanjing University, Tianbao Yang University of Iowa, Rong Jin Alibaba Group, Yichi Xiao Nanjing University, Zhi-hua Zhou Paper | Abstract
• Adaptive Algorithms for Online Convex Optimization with Long-term Constraints ,Rodolphe Jenatton , Jim Huang Amazon, Cedric Archambeau Paper | Abstract
• Actively Learning Hemimetrics with Applications to Eliciting User Preferences ,Adish Singla , Sebastian Tschiatschek ETH Zurich, Andreas Krause Paper | Abstract
• Learning Simple Algorithms from Examples ,Wojciech Zaremba , Tomas , Armand Joulin , Rob Fergus Facebook AI ResearchPaper | Abstract
• Learning Physical Intuition of Block Towers by Example ,Adam Lerer Facebook AI Research, Rob Fergus Facebook AI ResearchPaper | Abstract
• Structure Learning of Partitioned Markov Networks ,Song Liu The Inst. of Stats. Math., Taiji Suzuki , Masashi Sugiyama University of Tokyo, Kenji Paper | Abstract
• Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient ,Tianbao Yang University of Iowa, Lijun Zhang Nanjing University, Rong Jin , Jinfeng Yi IBM ResearchPaper | Abstract
• Beyond CCA: Moment Matching for Multi-View Models ,Anastasia Podosinnikova INRIA – ENS, Francis Bach Inria, Simon Lacoste-Julien INRIAPaper | Abstract
• Fast Methods for Estimating the Numerical Rank of Large Matrices ,Shashanka Ubaru University of Minnesota, Yousef Saad University of MinnesotaPaper | Abstract
• Unsupervised Deep Embedding for Clustering Analysis ,Junyuan Xie University of Washington, Ross Girshick Facebook, Ali Farhadi University of WashingtonPaper | Abstract
• Efficient Private Empirical Risk Minimization for High-dimensional Learning ,Shiva Kasiviswanathan Samsung Research America, Hongxia Jin Samsung Research AmericaPaper | Abstract
• Parameter Estimation for Generalized Thurstone Choice Models ,Milan Vojnovic Microsoft, Seyoung Yun MicrosoftPaper | Abstract
• Large-Margin Softmax Loss for Convolutional Neural Networks ,Weiyang Liu Peking University, Yandong Wen South China University of Technology, Zhiding Yu Carnegie Mellon University, Meng Yang Shenzhen UniversityPaper | Abstract
• A Random Matrix Approach to Recurrent Neural Networks ,Romain Couillet CentraleSupelec, Gilles Wainrib ENS Ulm, Hafiz Tiomoko Ali CentraleSupelec, Harry Sevi ENS LyonPaper | Abstract
• Supervised and Semi-Supervised Text Categorization using One-Hot LSTM for Region Embeddings ,Rie Johnson RJ Research Consulting, Tong Zhang Paper | Abstract
• Optimality of Belief Propagation for Crowdsourced Classification ,Jungseul Ok KAIST, Sewoong Oh UIUC, Jinwoo Shin KAIST, Yung Yi KAISTPaper | Abstract
• Stability of Controllers for Gaussian Process Forward Models ,Julia Vinogradska Robert Bosch GmBH, Bastian Bischoff , Duy Nguyen-Tuong , Anne Romer , Henner Schmidt , Jan Peters Paper | Abstract
• Learning privately from multiparty data ,Jihun Hamm , Yingjun Cao UC-San Diego, Mikhail Belkin Paper | Abstract
• Network Morphism ,Tao Wei University at Buffalo, Changhu Wang Microsoft Research, Yong Rui Microsoft Research, Chang Wen Chen Paper | Abstract
• A Kronecker-factored approximate Fisher matrix for convolution layers ,Roger Grosse , James Martens University of TorontoPaper | Abstract
• Experimental Design on a Budget for Sparse Linear Models and Applications ,Sathya Narayanan Ravi UW Madison, Vamsi Ithapu UW Madison, Vikas Singh , Sterling Johnson UW MadisonPaper | Abstract
• Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVM ,Anton Osokin , Jean-Baptiste Alayrac ENS, Puneet Dokania INRIA and Ecole Centrale Paris, Simon Lacoste-Julien INRIAPaper | Abstract
• Exact Exponent in Optimal Rates for Crowdsourcing ,Chao Gao Yale University, Yu Lu Yale University, Dengyong Zhou Microsoft ResearchPaper | Abstract
• Augmenting Neural Networks with Reconstructive Decoding Pathways for Large-scale Image Classification ,Yuting Zhang University of Michigan, Kibok Lee University of Michigan, Honglak Lee University of MichiganPaper | Abstract
• Online Low-Rank Subspace Clustering by Explicit Basis Modeling ,Jie Shen Rutgers University, Ping Li Rutgers, Huan Xu Paper | Abstract
• A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization ,Frank Curtis Lehigh UniversityPaper | Abstract
• Stochastic Quasi-Newton Langevin Monte Carlo ,Umut Simsekli Telecom ParisTech, Roland Badeau , Taylan Cemgil , Gaël Richard Paper | Abstract
• Doubly Robust Off-policy Value Evaluation for Reinforcement Learning ,Nan Jiang University of Michigan, Lihong Li MicrosoftPaper | Abstract
• Fast Rate Analysis of Some Stochastic Optimization Algorithms ,Chao Qu Nus, Huan Xu , Chong jin Ong NusPaper | Abstract
• Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing ,Ke Li UC Berkeley, Jitendra Malik Paper | Abstract
• Smooth Imitation Learning ,Hoang Le Caltech, Andrew Kang , Yisong Yue Caltech, Peter Carr Paper | Abstract
• Community Recovery in Graphs with Locality ,Yuxin Chen Stanford University, Govinda Kamath Stanford University, Changho Suh KAIST, David Tse Stanford UniversityPaper | Abstract
• Variance Reduction for Faster Non-Convex Optimization ,Zeyuan Allen-Zhu Princeton University, Elad Hazan Princeton UniversityPaper | Abstract
• Loss factorization, weakly supervised learning and label noise robustness ,Giorgio Patrini ANU / Data61, Frank Nielsen Ecole Polytechnique and Sony CS Labs Inc., Richard Nock Nicta & ANU, Marcello Carioni Max Planck InstitutePaper | Abstract
• Analysis of Deep Neural Networks with Extended Data Jacobian Matrix ,Shengjie Wang University of Washington, Abdel-rahman Mohamed , Rich Caruana Microsoft, Jeff Bilmes U. of Washington, Matthai Plilipose , Matthew Richardson , Krzysztof Geras , Gregor Urban UC Irvine, Ozlem Aslan Paper | Abstract
• Doubly Decomposing Nonparametric Tensor Regression ,Masaaki Imaizumi University of Tokyo, Kohei Hayashi NIIPaper | Abstract
• Hyperparameter optimization with approximate gradient ,Fabian Pedregosa INRIAPaper | Abstract
