The NIPS 2016 pre-proceedings are out:

The papers below appear in Advances In Neural Information Processing Systems 29 edited by D.D. Lee and U.V. Luxburg and I. Guyon and R. Garnett.

They are proceedings from the conference, "Neural Information Processing Systems 2016."

credit photo: NASA/JPL/University of Arizona

They are proceedings from the conference, "Neural Information Processing Systems 2016."

- Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan D. He, Christopher M. De Sa, Ioannis Mitliagkas, Christopher Ré
- Deep ADMM-Net for Compressive Sensing MRI yan yang, Jian Sun, Huibin Li, Zongben Xu
- A scaled Bregman theorem with applications Richard Nock, Aditya Menon, Cheng Soon Ong
- Swapout: Learning an ensemble of deep architectures Saurabh Singh, Derek Hoiem, David Forsyth
- On Regularizing Rademacher Observation Losses Richard Nock
- Without-Replacement Sampling for Stochastic Gradient Methods Ohad Shamir
- Fast and Provably Good Seedings for k-Means Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause
- Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn, Ian Goodfellow, Sergey Levine
- Matrix Completion and Clustering in Self-Expressive Models Ehsan Elhamifar
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Chengkai Zhang, Jiajun Wu, Tianfan Xue, Bill Freeman, Josh Tenenbaum
- Probabilistic Modeling of Future Frames from a Single Image Tianfan Xue, Jiajun Wu, Katherine Bouman, Bill Freeman
- Human Decision-Making under Limited Time Pedro A. Ortega, Alan A. Stocker
- Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Shizhong Han, Zibo Meng, AHMED-SHEHAB KHAN, Yan Tong
- Natural-Parameter Networks: A Class of Probabilistic Neural Networks Hao Wang, Xingjian SHI, Dit-Yan Yeung
- Tree-Structured Reinforcement Learning for Sequential Object Localization Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan
- Unsupervised Domain Adaptation with Residual Transfer Networks Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
- Verification Based Solution for Structured MAB Problems Zohar S. Karnin
- Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen
- Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
- SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille
- Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton
- Sorting out typicality with the inverse moment matrix SOS polynomial Edouard Pauwels, Jean B. Lasserre
- Multi-armed Bandits: Competing with Optimal Sequences Zohar S. Karnin, Oren Anava
- Multivariate tests of association based on univariate tests Ruth Heller, Yair Heller
- Learning What and Where to Draw Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
- The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Damek Davis, Brent Edmunds, Madeleine Udell
- Integrator Nets Hakan Bilen, Andrea Vedaldi
- Combining Low-Density Separators with CNNs Yu-Xiong Wang, Martial Hebert
- CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu
- Cooperative Graphical Models Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka
- Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan-Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank Wood
- Hierarchical Question-Image Co-Attention for Visual Question Answering Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh
- Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail, Christos Boutsidis
- FPNN: Field Probing Neural Networks for 3D Data Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, Leonidas J. Guibas
- CRF-CNN: Modeling Structured Information in Human Pose Estimation Xiao Chu, Wanli Ouyang, hongsheng Li, Xiaogang Wang
- Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H. Morgenstern, Aaron Roth
- Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy
- Domain Separation Networks Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan
- DISCO Nets : DISsimilarity COefficients Networks Diane Bouchacourt, Pawan K. Mudigonda, Sebastian Nowozin
- Multimodal Residual Learning for Visual QA Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
- CMA-ES with Optimal Covariance Update and Storage Complexity Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
- R-FCN: Object Detection via Region-based Fully Convolutional Networks jifeng dai, Yi Li, Kaiming He, Jian Sun
- GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
- Learning and Forecasting Opinion Dynamics in Social Networks Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
- Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares Rong Zhu
- Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks Hao Wang, Xingjian SHI, Dit-Yan Yeung
- Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula jean barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová
- A Unified Approach for Learning the Parameters of Sum-Product Networks Han Zhao, Pascal Poupart, Geoffrey J. Gordon
- Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images Junhua Mao, Jiajing Xu, Kevin Jing, Alan L. Yuille
- Stochastic Online AUC Maximization Yiming Ying, Longyin Wen, Siwei Lyu
- The Generalized Reparameterization Gradient Francisco R. Ruiz, Michalis Titsias RC AUEB, David Blei
- Coupled Generative Adversarial Networks Ming-Yu Liu, Oncel Tuzel
- Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei
- Variational Information Maximization for Feature Selection Shuyang Gao, Greg Ver Steeg, Aram Galstyan
- Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei
- Fast learning rates with heavy-tailed losses Vu C. Dinh, Lam S. Ho, Binh Nguyen, Duy Nguyen
- Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii, Hisashi Kashima
