Saturday Morning Videos: New videos of ICML 2015 in Lille

Rich Sutton locked out of the RL workshop at #nips2015 because it's too full :) pic.twitter.com/VutJwIAYW0

— Nando de Freitas (@NandoDF) December 11, 2015

As NIPS sessions have to turn away people, here are some videos of ICML this past summer. A month ago we mentioned some of these videos but here are some new ones:

Tutorials
13 views, 2:04:30   Tutorial Natural Language Understanding: Foundations and State-of-the-Art Percy Liang	14 views, 1:10:55   Tutorial Advances in Structured Prediction Hal Daumé III	9 views, 54:29   Tutorial Modern Convex Optimization Methods for Large-scale Empirical Risk Minimization Mark Schmidt	12 views, 1:55:23     Tutorial Bayesian Time Series Modeling: Structured Representations for Scalability Emily Fox
6 views, 2:01:20   Tutorial Computational Social Science Hanna M. Wallach 6 views, 2:18:53   Tutorial Policy Search: Methods and Applications Gerhard Neumann, Jan Peters
Welcome Address
5 views, 09:27   Welcome address Joelle Pineau, David Blei, Francis R. Bach
Keynote Speakers
15 views, 41:17   Keynote Two high stakes challenges in machine learning Léon Bottou	2 views, 52:12   Keynote Learning Treatment Policies in Mobile Health Susan A. Murphy	6 views, 1:06:13   Keynote Social Interaction in Global Networks Jon Kleinberg
Awards
19:07   Best Paper Award A Nearly-Linear Time Framework for Graph-Structured Sparsity Ludwig Schmidt	7 views, 15:09   Test of Time Award Learning to Rank Using Gradient Descent Chris Burges
Deep Learning I
2 views, 15:58   Learning Program Embeddings to Propagate Feedback on Student Code Chris Piech	16:36   BilBOWA: Fast Bilingual Distributed Representations without Word Alignments Stephan Gouws	8 views, 15:44   Modeling Order in Neural Word Embeddings at Scale David Gilmore, Andrew Trask	4 views, 18:02   Gated Feedback Recurrent Neural Networks Junyoung Chung
3 views, 14:32   An Empirical Exploration of Recurrent Network Architectures Rafal Jozefowicz     Distributed Optimization
1 view, 16:15   PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent Cho-Jui Hsieh	2 views, 14:27   An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization Garud Iyengar	4 views, 16:57   DiSCO: Distributed Optimization for Self-Concordant Empirical Loss Yuchen Zhang	16:31   Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM Ching-Pei Lee
3 views, 15:50   Adding vs. Averaging in Distributed Primal-Dual Optimization Virginia Smith     Bayesian Nonparametrics I
1 view, 18:56   A Bayesian nonparametric procedure for comparing algorithms Francesca Mangili	4 views, 17:57   Manifold-valued Dirichlet Processes Hyunwoo J. Kim	18:03   JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes Jonathan Huggins	1 view, 17:42   Atomic Spatial Processes Neil Spencer
2 views, 17:37   DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics Yining Wang     Reinforcement Learning I
73 views, 19:20   Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing Alan Malek	27 views, 15:22   Off-policy Model-based Learning under Unknown Factored Dynamics Assaf Hallak	81 views, 15:56   A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits Pratik Gajane	13:33   On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence Nathaniel Korda
Learning Theory I
25 views, 15:19   Entropy-Based Concentration Inequalities for Dependent Variables Liva Ralaivola	34 views, 18:27   The Ladder: A Reliable Leaderboard for Machine Learning Competitions Moritz Hardt 1 comment	25 views, 15:40   Theory of Dual-sparse Regularized Randomized Reduction Tianbao Yang	14 views, 15:07   A Theoretical Analysis of Metric Hypothesis Transfer Learning Michaël Perrot
20 views, 15:19   Optimizing Non-decomposable Performance Measures: A Tale of Two Classes Harikrishna Narasimhan     Supervised Learning I
25 views, 18:48   Learning from Corrupted Binary Labels via Class-Probability Estimation Aditya Menon	30 views, 17:00   Multi-instance multi-label learning in the presence of novel class instances Raviv Raich	6 views, 18:43   Entropy evaluation based on confidence intervals of frequency estimates: Application to the learning of decision trees Mathieu Serrurier	50 views, 13:08   Support Matrix Machines Luo Luo