• SDCA without Duality, Regularization, and Individual Convexity ,Shai Shalev-Shwartz Hebrew University of JerusalemPaper | Abstract
• Heteroscedastic Sequences: Beyond Gaussianity ,Oren , Shie Mannor TechnionPaper | Abstract
• A Neural Autoregressive Approach to Collaborative Filtering ,Yin Zheng Hulu LLC., Bangsheng Tang Hulu LLC, Wenkui Ding Hulu LLC., Hanning Zhou HuluPaper | Abstract
• On the Quality of the Initial Basin in Overspecified Neural Networks ,Itay Safran Weizmann Institute of Science, Ohad Shamir Weizmann Institute of SciencePaper | Abstract
• Primal-Dual Rates and Certificates ,Celestine Dünner IBM Research, Simone Forte Google, Martin Takac , Martin Jaggi Paper | Abstract
• Minimizing the Maximal Loss: How and Why ,Shai Shalev-Shwartz Hebrew University of Jerusalem, Yonatan Wexler OrcamPaper | Abstract
• The Sample Complexity of Subspace Clustering with Missing Data ,Daniel Pimentel-Alarcon UW-Madison, Robert Nowak Paper | Abstract
• Online Learning with Feedback Graphs Without the Graphs ,Alon Cohen Technion, Tamir Hazan , Tomer Koren Paper | Abstract
• PAC learning of Probabilistic Automaton based on the Method of Moments ,Hadrien Glaude University of Lille, Olivier Pietquin Univ. LillePaper | Abstract
• Estimating Structured Vector Autoregressive Models ,Igor Melnyk University of Minnesota, Arindam Banerjee Paper | Abstract
• Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends ,Christopher Tosh UC San DiegoPaper | Abstract
• Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms ,Mathieu Blondel NTT, Masakazu Ishihata NTT Communication Science Labo, Akinori Fujino NTT, Naonori Ueda Paper | Abstract
• A New PAC-Bayesian Perspective on Domain Adaptation ,Pascal Germain INRIA, Amaury Habrard , François Laviolette GRAAL, Emilie Morvant Paper | Abstract
• Correlation Clustering and Biclustering with Locally Bounded Errors ,Gregory Puleo UIUC, Olgica Milenkovic UIUCPaper | Abstract
• PAC Lower Bounds and Efficient Algorithms for The Max K
• -Armed Bandit Problem ,Yahel David Technion, Nahum Shimkin TechnionPaper | Abstract
• A Comparative Analysis and Study of Multiview Convolutional Neural Network Models for Joint Object Categorization and Pose Estimation ,Mohamed Elhoseiny Rutgers University, Tarek El-Gaaly Rutgers University, Amr Bakry Rutgers University, Ahmed Elgammal Rutgers UniversityPaper | Abstract
• BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces ,Shane Carr Washington University in St. L, Roman Garnett Wustl, Cynthia Lo Washington University in St. LouisPaper | Abstract
• On the Iteration Complexity of Oblivious First-Order Optimization Algorithms ,Yossi Arjevani Weizmann Institute of Science, Ohad Shamir Weizmann Institute of SciencePaper | Abstract
• Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ,Xingguo Li University of Minnesota, Tuo Zhao , Raman Arora Johns Hopkins University, Han , Jarvis Haupt Paper | Abstract
• Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation ,David Wipf Microsoft ResearchPaper | Abstract
• Fast k-means with accurate bounds ,James Newling Idiap Research Institute, Francois Fleuret Idiap research institutePaper | Abstract
• Boolean Matrix Factorization and Noisy Completion via Message Passing ,Siamak Ravanbakhsh CMU, Barnabas Poczos , Russell Greiner University of AlbertaPaper | Abstract
• Convolutional Rectifier Networks as Generalized Tensor Decompositions ,Nadav Cohen Hebrew University of Jerusalem, Amnon Shashua MobileyePaper | Abstract
• Low-rank Solutions of Linear Matrix Equations via Procrustes Flow ,Stephen Tu UC Berkeley, Ross Boczar UC Berkeley, Max Simchowitz UC Berkeley, mahdi Soltanolkotabi , Ben Recht BerkeleyPaper | Abstract
• Anytime Exploration for Multi-armed Bandits using Confidence Information ,Kwang-Sung Jun UW-Madison, Robert Nowak Paper | Abstract
• Structured Prediction Energy Networks ,David Belanger University of Massachusetts Am, Andrew McCallum Paper | Abstract
• L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ,Yuchen Zhang , Jason Lee UC Berkeley, Michael Paper | Abstract
• Compressive Spectral Clustering ,Nicolas TREMBLAY INRIA Rennes, Gilles Puy INRIA, Remi Gribonval INRIA, Pierre Vandergheynst EPFLPaper | Abstract
• Low-rank tensor completion: a Riemannian manifold preconditioning approach , Hiroyuki Kasai The University of Electro-Comm, Bamdev Mishra Amazon Development Centre IndiaPaper | Abstract
• Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow ,Huishuai Zhang Syracuse University, Yuejie Chi Ohio State University, Yingbin Liang Syracuse UniversityPaper | Abstract
• Estimating Maximum Expected Value through Gaussian Approximation ,Carlo D’Eramo Politecnico di Milano, Marcello Restelli Politecnico di Milano, Alessandro Nuara Politecnico di MilanoPaper | Abstract
• Representational Similarity Learning with Application to Brain Networks ,Urvashi Oswal University of Wisconsin, Christopher Cox University of Wisconsin, Timothy Rogers University of Wisconsin, Robert Nowak Paper | Abstract
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ,Yarin Gal University of Cambridge, Zoubin Paper | Abstract
• Generative Adversarial Text to Image Synthesis ,Scott Reed , Zeynep Akata Max Planck Institute for Informatics, Xinchen Yan University of Michigan, Lajanugen Logeswaran University of Michigan – Ann Arbor, Honglak Lee University of Michigan, Bernt Schiele Paper | Abstract
• Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data ,Sandhya Prabhakaran Columbia University, Elham Azizi Columbia University, Ambrose Carr Columbia University, Dana Pe’er Columbia UniversityPaper | Abstract
• Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives ,Zeyuan Allen-Zhu Princeton University, Yang Yuan CornellPaper | Abstract