- Learning feed-forward one-shot learners Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi
- Learning User Perceived Clusters with Feature-Level Supervision Ting-Yu Cheng, Guiguan Lin, xinyang gong, Kang-Jun Liu, Shan-Hung Wu
- Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang
- Residual Networks are Exponential Ensembles of Relatively Shallow Networks Andreas Veit, Michael J. Wilber, Serge Belongie
- Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart
- Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang, Georgios Giannakis
- Coin Betting and Parameter-Free Online Learning Francesco Orabona, David Pal
- Deep Learning without Poor Local Minima Kenji Kawaguchi
- Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko
- A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei
- Generating Videos with Scene Dynamics Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
- Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah Goodman
- A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He, Yan Wang, John Hopcroft
- Optimizing affinity-based binary hashing using auxiliary coordinates Ramin Raziperchikolaei, Miguel A. Carreira-Perpinan
- Double Thompson Sampling for Dueling Bandits Huasen Wu, Xin Liu
- Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy, Thomas Brox
- Dynamic Filter Networks Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc V. Gool
- A Simple Practical Accelerated Method for Finite Sums Aaron Defazio
- Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian
- On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability Guillaume Papa, Aurélien Bellet, Stephan Clémençon
- Optimal spectral transportation with application to music transcription Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
- Regularized Nonlinear Acceleration Damien Scieur, Alexandre d'Aspremont, Francis Bach
- SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros
- Single-Image Depth Perception in the Wild Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
- Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan, Sewoong Oh
- Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky, Kwabena A. Boahen
- An ensemble diversity approach to supervised binary hashing Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei
- Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang, Jialei Wang, Dan Garber, Dan Garber, Nati Srebro
- The Power of Adaptivity in Identifying Statistical Alternatives Kevin G. Jamieson, Daniel Haas, Benjamin Recht
- On Explore-Then-Commit strategies Aurelien Garivier, Tor Lattimore, Emilie Kaufmann
- Sublinear Time Orthogonal Tensor Decomposition Zhao Song, David Woodruff, Huan Zhang
- DECOrrelated feature space partitioning for distributed sparse regression Xiangyu Wang, David B. Dunson, Chenlei Leng
- Deep Alternative Neural Networks: Exploring Contexts as Early as Possible for Action Recognition Jinzhuo Wang, Wenmin Wang, xiongtao Chen, Ronggang Wang, Wen Gao
- Machine Translation Through Learning From a Communication Game Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tieyan Liu, Wei-Ying Ma
- Dialog-based Language Learning Jason E. Weston
- Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Theodore Bluche
- Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
- Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner
- Proximal Deep Structured Models Shenlong Wang, Sanja Fidler, Raquel Urtasun
- Faster Projection-free Convex Optimization over the Spectrahedron Dan Garber, Dan Garber
- Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Remi Lam, Karen Willcox, David H. Wolpert
- Learning Sound Representations from Unlabeled Video Yusuf Aytar, Carl Vondrick, Antonio Torralba
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma
- Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
- Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis Yoshinobu Kawahara
- Distributed Flexible Nonlinear Tensor Factorization Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani
- The Robustness of Estimator Composition Pingfan Tang, Jeff M. Phillips
- Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats Bipin Rajendran, Pulkit Tandon, Yash H. Malviya
- PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli
- Differential Privacy without Sensitivity Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa
- Optimal Cluster Recovery in the Labeled Stochastic Block Model Se-Young Yun, Alexandre Proutiere
- Even Faster SVD Decomposition Yet Without Agonizing Pain Zeyuan Allen-Zhu, Yuanzhi Li
- An algorithm for L1 nearest neighbor search via monotonic embedding Xinan Wang, Sanjoy Dasgupta
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos
- Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes Dan Garber, Dan Garber, Ofer Meshi
- Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S. Du, Barnabas Poczos
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal, Zoubin Ghahramani
- Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray
- Direct Feedback Alignment Provides Learning in Deep Neural Networks Arild Nøkland
- Safe and Efficient Off-Policy Reinforcement Learning Remi Munos, Tom Stepleton, Anna Harutyunyan, Marc Bellemare
- A Multi-Batch L-BFGS Method for Machine Learning Albert S. Berahas, Jorge Nocedal, Martin Takac
- Semiparametric Differential Graph Models Pan Xu, Quanquan Gu
- Rényi Divergence Variational Inference Yingzhen Li, Richard E. Turner
- Doubly Convolutional Neural Networks Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang
- Density Estimation via Discrepancy Based Adaptive Sequential Partition Dangna Li, Kun Yang, Wing Hung Wong