17 views, 13:16   Attribute Efficient Linear Regression with Distribution-Dependent Sampling Doron Kukliansky     Bandit Learning
2 views, 19:06   Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays Junpei Komiyama	1 view, 15:08   Simple regret for infinitely many armed bandits Alexandra Carpentier	1 view, 18:06   Efficient Learning in Large-Scale Combinatorial Semi-Bandits Branislav Kveton	23:31   Cascading Bandits: Learning to Rank in the Cascade Model Branislav Kveton
19:07   Cheap Bandits Manjesh Kumar Hanawal 3 views, 22:33   Qualitative Multi-Armed Bandits: A Quantile-Based Approach Szörényi Balázs
Deep Learning Computations
17:37   Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix Roger Grosse	1 view, 12:53   Compressing Neural Networks with the Hashing Trick Kilian Q. Weinberger	7 views, 16:37   Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe	18:20   Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens
1 view, 18:05   Deep Learning with Limited Numerical Precision Suyog Gupta   Causality
1 view, 17:59   Removing systematic errors for exoplanet search via latent causes Bernhard Schölkopf	18 views, 17:38   Towards a Learning Theory of Cause-Effect Inference Krikamol Muandet	2 views, 17:07   Counterfactual Risk Minimization: Learning from Logged Bandit Feedback Adith Swaminathan	2 views, 16:23   Telling cause from effect in deterministic linear dynamical systems Naji Shajarisales
2 views, 17:11   Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger 17:16   Discovering Temporal Causal Relations from Subsampled Data Kun Zhang
Matrix Factorization
26 views, 17:48   Guaranteed Tensor Decomposition: A Moment Approach Gongguo Tang	19 views, 17:58   PU Learning for Matrix Completion Cho-Jui Hsieh	19 views, 18:18   Low-Rank Matrix Recovery from Row-and-Column Affine Measurements Avishai Wagner	18 views, 20:36   Intersecting Faces: Non-negative Matrix Factorization With New Guarantees James Y. Zou
22 views, 10:12   CUR Algorithm for Partially Observed Matrices Miao Xu 13 views, 18:16   An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection Tianbao Yang
Sparsity I
17 views, 21:29   Swept Approximate Message Passing for Sparse Estimation Eric W. Tramel 1 comment	13 views, 17:32   Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing Rongda Zhu	22 views, 20:24   Sparse Subspace Clustering with Missing Entries René Vidal	12 views, 16:47   Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems Qiang Zhou
34 views, 20:50   Complete Dictionary Recovery Using Nonconvex Optimization John Wright       Ranking Learning
18 views, 16:58   Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top Arun Rajkumar	15 views, 18:09   MRA-based Statistical Learning from Incomplete Rankings Eric Sibony	15:08   Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons	7 views, 15:28   Distributional Rank Aggregation, and an Axiomatic Analysis Harsh Pareek
17 views, 19:07   Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons Changho Suh 12 views, 14:44   Generalization error bounds for learning to rank: Does the length of document lists matter? Sougata Chaudhuri
Optimization I
4 views, 17:30   A Lower Bound for the Optimization of Finite Sums Alekh Agarwal	1 view, 15:35   Stochastic Optimization with Importance Sampling for Regularized Loss Minimization Peilin Zhao	1 view, 15:59   Stochastic Dual Coordinate Ascent with Adaptive Probabilities Zheng Qu	17:26   Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems Christopher De Sa
Structured Prediction I
4 views, 19:16   How Hard is Inference for Structured Prediction? David Sontag	18:22   Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs Stephen Bach	1 view, 18:19   Learning Submodular Losses with the Lovasz Hinge Jiaqian Yu	18:26   PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data Toby Dylan Hocking
Reinforcement Learning II
18:23   Non-Stationary Approximate Modified Policy Iteration Bruno Scherrer	16:51   Trust Region Policy Optimization John Schulman	17:29   Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning Ronald Ortner	17:23   Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games Julien Perolat