• Sparse Parameter Recovery from Aggregated Data ,Avradeep Bhowmik University of Texas at Austin, Joydeep , Oluwasanmi Koyejo Stanford University & University of Illinois at Urbana ChampaignPaper | Abstract
• Deep Structured Energy Based Models for Anomaly Detection ,Shuangfei Zhai Binghamton University, Yu Cheng IBM Research, Zhongfei Zhang Binghamton UniversityPaper | Abstract
• Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling ,Zeyuan Allen-Zhu Princeton University, Zheng Qu The University of Hong Kong, Peter Richtarik , Yang Yuan CornellPaper | Abstract
• Unitary Evolution Recurrent Neural Networks ,Martin Arjovsky University of Buenos Aires, Amar Shah University of Cambridge, Yoshua Bengio Paper | Abstract
• Markov Latent Feature Models ,Aonan Zhang Columbia University, John Paisley Paper | Abstract
• The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks ,Yingfei Wang Princeton University, Chu Wang , Warren Powell Paper | Abstract
• A Simple and Provable Algorithm for Sparse CCA ,Megasthenis Asteris University of Texas at Austin, Anastasios Kyrillidis , Oluwasanmi Koyejo Stanford University & University of Illinois at Urbana Champaign, Russell Poldrack Stanford UniversityPaper | Abstract
• Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods ,Huikang Liu CUHK, Weijie Wu CUHK, Anthony Man-Cho So Paper | Abstract
• Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks ,Devansh Arpit SUNY Buffalo, Yingbo Zhou SUNY Buffalo, Bhargava Kota SUNY Buffalo, Venu Govindaraju SUNY BuffaloPaper | Abstract
• Learning to Generate with Memory ,Chongxuan Li Tsinghua University, Jun Zhu Tsinghua, Bo Zhang Tsinghua UniversityPaper | Abstract
• Learning End-to-end Video Classification with Rank-Pooling ,Basura Fernando ANU Canberra Australia, Stephen Gould Australian National UniversityPaper | Abstract
• Learning to Filter with Predictive State Inference Machines ,Wen Sun Carnegie Mellon University, Arun Venkatraman Carnegie Mellon University, Byron Boots , J.Andrew Bagnell Carnegie Mellon UniversityPaper | Abstract
• A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling ,Mostafa Rahmani University of Central Florida, Geroge Atia University of Central FloridaPaper | Abstract
• DCM Bandits: Learning to Rank with Multiple Clicks ,Sumeet Katariya University of Wisconsin Madiso, Branislav Kveton , Csaba Szepesvari Alberta, Zheng Wen Adobe ResearchPaper | Abstract
• Train faster, generalize better: Stability of stochastic gradient descent ,Moritz Hardt Google, Ben Recht Berkeley, Yoram Paper | Abstract
• Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm ,Junpei Komiyama The University of Tokyo, Junya Honda The University of Tokyo, Hiroshi Nakagawa The University of TokyoPaper | Abstract
• Contextual Combinatorial Cascading Bandits ,Shuai Li CUHK, Baoxiang Wang , Shengyu Zhang , Wei Chen Paper | Abstract
• Conservative Bandits ,Roshan Shariff University of Alberta, Yifan Wu , Tor , Csaba Szepesvari AlbertaPaper | Abstract
• Variance-Reduced and Projection-Free Stochastic Optimization ,Elad Hazan Princeton University, Haipeng Luo Paper | Abstract
• Factored Temporal Sigmoid Belief Networks for Sequence Learning ,Jiaming Song Tsinghua University, Zhe Gan Duke University, Lawrence Carin Paper | Abstract
• False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking ,QianQian Xu IIE, Jiechao Xiong Peking University, Xiaochun Cao Institute of information engineering, Yuan Yao Peking UniversityPaper | Abstract
• Strongly-Typed Recurrent Neural Networks ,David Balduzzi , Muhammad Ghifary Victoria University Wellington, Weta DigitalNAPaper | Abstract
• Distributed Clustering of Linear Bandits in Peer to Peer Networks ,Nathan Korda University of Oxford, Balazs Szorenyi , Shuai Li University of InsubriaPaper | Abstract
• Collapsed Variational Inference for Sum-Product Networks ,Han Zhao Carnegie Mellon University, Tameem Adel University of Amsterdam, Geoff Gordon CMU, Brandon Amos Carnegie Mellon UniversityPaper | Abstract
• On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search ,Piyush Khandelwal University of Texas at Austin, Elad Liebman , Scott , Peter Stone U. of TexasPaper | Abstract
• Benchmarking Deep Reinforcement Learning for Continuous Control ,Yan Duan University of California, Xi Chen University of California, Rein Houthooft Ghent University, John Schulman University of California, Pieter Abbeel BerkeleyPaper | Abstract
• K
• -Means Clustering with Distributed Dimensions ,Hu Ding State University of New York at Buffalo, Lingxiao Huang , Yu Liu Tsinghua University, Jian Li Paper | Abstract
• Texture Networks: Feed-forward Synthesis of Textures and Stylized Images ,Dmity Ulyanov Skolkovo institute of science , Vadim Lebedev , Andrea , Victor Lempitsky Skolkovo Institute of Sci&TechPaper | Abstract
• Fast Constrained Submodular Maximization: Personalized Data Summarization ,Baharan Mirzasoleiman ETH Zurich, Ashwinkumar Badanidiyuru Google Research, Amin Karbasi Yale UniversityPaper | Abstract
• On the Statistical Limits of Convex Relaxations ,Zhaoran Wang Princeton University, Quanquan Gu , Han Paper | Abstract
• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing ,Ankit Kumar MetaMind, Ozan Irsoy MetaMind, Mohit Iyyer MetaMind, James Bradbury MetaMind, Ishaan Gulrajani MetaMind, Victor Zhong MetaMind, Romain Paulus MetaMind, Richard Socher Paper | Abstract
• Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions ,Igor Colin , Aurelien Bellet INRIA, Joseph Salmon , Stéphan Clémençon Paper | Abstract
• Solving Ridge Regression using Sketched Preconditioned SVRG ,Alon , Francesco Orabona Yahoo, Shai Shalev-Shwartz Hebrew University of JerusalemPaper | Abstract
• Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control ,Prashanth L.A. University of Maryland , Cheng Jie University of Maryland – College Park, Michael Fu University of Maryland – College Park, Steve Marcus University of Maryland – College Park, Csaba Szepesvari AlbertaPaper | Abstract