- How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt, Jonah G. Cader, Thomas Serre
- Variational Information Maximizing Exploration Rein Houthooft, Xi Chen, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
- Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Timothy Rubin, Oluwasanmi O. Koyejo, Michael N. Jones, Tal Yarkoni
- Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue, zhiyuan li, Stefano Ermon, Carla P. Gomes, Bart Selman
- Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata, Makoto Yamada
- Fast Stochastic Methods for Nonsmooth Nonconvex Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
- Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabas Poczos, Alexander J. Smola, Eric P. Xing
- Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
- Dense Associative Memory for Pattern Recognition Dmitry Krotov, John J. Hopfield
- Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore, Tor Lattimore, Mark D. Reid
- Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore
- Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
- Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than
`O(1/\epsilon)`Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang - Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functionals Estimators Shashank Singh, Barnabas Poczos
- A state-space model of cross-region dynamic connectivity in MEG/EEG Ying Yang, Elissa Aminoff, Michael Tarr, Kass E. Robert
- What Makes Objects Similar: A Unified Multi-Metric Learning Approach Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou
- Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Nguyen Cuong, Huan Xu
- Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal
- Local Similarity-Aware Deep Feature Embedding Chen Huang, Chen Change Loy, Xiaoou Tang
- A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tieyan Liu
- Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
- Sampling for Bayesian Program Learning Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
- Learning Kernels with Random Features Aman Sinha, John C. Duchi
- Optimal Tagging with Markov Chain Optimization Nir Rosenfeld, Amir Globerson
- Crowdsourced Clustering: Querying Edges vs Triangles Ramya Korlakai Vinayak, Babak Hassibi
- Mixed vine copulas as joint models of spike counts and local field potentials Arno Onken, Stefano Panzeri
- Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation Emmanuel Abbe, Colin Sandon
- Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
- Nested Mini-Batch K-Means James Newling, François Fleuret
- Deep Learning Models of the Retinal Response to Natural Scenes Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus
- Preference Completion from Partial Rankings Suriya Gunasekar, Oluwasanmi O. Koyejo, Joydeep Ghosh
- Dynamic Network Surgery for Efficient DNNs Yiwen Guo, Anbang Yao, Yurong Chen
- Learning a Metric Embedding for Face Recognition using the Multibatch Method Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
- A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf
- End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal
- Stochastic Variance Reduction Methods for Saddle-Point Problems Balamurugan Palaniappan, Francis Bach
- Flexible Models for Microclustering with Applications to Entity Resolution Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna Wallach, Abbas Zaidi, Beka Steorts
- Catching heuristics are optimal control policies Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan R. Peters
- Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny, Robert B. Gramacy, Stefan Wild, Sebastien Le Digabel
- Adaptive Neural Compilation Rudy R. Bunel, Alban Desmaison, Pawan K. Mudigonda, Pushmeet Kohli, Philip Torr
- Synthesis of MCMC and Belief Propagation Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
- Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
- Unifying Count-Based Exploration and Intrinsic Motivation Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos
- Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad Saberian, Jose Costa Pereira, Nuno Nvasconcelos
- Stochastic Structured Prediction under Bandit Feedback Artem Sokolov, Julia Kreutzer, Stefan Riezler
- Simple and Efficient Weighted Minwise Hashing Anshumali Shrivastava
- Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher
- Structured Sparse Regression via Greedy Hard Thresholding Prateek Jain, Nikhil Rao, Inderjit S. Dhillon
- Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
- SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky
- Long-Term Trajectory Planning Using Hierarchical Memory Networks Stephan Zheng, Yisong Yue, Jennifer Hobbs
- Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola
- Observational-Interventional Priors for Dose-Response Learning Ricardo Silva
- Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Steven Z. Wu
- Identification and Overidentification of Linear Structural Equation Models Bryant Chen
- Adaptive Skills Adaptive Partitions (ASAP) Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
- Multiple-Play Bandits in the Position-Based Model Paul Lagrée, Claire Vernade, Olivier Cappe
- Optimal Black-Box Reductions Between Optimization Objectives Zeyuan Allen-Zhu, Elad Hazan
- On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege, Pierre-Louis Giscard, Richard Wilson
- Robustness of classifiers: from adversarial to random noise Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
- A Non-convex One-Pass Framework for Generalized Factorization Machines and Rank-One Matrix Sensing Ming Lin, Jieping Ye
- Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan
- Combinatorial Multi-Armed Bandit with General Reward Functions Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu
- Boosting with Abstention Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
- Regret of Queueing Bandits Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai
- Deep Learning Games Dale Schuurmans, Martin A. Zinkevich
- Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Antoine Gautier, Quynh N. Nguyen, Matthias Hein
- Learning Volumetric 3D Object Reconstruction from Single-View with Projective Transformations Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee
- A Credit Assignment Compiler for Joint Prediction Kai-Wei Chang, He He, Stephane Ross, Hal Daume III, John Langford
- Accelerating Stochastic Composition Optimization Mengdi Wang, Ji Liu
- Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans
- Consistent Kernel Mean Estimation for Functions of Random Variables Adam Scibior, Carl-Johann Simon-Gabriel, Ilya O. Tolstikhin, Prof. Bernhard Schölkopf
- Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
- Scalable Adaptive Stochastic Optimization Using Random Projections Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
- Variational Inference in Mixed Probabilistic Submodular Models Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
- Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated Namrata Vaswani, Han Guo
- The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider
- Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Kejun Huang, Xiao Fu, Nikolaos D. Sidiropoulos
- Bootstrap Model Aggregation for Distributed Statistical Learning JUN HAN, Qiang Liu
- A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification Steven Cheng-Xian Li, Benjamin M. Marlin
- A Bandit Framework for Strategic Regression Yang Liu, Yiling Chen
- Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan R. Salakhutdinov, Yoshua Bengio
- Statistical Inference for Cluster Trees Jisu KIM, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman
- Contextual-MDPs for PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford
- Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn
- Unsupervised Learning of Spoken Language with Visual Context David Harwath, Antonio Torralba, James Glass
- Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri
- PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien
- Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik
- Learned Region Sparsity and Diversity Also Predicts Visual Attention Zijun Wei, Hossein Adeli, Minh Hoai, Greg Zelinsky, Dimitris Samaras
- End-to-End Goal-Driven Web Navigation Rodrigo Nogueira, Kyunghyun Cho
- Automated scalable segmentation of neurons from multispectral images Uygar Sümbül, Douglas Roossien, Dawen Cai, John P. Cunningham, Liam Paninski
- Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan M. Rogers, Salil Vadhan, Aaron Roth, Jonathan Ullman
- Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Prof. Bernhard Schölkopf
- Adaptive optimal training of animal behavior Ji Hyun Bak, Jung Choi, Ilana Witten, Jonathan W. Pillow
- Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition Seyed Hamidreza Kasaei
- Relevant sparse codes with variational information bottleneck Matthew Chalk, Olivier Marre, Gasper Tkacik
- Combinatorial Energy Learning for Image Segmentation Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel
- Orthogonal Random Features Felix X. Yu, Ananda Theertha Suresh, Krzysztof M. Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar
- Fast Active Set Methods for Online Spike Inference from Calcium Imaging Johannes Friedrich, Liam Paninski
- Diffusion-Convolutional Neural Networks James Atwood
- Bayesian latent structure discovery from multi-neuron recordings Scott Linderman, Ryan P. Adams, Jonathan W. Pillow
- A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad, Vikash K. Mansinghka
- A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela Van Der Schaar
- Inference by Reparameterization in Neural Population Codes Rajkumar Vasudeva Raju, Xaq Pitkow
- Tensor Switching Networks Chuan-Yung Tsai, Andrew M. Saxe, David Cox
- Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
- Coordinate-wise Power Method Qi Lei, Kai Zhong, Inderjit S. Dhillon
- Learning Influence Functions from Incomplete Observations Xinran He, Ke Xu, David Kempe, Yan Liu
- Learning Structured Sparsity in Deep Neural Networks Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
- Sample Complexity of Automated Mechanism Design Maria-Florina F. Balcan, Tuomas Sandholm, Ellen Vitercik
- Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover
- Brains on Beats Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven
- Learning Transferrable Representations for Unsupervised Domain Adaptation Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese
- Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
- Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson
- Value Iteration Networks Aviv Tamar, Sergey Levine, Pieter Abbeel, YI WU, Garrett Thomas
- Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah
- On the Recursive Teaching Dimension of VC Classes Xi Chen, Xi Chen, Yu Cheng, Bo Tang
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
- Hardness of Online Sleeping Combinatorial Optimization Problems Satyen Kale, Chansoo Lee, David Pal
- Mixed Linear Regression with Multiple Components Kai Zhong, Prateek Jain, Inderjit S. Dhillon
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credit photo: NASA/JPL/University of Arizona

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