Gaussian Processes I
12 views, 19:13   Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods Seth Flaxman	11 views, 18:59   A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data Bryan Kian Hsiang Low	15 views, 18:57   Controversy in mechanistic modelling with Gaussian processes Benn Macdonald, Dirk Husmeier	59 views, 17:06   Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) Andrew Gordon Wilson
Transfer Learning I
14 views, 18:02   Active Nearest Neighbors in Changing Environments Christopher Berlind	27 views, 18:18   Asymmetric Transfer Learning with Deep Gaussian Processes Melih Kandemir	43 views, 16:34   An embarrassingly simple approach to zero-shot learning Bernardino Romera-Paredes	7 views, 14:45   Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices Tianbao Yang
Networks and Graphs I
8 views, 16:58   A Divide and Conquer Framework for Distributed Graph ClusteringLearning with Multiple Data Matrices Wenzhuo Yang	24 views, 12:12   Scalable Model Selection for Large-Scale Factorial Relational Models Lu Feng	26 views, 19:17   Consistent estimation of dynamic and multi-layer block models Kevin Shuai Xu	12 views, 19:07   Community Detection Using Time-Dependent Personalized PageRank Haim Avron
Deep Learning II
1 view, 18:04   Variational Generative Stochastic Networks with Collaborative Shaping Philip Bachman	15:28   How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? Senjian An	9 views, 16:10   Unsupervised Domain Adaptation by Backpropagation Yaroslav Ganin	18:24   Learning Transferable Features with Deep Adaptation Networks Mingsheng Long
Networks and Graphs II
1 view, 18:49   Correlation Clustering in Data Streams Sudipto Guha	17:55   An Aligned Subtree Kernel for Weighted Graphs Lu Bai	18:12   HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades Xinran He	14:04   Inferring Graphs from Cascades: A Sparse Recovery Framework Thibaut Horel
Online Learning I
12:18   Strongly Adaptive Online Learning Alon Gonen	1 view, 18:01   Adaptive Belief Propagation Georgios Papachristoudis	18:30   The Hedge Algorithm on a Continuum Walid Krichene
Probabilistic Models I
15 views, 16:57   Inference in a Partially Observed Queuing Model with Applications in Ecology Kevin Winner	7 views, 14:16   Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood Wenzhao Lian	13 views, 15:29   On the Relationship between Sum-Product Networks and Bayesian Networks Han Zhao	35 views, 17:51   Fixed-point algorithms for learning determinantal point processes Zelda Mariet
Learning Theory II
8 views, 13:19   Convex Calibrated Surrogates for Hierarchical Classification Harish G. Ramaswamy	4 views, 17:08   Risk and Regret of Hierarchical Bayesian Learners Jonathan Huggins	19 views, 12:40   Classification with Low Rank and Missing Data Roi Livni	19 views, 20:14   Surrogate Functions for Maximizing Precision at the Top Purushottam Kar
Transfer Learning II
15 views, 18:46   Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer Shan-Hung Wu	12 views, 17:28   A Probabilistic Model for Dirty Multi-task Feature Selection Daniel Hernandez-Lobato	10 views, 19:10   Convex Learning of Multiple Tasks and their Structure Carlo Ciliberto	16 views, 12:42   Convex Formulation for Learning from Positive and Unlabeled Data Marthinus Christoffel du Plessis
Deep Learning and Vision
12:58   Unsupervised Learning of Video Representations using LSTMs Elman Mansimov	24 views, 17:19   Deep Edge-Aware Filters Jimmy SJ. Ren	18:25   DRAW: A Recurrent Neural Network For Image Generation Karol Gregor	1 view, 19:32   Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu
17:44   Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network Seunghoon Hong   Bayesian Optimization
2 views, 18:25   Gradient-based Hyperparameter Optimization through Reversible Learning David Kristjanson Duvenaud	18:27   Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek	1 view, 18:58   High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy	2 views, 16:35   Safe Exploration for Optimization with Gaussian Processes Alkis Gotovos
1 view, 19:23   Predictive Entropy Search for Bayesian Optimization with Unknown Constraints José Miguel Hernández-Lobato     Variational Inference