• Estimating Accuracy from Unlabeled Data: A Bayesian Approach ,Emmanouil Antonios Platanios Carnegie Mellon University, Avinava Dubey Carnegie Mellon University, Tom Mitchell Carnegie Mellon UniversityPaper | Abstract
• Non-negative Matrix Factorization under Heavy Noise ,Jagdeep Pani Indian Institute of Science, Ravindran Kannan Microsoft Reseach India, Chiranjib Bhattacharya , Navin Goyal Microsoft Research IndiaPaper | Abstract
• Extreme F-measure Maximization using Sparse Probability Estimates ,Kalina Jasinska , Krzysztof Dembczynski , Robert Busa-Fekete , Karlson Pfannschmidt , Timo Klerx , Eyke Hullermeier Paper | Abstract
• Auxiliary Deep Generative Models ,Lars Maaløe Technical University Denmark, Casper Kaae Sønderby University of Copenhagen, Søren Kaae Sønderby University of Copenagen, Ole Winther Technical University of DenmarkPaper | Abstract
• Importance Sampling Tree for Large-scale Empirical Expectation ,Olivier CANEVET Idiap Research Institut, Cijo Jose Idiap Research Institute/ Éc , Francois Fleuret Idiap research institutePaper | Abstract
• Starting Small – Learning with Adaptive Sample Sizes ,Hadi Daneshmand ETH Zurich, Aurelien Lucchi ETHZ, Thomas Hofmann Paper | Abstract
• Deep Gaussian Processes for Regression using Approximate Expectation Propagation ,Thang Bui University of Cambridge, Daniel Hernandez-Lobato , Yingzhen Li University of Cambridge, Jose miguel Hernandez-Lobato , Richard Turner University of CambridgePaper | Abstract
• DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression ,Jovana Mitrovic University of Oxford, Dino Sejdinovic University of Oxford, Yee-Whye Teh OxfordPaper | Abstract
• Predictive Entropy Search for Multi-objective Bayesian Optimization ,Daniel Hernandez-Lobato , Jose miguel Hernandez-Lobato , Amar Shah University of Cambridge, Ryan Adams HarvardPaper | Abstract
• Rich Component Analysis ,Rong Ge , James Zou Paper | Abstract
• Black-Box Alpha Divergence Minimization ,Jose miguel Hernandez-Lobato , Yingzhen Li University of Cambridge, Mark Rowland University of Cambridge, Thang Bui University of Cambridge, Daniel Hernandez-Lobato , Richard Turner University of CambridgePaper | Abstract
• One-Shot Generalization in Deep Generative Models ,Danilo Rezende Google DeepMind, Shakir , Daan Wierstra Google DeepMindPaper | Abstract
• Optimal Classification with Multivariate Losses ,Nagarajan Natarajan Microsoft Research India, Oluwasanmi Koyejo Stanford University & University of Illinois at Urbana Champaign, Pradeep Ravikumar UT Austin, Inderjit Paper | Abstract
• A ranking approach to global optimization ,Cedric Malherbe ENS Cachan, Emile Contal ENS Cachan, Nicolas Vayatis ENS CachanPaper | Abstract
• Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms ,Yu-Xiang Wang , Veeranjaneyulu Sadhanala , Wei Dai Carnegie Mellon University, Willie Neiswanger , Suvrit Sra , Eric Xing CMUPaper | Abstract
• Autoencoding beyond pixels using a learned similarity metric ,Anders B. L. Larsen Technical University of Denmar, Søren Kaae Sønderby University of Copenagen, Hugo Larochelle Twitter, Ole Winther Technical University of DenmarkPaper | Abstract
• Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling ,Christopher De Sa Stanford, Chris Re Stanford University, Kunle Olukotun StanfordPaper | Abstract
• Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling ,Atsushi Shibagaki Nagoya Institute of Technology, Masayuki Karasuyama Nagoya Institute of Technology, Kohei Hatano Kyushu University, Ichiro Takeuchi Paper | Abstract
• Anytime optimal algorithms in stochastic multi-armed bandits ,Rémy Degenne Université Paris Diderot, Vianney Perchet Paper | Abstract
• Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design ,William Hoiles UCLA, Mihaela van der Schaar Paper | Abstract
• Mixed membership modelling with hierarchical CRMs ,Gaurav Pandey Indian Institute of Science, Ambedkar Dukkipati Indian Institute of SciencePaper | Abstract
• From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification ,Andre Martins , Ramon Astudillo UnbabelPaper | Abstract
• Black-box optimization with a politician ,Sebastien Bubeck Microsoft, Yin Tat Lee MITPaper | Abstract
• Gaussian process nonparametric tensor estimator and its minimax optimality ,Heishiro Kanagawa Tokyo Institute of Technology, Taiji Suzuki , Hayato Kobayashi Yahoo Japan Corporation, Nobuyuki Shimizu , Yukihiro Tagami Yahoo Japan CorporationPaper | Abstract
• No-Regret Algorithms for Heavy-Tailed Linear Bandits ,Andres Munoz Medina , Scott Yang Paper | Abstract
• Extended and Unscented Kitchen Sinks ,Edwin Bonilla UNSW, Daniel Steinberg DATA61, Alistair Reid Data61Paper | Abstract
• Matrix Eigendecomposition via Doubly Stochastic Riemannian Optimization ,Zhiqiang Xu Institute of Infocomm Research, Peilin Zhao I2RPaper | Abstract
• Recommendations as Treatments: Debiasing Learning and Evaluation ,Tobias Schnabel Cornell University, Thorsten Joachims Cornell, Adith Swaminathan Cornell University, Ashudeep Singh Cornell University, Navin Chandak GooglePaper | Abstract
• ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission ,Jinsung Yoon University of California, Ahmed Alaa University of California, Scott Hu University of California, Mihaela van der Schaar Paper | Abstract
• An optimal algorithm for the Thresholding Bandit Problem ,Andrea LOCATELLI University of Potsdam, Maurilio Gutzeit Universität Potsdam, Alexandra Carpentier Paper | Abstract
• Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching ,Mu Niu University of Glasgow, Simon Rogers University of Glasgow, Maurizio Filippone EURECOM, Dirk Husmeier University of GlasgowPaper | Abstract
• The Deep Neural Matrix Gaussian Process ,Christos Louizos University of Amsterdam, Max Welling University of Amsterdam / CIFARPaper | Abstract
• Learning Granger Causality for Hawkes Processes ,Hongteng Xu Georgia Tech, Mehrdad Farajtabar , Hongyuan Paper | Abstract