5 views, 17:27   A trust-region method for stochastic variational inference with applications to streaming data Lucas Theis	12 views, 16:13   Variational Inference with Normalizing Flows Danilo Jimenez Rezende	2 views, 18:39   Markov Chain Monte Carlo and Variational Inference: Bridging the Gap Tim Salimans	17:49   An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process Amar Shah
1 view, 16:40   Scalable Variational Inference in Log-supermodular Models Josip Djolonga   Large Scale Learning
13 views, 16:35   Coresets for Nonparametric Estimation - the Case of DP-Means Olivier Bachem	8 views, 15:52   K-hyperplane Hinge-Minimax Classifier Margarita Osadchy	6 views, 16:51   Faster cover trees Mike Izbicki	16 views, 13:21   Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis Zhuang Ma
16 views, 16:55   Large-scale log-determinant computation through stochastic Chebyshev expansions Insu Han     Structured Prediction II
31 views, 16:20   Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction Amélie Rolland	4 views, 16:30   Learning Scale-Free Networks by Dynamic Node Specific Degree Prior Jinbo Xu	23 views, 18:23   Learning Deep Structured Models Liang-Chieh Chen	20 views, 16:40   Learning to Search Better than Your Teacher Akshay Krishnamurthy
15 views, 15:26   Learning Fast-Mixing Models for Structured Prediction Jacob Steinhardt      Manifold Learning
22 views, 15:32   Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations Tam Le 1 comment	13 views, 17:13   Learning Local Invariant Mahalanobis Distances Ethan Fetaya	12 views, 16:37   Landmarking Manifolds with Gaussian Processes Dawen Liang	6 views, 18:05   Subsampling Methods for Persistent Homology Bertrand Michel
3 views, 13:34   Bipartite Edge Prediction via Transductive Learning over Product Graphs Hanxiao Liu     Gaussian Processes II
2 views, 16:29   Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data Yarin Gal	18:37   Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes Yves-Laurent Kom Samo	17:55   Finding Galaxies in the Shadows of Quasars with Gaussian Processes Roman Garnett
Monte Carlo Methods
1 view, 16:51   Exponential Integration for Hamiltonian Monte Carlo Wei-Lun Chao	3 views, 18:39   Nested Sequential Monte Carlo Methods Christian Andersson Naesseth	1 view, 22:19   The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling Michael Betancourt
Hashing
1 view, 21:27   Binary Embedding: Fundamental Limits and Fast Algorithm Constantine Caramanis	1 view, 10:25   Hashing for Distributed Data Cong Leng	5 views, 13:45   On Symmetric and Asymmetric LSHs for Inner Product Search Nathan Srebro
Feature Selection I
12 views, 16:21   Multiview Triplet Embedding: Learning Attributes in Multiple Maps Ehsan Amid	9 views, 16:32   Low Rank Approximation using Error Correcting Coding Matrices Shashanka Ubaru	7 views, 20:44   A Unified Framework for Outlier-Robust PCA-like Algorithms Wenzhuo Yang
Kernel Methods
13 views, 18:49   A low variance consistent test of relative dependency Wacha Bounliphone	7 views, 16:08   Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets Woosang Lim	3 views, 15:25   The Kendall and Mallows Kernels for Permutations Yunlong Jiao
Computational Advertising and Social Science
7 views, 17:57   Threshold Influence Model for Allocating Advertising Budgets Atsushi Miyauchi	48 views, 18:20   Approval Voting and Incentives in Crowdsourcing Nihar B. Shah 2 comments	11 views, 15:24   Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View Akihiro Yabe
Optimization II
16:17   Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection Julie Nutini	18:51   A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate Ohad Shamir	19:05   Online Learning of Eigenvectors Dan Garber
Approximate Inference I
19:50   A Hybrid Approach for Probabilistic Inference using Random Projections Stefano Ermon	16:40   Reified Context Models Jacob Steinhardt	18:46   Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood Kohei Hayashi
Bayesian Nonparametrics II
13:00   Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models Mrinal Kanti Das	2 views, 22:52   Bayesian and Empirical Bayesian Forests Matt Taddy	18:34   Metadata Dependent Mondrian Processes Yi Wang
Time Series Analysis I
47 views, 16:42   Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams Qi (Rose) Yu	27 views, 15:47   A Multitask Point Process Predictive Model Wenzhao Lian	15 views, 18:56   Moderated and Drifting Linear Dynamical Systems Jinyan Guan
Feature Selection II
15 views, 17:55   Streaming Sparse Principal Component Analysis Wenzhuo Yang	7 views, 14:47   Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA Bo Xin	7 views, 17:48   Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares Alex Gittens
Unsupervised Learning I
55 views, 16:06   Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Sohl-Dickstein	16 views, 16:09   Structural Maxent Models Vitaly Kuznetsov	27 views, 15:46   Hidden Markov Anomaly Detection Nico Görnitz
Optimization III
18:26   Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets Dan Garber	1 view, 18:33   Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods Zirui Zhou	2 views, 15:18   Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization Roy Frostig
Probabilistic Models II
15:30   Harmonic Exponential Families on Manifolds Taco Cohen	15:33   Celeste: Variational inference for a generative model of astronomical images Jeffrey Regier	20:28   Vector-Space Markov Random Fields via Exponential Families Wesley Tansey	16:06   Dealing with small data: On the generalization of context trees Ralf Eggeling
Natural Language Processing I
16:36   Learning Word Representations with Hierarchical Sparse Coding Dani Yogatama	16:58   A Linear Dynamical System Model for Text David Belanger	13:26   From Word Embeddings To Document Distances Matt J. Kusner	19:05   A Fast Variational Approach for Learning Markov Random Field Language Models Yacine Jernite
Submodularity
26 views, 18:08   On Greedy Maximization of Entropy Dravyansh Sharma	9 views, 17:00   Submodularity in Data Subset Selection and Active Learning Kai Wei	8 views, 19:09   Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions Alina Raluca Ene	11 views, 18:31   The Power of Randomization: Distributed Submodular Maximization on Massive Datasets Justin Ward
Online Learning II
8 views, 17:01   An Online Learning Algorithm for Bilinear Models Yuanbin Wu	13 views, 21:47   Online Time Series Prediction with Missing Data Oren Anava	5 views, 21:56   On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments Yifan Wu	6 views, 14:16   Following the Perturbed Leader for Online Structured Learning Alon Cohen
Unsupervised Learning II
4 views, 17:40   Entropic Graph-based Posterior Regularization Maxwell Libbrecht	8 views, 15:25   Information Geometry and Minimum Description Length Networks Ke Sun	22 views, 19:24   Context-based Unsupervised Data Fusion for Decision Making Mihaela van der Schaar	9 views, 17:44   Alpha-Beta Divergences Discover Micro and Macro Structures in Data Sergey Levine
Deep Learning III
14:40   Weight Uncertainty in Neural Network Charles Blundell	14:06   MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain	11:47   Generative Moment Matching Networks Kevin Swersky	3 views, 18:58   Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato
16:03   Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions Taehoon Lee   Privacy
42 views, 18:41   Is Feature Selection Secure against Training Data Poisoning? Battista Biggio	2 views, 19:23   Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu-Xiang Wang	3 views, 18:43   Rademacher Observations, Private Data, and Boosting Giorgio Patrini	1 view, 19:33   The Composition Theorem for Differential Privacy Sewoong Oh
1 view, 17:41   Differentially Private Bayesian Optimization Matt J. Kusner     Topic Models
18:19   Markov Mixed Membership Models John Paisley	18:02   Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models James Foulds	17:05   Scalable Deep Poisson Factor Analysis for Topic Modeling David E. Carlson	20:49   Ordinal Mixed Membership Models Seppo Virtanen
19:03   Efficient Training of LDA on a GPU by Mean-for-Mode Estimation Jean-Baptiste Tristan   Reinforcement Learning III