• Neural Variational Inference for Text Processing ,Yishu Miao University of Oxford, Lei Yu University of Oxford, Phil Blunsom Paper | Abstract
• Dictionary Learning for Massive Matrix Factorization ,Arthur Mensch Inria, Julien Mairal , Bertrand Thirion Inria, Gael Varoquaux Paper | Abstract
• Pixel Recurrent Neural Networks ,Aaron Van den Oord Google Deepmind, Nal Kalchbrenner Google Deepmind, Koray Kavukcuoglu Google DeepmindPaper | Abstract
• Sequential decision making under uncertainty: Are most decisions easy? ,Ozgur Simsek , Simon Algorta , Amit Kothiyal Paper | Abstract
• Gaussian quadrature for matrix inverse forms with applications ,Chengtao Li MIT, Suvrit Sra , Stefanie Jegelka MITPaper | Abstract
• Train and Test Tightness of LP Relaxations in Structured Prediction ,Ofer Meshi , Mehrdad Mahdavi , Adrian Weller University of Cambridge, David Sontag NYUPaper | Abstract
• Stochastic Optimization for Multiview Learning using Partial Least Squares ,Raman Arora Johns Hopkins University, Poorya Mianjy Johns Hopkins University, Teodor Marinov Paper | Abstract
• Hierarchical Compound Poisson Factorization ,Mehmet Basbug Princeton University, Barbara Engelhardt Paper | Abstract
• Opponent Modeling in Deep Reinforcement Learning ,He He , Jordan , Hal Daume MarylandPaper | Abstract
• No penalty no tears: Least squares in high-dimensional linear models ,Xiangyu Wang Duke University, David , Chenlei Leng University of WarwickPaper | Abstract
• SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ,Zheng Qu University of Hong Kong, Peter Richtarik , Martin Takac , Olivier Fercoq LTCIPaper | Abstract
• On Graduated Optimization for Stochastic Non-Convex Problems ,Elad Hazan Princeton University, Kfir Yehuda Levy Technion, Shai Shalev-Shwartz Hebrew University of JerusalemPaper | Abstract
• Meta-Learning with Memory-Augmented Neural Networks ,Adam Santoro Google DeepMind, Sergey Bartunov Higher School of Economics, Matthew Botvinick , Daan Wierstra Google DeepMind, Timothy Lillicrap Google DeepMindPaper | Abstract
• The knockoff filter for FDR control in group-sparse and multitask regression ,Ran Dai The University of Chicago, Rina Barber The University of ChicagoPaper | Abstract
• Softened Approximate Policy Iteration for Markov Games ,Julien Pérolat Univ. Lille, Bilal Piot Univ. Lille, Matthieu Geist , Bruno Scherrer , Olivier Pietquin Univ. LillePaper | Abstract
• Stochastic Block BFGS: Squeezing More Curvature out of Data ,Robert Gower University of Edinburgh, Donald Goldfarb Columbia University, Peter Richtarik Paper | Abstract
• Class Probability Estimation via Differential Geometric Regularization ,Qinxun Bai Boston University, Steven Rosenberg Boston University, Zheng Wu The Mathwork Inc., Stan Sclaroff Boston UniversityPaper | Abstract
• Exploiting Cyclic Symmetry in Convolutional Neural Networks ,Sander Dieleman Google DeepMind, Jeffrey De Fauw Google DeepMind, Koray Kavukcuoglu Google DeepmindPaper | Abstract
• Graying the black box: Understanding DQNs ,Tom Zahavy Technion, Nir Ben-Zrihem , Shie Mannor TechnionPaper | Abstract
• The Sum-Product Theorem: A Foundation for Learning Tractable Models ,Abram Friesen University of Washington, Pedro Paper | Abstract
• Pareto Frontier Learning with Expensive Correlated Objectives ,Amar Shah University of Cambridge, Zoubin Paper | Abstract
• Asynchronous Methods for Deep Reinforcement Learning ,Volodymyr Mnih Google DeepMind, Adria Puigdomenech Badia Google DeepMind, Mehdi Mirza , Alex Graves Google DeepMind, Timothy Lillicrap Google DeepMind, Tim Harley Google DeepMind, David , Koray Kavukcuoglu Google DeepmindPaper | Abstract
• A Simple and Strongly-Local Flow-Based Method for Cut Improvement ,Luke Veldt Purdue Unversity, David Gleich Purdue University, Michael Paper | Abstract
• Nonlinear Statistical Learning with Truncated Gaussian Graphical Models ,Qinliang Su Duke University, xuejun Liao , changyou Chen , Lawrence Carin Paper | Abstract
• Barron and Covers’ Theory in Supervised Learning　and Its Application to Lasso ,Masanori Kawakita Kyushu University, Jun’ichi Takeuchi Kyushu UniversityPaper | Abstract
• Nonparametric canonical correlation analysis ,Tomer Michaeli Technion, Weiran Wang , Karen Livescu TTI ChicagoPaper | Abstract
• BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ,Alexander , Karthik Sridharan YahooPaper | Abstract
• Associative Long Short-Term Memory ,Ivo Danihelka Google DeepMind, Greg Wayne Google DeepMind, Benigno Uria Google DeepMind, Nal Kalchbrenner Google Deepmind, Alex Graves Google DeepMindPaper | Abstract
• Dueling Network Architectures for Deep Reinforcement Learning ,Ziyu Wang Google Inc., Nando de Freitas University of Oxford, Tom Schaul Google Inc., Matteo Hessel Google Deepmind, Hado van Hasselt Google DeepMind, Marc Lanctot Google DeepmindPaper | Abstract
• Persistence weighted Gaussian kernel for topological data analysis ,Genki Kusano Tohoku University, Yasuaki Hiraoka , Kenji Paper | Abstract
• Learning Convolutional Neural Networks for Graphs ,Mathias Niepert NEC Laboratories Europe, Mohamed Ahmed NEC Laboratories Europe, Konstantin Kutzkov NEC Laboratories EuropePaper | Abstract
• Persistent RNNs: Stashing Recurrent Weights On-Chip ,Greg Diamos Baidu USA, Shubho Sengupta Baidu USA, Bryan Catanzaro Baidu USA, Mike Chrzanowski Baidu USA, Adam Coates , Erich Elsen Baidu USA, Jesse Engel Baidu USA, Awni Hannun Baidu USA, Sanjeev Satheesh Baidu USAPaper | Abstract
• Recurrent Orthogonal Networks and Long-Memory Tasks ,Mikael Henaff NYU, Arthur , Yann Paper | Abstract
• The Arrow of Time in Multivariate Time Series ,Stefan Bauer ETH Zurich, Bernhard Schölkopf , Jonas Paper | Abstract
• Mixture Proportion Estimation via Kernel Embeddings of Distributions ,Harish Ramaswamy , Clayton , Ambuj Paper | Abstract