50 views, 15:20   Training Deep Convolutional Neural Networks to Play Go Christopher Clark	5 views, 18:19   Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret Eric Eaton	4 views, 18:00   Robust partially observable Markov decision process Takayuki Osogami	12 views, 15:42   Fictitious Self-Play in Extensive-Form Games Johannes Heinrich
12 views, 16:18   A Deeper Look at Planning as Learning from Replay Harm Van Seijen   Gaussian Processes III
21 views, 19:52   Distributed Gaussian Processes Marc Peter Deisenroth	12 views, 18:03   Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) Maurizio Filippone	45 views, 19:04   Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Yarin Gal	5 views, 19:24   Sparse Variational Inference for Generalized GP Models Rishit Sheth
Supervised Learning II
17 views, 15:14   On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property Maxime Gasse	6 views, 16:02   Consistent Multiclass Algorithms for Complex Performance Measures Harikrishna Narasimhan	14 views, 17:34   Feature-Budgeted Random Forest Feng Nan	3 views, 16:39   A New Generalized Error Path Algorithm for Model Selection Bin Gu
2 views, 12:32   Dynamic Sensing: Better Classification under Acquisition Constraints Oran Richman     Reinforcement Learning IV
1 view, 17:43   Universal Value Function Approximators Tom Schaul	15:13   High Confidence Policy Improvement Philip S. Thomas	16:21   Abstraction Selection in Model-based Reinforcement Learning Nan Jiang
Optimization IV
2 views, 17:12   Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang	18:05   Adaptive Stochastic Alternating Direction Method of Multipliers Peilin Zhao	15:39   A General Analysis of the Convergence of ADMM Robert Nishihara
Bayesian Nonparametrics III
13:58   A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models Jimmy Xin Lin	17:18   Distributed Inference for Dirichlet Process Mixture Models Yutian Chen	17:06   Large-scale Distributed Dependent Nonparametric Trees Zhiting Hu
Sparsity II
9 views, 18:18   A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data Yining Wang	10 views, 16:45   Stay on path: PCA along graph paths Megasthenis Asteris	11 views, 14:36   Geometric Conditions for Subspace-Sparse Recovery René Vidal
Clustering I
19 views, 19:40   A Convex Optimization Framework for Bi-Clustering Huan Xu	26 views, 18:43   Multi-Task Learning for Subspace Segmentation Yu Wang	2 views, 16:33   Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization Jinbo Bi
Vision
12 views, 16:46   Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM Xiaojun Chang	11 views, 15:30   Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification Bo Xin	17 views, 17:21   Bayesian Multiple Target Localization Peter I. Frazier
Sparse optimization
17:22   Mind the duality gap: safer rules for the Lasso Alexandre Gramfort	13:56   Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains Sören Laue	14:50   A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis Jinbo Bi
Approximate Inference II
17:35   The Benefits of Learning with Strongly Convex Approximate Inference Ben London	16:31   Message Passing for Collective Graphical Models Tao Sun	17:31   Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach Jason L. Pacheco
Natural Language Processing II
16:45   Phrase-based Image Captioning Rémi Lebret	13:57   Long Short-Term Memory Over Recursive Structures Xiaodan Zhu	13:41   Bimodal Modelling of Source Code and Natural Language Miltos Allamanis
Time Series Analysis II
10 views, 18:15   Learning Parametric-Output HMMs with Two Aliased States Roi Weiss	9 views, 12:01   Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes Sheng Xu	28 views, 20:00   Functional Subspace Clustering with Application to Time Series Yan Liu
Clustering II
11 views, 19:39   Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup Xipeng Shen	11 views, 16:28   A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning Debarghya Ghoshdastidar	12:17   Spectral Clustering via the Power Method - Provably Alex Gittens
Learning Theory III
28 views, 14:43   Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds Yuchen Zhang	30 views, 18:25   Deterministic Independent Component Analysis Csaba Szepesvári	11 views, 16:26   Convergence rate of Bayesian tensor estimator and its minimax optimality Taiji Suzuki