• Fast DPP Sampling for Nystrom with Application to Kernel Methods ,Chengtao Li MIT, Stefanie Jegelka MIT, Suvrit Sra Paper | Abstract
• Complex Embeddings for Simple Link Prediction ,Théo Trouillon Xerox, Johannes Welbl , Guillaume Bouchard , Sebastian Riedel , Eric Gaussier Paper | Abstract
• Interactive Bayesian Hierarchical Clustering ,Sharad Vikram UCSD, Sanjoy Dasgupta UCSDPaper | Abstract
• A Convolutional Attention Network for Extreme Summarization of Source Code ,Miltiadis Allamanis University of Edinburgh, Hao Peng Peking University, Charles Paper | Abstract
• How to Fake Multiply by a Gaussian Matrix ,Vamsi Potluru Comcast Cable, Michael Kapralov , David Woodruff Paper | Abstract
• Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing ,Ryan Rogers University of Pennsylvania, Salil Vadhan Harvard University, Hyun Lim UCLA, Marco Gaboardi University at BuffaloPaper | Abstract
• Pliable Rejection Sampling ,Akram Erraqabi Inria Lille Nord Europe, Michal Valko Inria Lille – Nord Europe, Alexandra Carpentier , Odalric Maillard InriaPaper | Abstract
• Differentially Private Policy Evaluation ,Borja Balle Lancaster University, Maziar Gomrokchi McGill University, Doina Precup McGillPaper | Abstract
• Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning ,Philip Thomas CMU, Emma Paper | Abstract
• Discrete Deep Feature Extraction: A Theory and New Architectures ,Thomas Wiatowski ETH Zurich, Michael Tschannen ETH Zurich, Aleksandar Stanic ETH Zurich, Philipp Grohs University of Vienna, Helmut Boelcskei ETH ZurichPaper | Abstract
• Efficient Algorithms for Adversarial Contextual Learning ,Vasilis Syrgkanis Microsoft Research, Akshay Krishnamurthy Microsoft Research, Robert Schapire Microsoft ResearchPaper | Abstract
• Training Deep Neural Networks via Direct Loss Minimization ,Yang Song Tsinghua University, Alexander Schwing , Richard , Raquel Urtasun U. of TorontoPaper | Abstract
• Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification ,Kyuyeon Hwang Seoul National University, Wonyong Sung Seoul National UniversityPaper | Abstract
• Variational inference for Monte Carlo objectives ,Andriy Mnih , Danilo Rezende Google DeepMindPaper | Abstract
• Hierarchical Decision Making In Electricity Grid Management ,Gal Dalal Technion, Elad Gilboa Technion, Shie Mannor TechnionPaper | Abstract
• Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization ,Eric Balkanski , Baharan Mirzasoleiman ETH Zurich, Andreas Krause , Yaron Singer Paper | Abstract
• Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units ,Wenling Shang , Kihyuk Sohn NEC Laboratories America, Diogo Almeida Enlitic, Honglak Lee University of MichiganPaper | Abstract
• Isotonic Hawkes Processes ,Yichen Wang Georgia Tech, Bo Xie , Bo Dai Georgia Tech, Nan Du , Le Song GatechPaper | Abstract
• Cross-graph Learning of Multi-relational Associations ,Hanxiao Liu , Yiming Yang Paper | Abstract
• Markov-modulated marked Poisson processes for check-in data ,Jiangwei Pan Duke University, Vinayak Rao Purdue University, Pankaj Agarwal Duke Univeristy, Alan Gelfand Duke UniversityPaper | Abstract
• Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference ,Tudor Achim Stanford University, Ashish Sabharwal Allen Institute for AI, Stefano Ermon Paper | Abstract
• On the Power of Distance-Based Learning ,Periklis Papakonstantinou Rutgers University , Jia Xu Chinese Academy of Sciences, Guang Yang ICTPaper | Abstract
• A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery ,Xin Lin University of Texas at Austin, Ian En-Hsu Yen University of Texas at Austin, Jiong Zhang University of Texas at Austin, Pradeep Ravikumar UT Austin, Inderjit Paper | Abstract
• Generalized Direct Change Estimation in Ising Model Structure ,Farideh Fazayeli University of Minnesota, Arindam Banerjee Paper | Abstract
• Robust Principal Component Analysis with Side Information ,Kai-Yang Chiang UT Austin, Cho-Jui Hsieh UC Davis, Inderjit Paper | Abstract
• Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation ,Huan Gui University of Illinois at Urba, Jiawei Han university of illinois at urbana-champaign, Quanquan Gu Paper | Abstract
• Early and Reliable Event Detection Using Proximity Space Representation ,Maxime Sangnier LTCI, Jerome Gauthier CEA, Alain Rakotomamonjy Paper | Abstract
• Stratified Sampling Meets Machine Learning ,Edo Liberty , Kevin Lang Yahoo Labs, Konstantin Shmakov Yahoo LabsPaper | Abstract
• Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model ,Xinze Guan Oregon State University, Raviv Raich Oregon State University, Weng-keen Paper | Abstract
• Generalization Properties and Implicit Regularization for Multiple Passes SGM ,Junhong Lin Istituto Italiano di Tecnologi, Raffaello Camoriano IIT (Italy) and UNIGE (Italy), Lorenzo Rosasco Istituto Italiano di TecnologiaPaper | Abstract
• Principal Component Projection Without Principal Component Analysis ,Roy Frostig Stanford University, Cameron Musco Massachusetts Institute of Technology, Christopher Musco Mass. Institute of Technology, Aaron Sidford Microsoft ResearchPaper | Abstract
• Recovery guarantee of weighted low-rank approximation via alternating minimization ,Yuanzhi Li Princeton University, Yingyu Liang Princeton, Andrej Risteski Princeton UniversityPaper | Abstract
• Deconstructing the Ladder Network Architecture ,Mohammad Pezeshki Universite de Montreal, Linxi Fan , Philemon Brakel , Aaron Courville , Yoshua Bengio U. of MontrealPaper | Abstract
• Generalization and Exploration via Randomized Value Functions ,Ian Osband Stanford, Ben , Zheng Wen Adobe ResearchPaper | Abstract
• Evasion and Hardening of Tree Ensemble Classifiers ,Alex Kantchelian University of California, J. D. Tygar UC Berkeley, Anthony Joseph UC BerkeleyPaper | Abstract
• Dynamic Memory Networks for Visual and Textual Question Answering ,Caiming Xiong MetaMind, Stephen Merity MetaMind, Richard Socher Paper | Abstract
• Estimating Cosmological Parameters from the Dark-Matter Distribution ,Siamak Ravanbakhsh CMU, Sebastian Fromenteau Carnegie Mellon Unitversity, Junier Oliva CMU, Layne Price Carnegie Mellon Unitversity, Shirley Ho Carnegie Mellon Unitversity, Barnabas Poczos , Jeff Schneider Carnegie Mellon UnitversityPaper | Abstract
• Learning population-level diffusions with generative RNNs ,Tatsunori Hashimoto MIT, David Gifford MIT, Tommi Jaakkola MITPaper | Abstract
• Expressiveness of Rectifier Neural Network ,Xingyuan Pan University of Utah, Vivek Srikumar University of UtahPaper | Abstract
• Discrete Distribution Estimation under Local Privacy ,Peter Kairouz UIUC, Keith Bonawitz Google, Daniel Ramage Paper | Abstract
• Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families which Allow Positive Dependencies ,David Inouye University of Texas at Austin, Pradeep Ravikumar UT Austin, Inderjit Paper | Abstract
• A Box-Constrained Approach for Hard Permutation Problems ,Cong Han Lim Univ of Wisconsin – Madison, Steve Wright University of Wisconsin-MadisonPaper | Abstract
• Geometric Mean Metric Learning ,Pourya Zadeh Tehran university, Reshad Hosseini , Suvrit Sra Paper | Abstract
• Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference ,Zhuoran Yang Princeton University, Zhaoran Wang Princeton University, Han , Yonina Eldar Technion, Tong Zhang Paper | Abstract
• Conditional Bernoulli Mixtures for Multi-label Classification ,Cheng Li Northeastern University, Bingyu Wang Northeastern University, Virgil Pavlu Northeastern University, Javed Aslam Northeastern UniversityPaper | Abstract
• Scalable Discrete Sampling as a Multi-Armed Bandit Problem ,Yutian Chen University of Cambridge, Zoubin Paper | Abstract
• Recycling Randomness with Structure for Sublinear time Kernel Expansions ,Krzysztof Choromanski Google Research NYC, Vikas Sindhwani Google ResearchPaper | Abstract
• Bidirectional Helmholtz Machines ,Jorg Bornschein University of Montreal, Samira Shabanian University of Montreal, Asja Fischer , Yoshua Bengio U. of MontrealPaper | Abstract
• Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier ,Jacob Abernethy U. of Michigan, Elad Hazan Princeton UniversityPaper | Abstract
• Preconditioning Kernel Matrices ,Kurt Cutajar EURECOM, Michael Osborne , John Cunningham Columbia University, Maurizio Filippone EURECOMPaper | Abstract
• Greedy Column Subset Selection: New Bounds and Distributed Algorithms ,Jason Altschuler Princeton University, Aditya Bhaskara , Gang Fu Google Research, Vahab Mirrokni Google Research, Afshin Rostamizadeh Google, Morteza Zadimoghaddam Google ResearchPaper | Abstract
• Dynamic Capacity Networks ,Amjad Almahairi , Nicolas Ballas , Tim Cooijmans University of Montreal, Yin Zheng Hulu LLC., Hugo Larochelle Twitter, Aaron Courville Paper | Abstract
• Pricing a low-regret seller ,Hoda Heidari , Mohammad Mahdian Google, Umar Syed , Sergei Vassilvitskii , Sadra Yazdanbod Georgia Institute of TechnologyPaper | Abstract
• Estimation from Indirect Supervision with Linear Moments ,Aditi Raghunathan IIT Madras, Roy Frostig Stanford University, John , Percy Liang StanfordPaper | Abstract
• Speeding up k-means by approximating Euclidean distances via block vectors ,Thomas Bottesch Ulm University, Thomas Bühler Avira, Markus Kächele Ulm UniversityPaper | Abstract
• Learning and Inference via Maximum Inner Product Search ,Stephen Mussmann Stanford University, Stefano Ermon Paper | Abstract
• A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums ,Anton Rodomanov Higher School of Economics, Dmitry Kropotov Moscow State UniversityPaper | Abstract
• A Kernel Test of Goodness of Fit ,Kacper.chwialkowski@ Chwialkowski Ucl, Heiko Strathmann University College London, Arthur Gretton UCL GatsbyPaper | Abstract
• Interacting Particle Markov Chain Monte Carlo ,Tom Rainforth University of Oxford, Christian Naesseth Linköping University, Brooks Paige University of Oxford, Frank Wood , Jan-Willem Vandemeent , Fredrik Lindsten Uppsala UniversityPaper | Abstract
• Faster Eigenvector Computation via Shift-and-Invert Preconditioning ,Dan Garber TTI Chicago, Elad Hazan Princeton University, Chi Jin UC Berkeley, Sham , Cameron Musco Massachusetts Institute of Technology, Praneeth Netrapalli Microsoft Research, Aaron Sidford Microsoft ResearchPaper | Abstract
• A Theory of Generative ConvNet ,Jianwen Xie UCLA, Yang Lu UCLA, Song-Chun Zhu UCLA, Yingnian Wu UCLAPaper | Abstract
• Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution ,Quanming Yao HKUST, James Kwok Hong Kong University Science TechnologyPaper | Abstract
• Computationally Efficient Nystr\”{o}m Approximation using Fast Transforms ,Si Si , Cho-Jui Hsieh UC Davis, Inderjit Paper | Abstract
• Gromov-Wasserstein Barycenters of Similarity Matrices ,Gabriel , Justin Solomon , Marco Cuturi KyotoPaper | Abstract
• Robust Monte Carlo Sampling using Riemannian Nos\'{e}-Poincar\'{e} Hamiltonian Dynamics ,Anirban Roychowdhury , Brian Kulis Boston University, Srinivasan Parthasarathy Paper | Abstract
• The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM ,Ardavan Saeedi MIT, Matthew Hoffman Adobe Research, Ryan Adams Harvard, Matthew Johnson Paper | Abstract
• Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning ,Yury Ustinovskiy Yandex, Valentina Fedorova Yandex, Gleb Gusev Yandex, Pavel Serdyukov YandexPaper | Abstract
• Discriminative Embeddings of Latent Variable Models for Structured Data ,Hanjun Dai Georgia Tech, Bo Dai Georgia Tech, Le Song GatechPaper | Abstract
• Robust Random Cut Forest Based Anomaly Detection on Streams ,Sudipto Guha , Nina Mishra , Gourav Roy , Okke Schrijvers Paper | Abstract
• Training Neural Networks Without Gradients: A Scalable ADMM Approach ,Tom Goldstein University of Maryland, Gavin Taylor US Naval Academy, Ankit Patel Rice University, Ryan Burmeister US Naval Academy, Zheng Xu University of Maryland, Bharat Singh University of MarylandPaper | Abstract
• Topographical Features of High-Dimensional Categorical Data and Their Applications to Clustering ,Chao Chen CUNY, Novi Quadrianto University of SussexPaper | Abstract
• Efficient Algorithms for Large-scale Generalized Eigenvector Computation and CCA ,Rong Ge , Chi Jin UC Berkeley, Sham , Praneeth Netrapalli Microsoft Research, Aaron Sidford Microsoft ResearchPaper | Abstract
• Algorithms for Optimizing the Ratio of Submodular Functions ,Wenruo Bai University of Washington, Rishabh Iyer , Kai Wei , Jeff Bilmes U. of WashingtonPaper | Abstract
• Model-Free Imitation Learning with Policy Optimization ,Jonathan Ho Stanford, Jayesh Gupta Stanford University, Stefano Ermon Paper | Abstract
• ADIOS: Architectures Deep In Output Space ,Moustapha Cisse , Maruan Al-Shedivat CMU, Samy Bengio GooglePaper | Abstract
• Causal Strength via Shannon Capacity: Axioms, Estimators and Applications ,Weihao Gao UIUC, Sreeram Kannan UW Seattle, Sewoong Oh UIUC, Pramod Viswanath UIUCPaper | Abstract
• Memory-based Control of Active Perception and Action in Minecraft ,Junhyuk Oh University of Michigan, Valliappa Chockalingam University of Michigan, Satinder , Honglak Lee University of MichiganPaper | Abstract
• The Label Complexity of Mixed-Initiative Classifier Training ,Jina Suh Microsoft, Xiaojin Zhu University of Wisconsin, Saleema Amershi MicrosoftPaper | Abstract
• Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations ,Aaron Schein , Mingyuan Zhou , Blei David Columbia, Hanna Wallach MicrosoftPaper | Abstract
• Tensor Decomposition via Joint Matrix Schur Decomposition ,Nikos Vlassis Adobe, Nicolo Colombo Univ of LuxembourgPaper | Abstract
• Improving the Efficiency of Deep Reinforcement Learning with Normalized Advantage Functions and Synthetic Experience ,Shixiang Gu University of Cambridge, Sergey Levine Google, Timothy Lillicrap Google DeepMind, Ilya Sutskever OpenAIPaper | Abstract
• Domain Adaptation with Conditional Transferable Components ,Mingming Gong University of Technology Sydne, Kun Zhang Carnegie Mellon University, Tongliang Liu MPI Tuebingen, Dacheng Tao , Bernhard Schölkopf Paper | Abstract
• Fixed Point Quantization of Deep Convolutional Networks ,Darryl Lin Qualcomm Research, Sachin Talathi Qualcomm Research, Sreekanth Annapureddy NetraDyne Inc.Paper | Abstract
• Provable Algorithms for Inference in Topic Models ,Sanjeev Arora Princeton University, Rong Ge , Frederic Koehler Princeton University, Tengyu Ma Princeton University, Ankur Moitra Paper | Abstract
• Epigraph projections for fast general convex programming ,Po-Wei Wang Carnegie Mellon University, Matt Wytock , Zico Paper | Abstract
• Fast Algorithms for Segmented Regression ,Ludwig Schmidt , Jerry Li MIT, Jayadev Acharya MIT, Ilias Diakonikolas Paper | Abstract
• Energetic Natural Gradient Descent ,Philip Thomas CMU, Christoph Dann Carnegie Mellon University, Bruno Castro da Silva , Emma Paper | Abstract
• Partition Functions from Rao-Blackwellized Tempered Sampling ,Patrick Stinson Columbia University, David Carlson Columbia University, Ari Pakman Columbia University, Liam Paper | Abstract
• Learning Mixtures of Plackett-Luce Models ,Zhibing Zhao RPI, Peter Piech RPI, Lirong Xia RPIPaper | Abstract
• Near Optimal Behavior via Approximate State Abstraction ,David Abel Brown University, David Hershkowitz Brown University, Michael Littman Paper | Abstract
• Power of Ordered Hypothesis Testing ,Lihua Lei Lihua, William Fithian UC BerkeleyPaper | Abstract
• PHOG: Probabilistic Model for Code ,Pavol Bielik ETH Zurich, Veselin Raychev ETH Zurich, Martin Vechev ETH ZurichPaper | Abstract
• Shifting Regret, Mirror Descent, and Matrices ,Andras Gyorgy , Csaba Szepesvari AlbertaPaper | Abstract
• Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters ,Jelena Luketina Aalto University, Tapani Raiko , Mathias Berglund Aalto UniversityPaper | Abstract
• Model-Free Trajectory Optimization\\ for Reinforcement Learning of Motor Skills ,Riad Akrour TU Darmstadt, Gerhard Neumann Paper | Abstract
• Controlling the distance to a Kemeny consensus without computing it ,Anna Korba Telecom Paris Tech, Yunlong Jiao Mines Paris Tech, Eric Sibony Telecom Paris TechPaper | Abstract
• Horizontally Scalable Submodular Maximization ,Mario Lucic ETH Zurich, Olivier Bachem ETH Zurich, Morteza Zadimoghaddam Google Research, Andreas Krause Paper | Abstract
• Group Equivariant Convolutional Networks ,Taco Cohen University of Amsterdam, Max Welling University of Amsterdam / CIFARPaper | Abstract
• Stochastic Discrete Clenshaw-Curtis Quadrature ,Nico Piatkowski TU Dortmund, Katharina Paper | Abstract
• Correcting Forecasts with Multi-force Neural Attention ,Matthew Riemer IBM, Aditya Vempaty IBM, Flavio Calmon IBM, Fenno Heath IBM, Richard Hull IBM, Elham Khabiri IBMPaper | Abstract
• Learning Representations for Counterfactual Inference ,Uri Shalit , David Sontag NYU, Fredrik Johansson Chalmers Technical UniversityPaper | Abstract
• The Automatic Statistician: A Relational Perspective ,Yunseong Hwang UNIST, Anh Tong UNIST, Jaesik Choi UNISTPaper | Abstract
• Inference Networks for Sequential Monte Carlo in Graphical Models ,Brooks Paige University of Oxford, Frank Wood Paper | Abstract
• Slice Sampling on Hamiltonian Trajectories ,Benjamin Bloem-Reddy Columbia University, John Cunningham Columbia UniversityPaper | Abstract
• Noisy Activation Functions ,Caglar Gülçehre , Marcin Moczulski , Misha Denil , Yoshua Bengio U. of MontrealPaper | Abstract
• A Primal and Dual Sparse Approach to Extreme Classification ,Ian En-Hsu Yen University of Texas at Austin, Xiangru Huang UTaustin, Pradeep Ravikumar UT Austin, Kai Zhong ICES department, Inderjit Paper | Abstract

 
 
 
 

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