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Perfect Recovery Conditions For Non-Negative Sparse Modeling / Compressive hyperspectral imaging via adaptive sampling and dictionary learning

 

Perfect Recovery Conditions For Non-Negative Sparse Modeling by Yuki Itoh, Marco F. Duarte, Mario Parente

Sparse modeling has been widely and successfully used in many applications such as computer vision, machine learning, and pattern recognition and, accompanied with those applications, significant research has studied the theoretical limits and algorithm design for convex relaxations in sparse modeling. However, only little has been done for theoretical limits of non-negative versions of sparse modeling. The behavior is expected to be similar as the general sparse modeling, but a precise analysis has not been explored. This paper studies the performance of non-negative sparse modeling, especially for non-negativity constrained and ℓ1-penalized least squares, and gives an exact bound for which this problem can recover the correct signal elements. We pose two conditions to guarantee the correct signal recovery: minimum coefficient condition (MCC) and non-linearity vs. subset coherence condition (NSCC). The former defines the minimum weight for each of the correct atoms present in the signal and the latter defines the tolerable deviation from the linear model relative to the positive subset coherence (PSC), a novel type of "coherence" metric. We provide rigorous performance guarantees based on these conditions and experimentally verify their precise predictive power in a hyperspectral data unmixing application.

 
 

Compressive hyperspectral imaging via adaptive sampling and dictionary learning by Mingrui Yang, Frank de Hoog, Yuqi Fan, Wen Hu

In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary from training spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain the compressive measurements for reconstruction. The proposed method provides significant improvement over the conventional compressive sensing approaches. The reconstruction performance is further improved by reconditioning the sensing matrix using matrix balancing. We also demonstrate that the combination of dictionary learning and SVD is robust by applying them to different datasets.

 
 

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Compressive Sampling using Annihilating Filter-based Low-Rank Interpolation

Compressive Sampling using Annihilating Filter-based Low-Rank Interpolation by Jong Chul Ye, Jong Min Kim, Kyong Hwan Jin

While the recent theory of compressed sensing or compressive sampling (CS) provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a recovery algorithm usually takes the form of penalized least squares or constraint optimization framework that is different from classical signal sampling theory. In this paper, we provide a drastically different compressive sampling framework that can exploit all the benefits of the CS, but can be still implemented in a classical sampling framework using a digital correction filter. The main idea is originated from the fundamental duality between the sparsity in the primary space and the low-rankness of a structured matrix in the reciprocal spaces, which demonstrates that the low-rank interpolator as a digital correction filter can enjoy all the optimality of the standard CS. We show that the idea can be generalised to recover signals in large class of signals such as piece-wise polynomial, and spline representations. Moreover, by restricting signal class as cardinal splines, the proposed low-rank interpolation approach can achieve inherent regularization to improve the noise robustness. Using the powerful dual certificates and golfing scheme by Gross, we show that the new framework still achieves the near-optimal sampling rate for signal recovery. Numerical results using various type of signals confirmed that the proposed scheme has significant better phase transition than the conventional CS approaches.

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Challenge: Computational Imaging for VLBI Image Reconstruction

Here is a reconstruction endeavor that could eventually help the Event Horizon Telescope: Computational Imaging for VLBI Image Reconstruction by Katherine L. Bouman, Michael D. Johnson, Daniel Zoran, Vincent L. Fish, Sheperd S. Doeleman, William T. Freeman

Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods require careful tuning and parameter selection for different types of images, our method is robust and produces good results under different settings such as low SNR or extended emissions. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the computer vision community, and provide a dataset website (vlbiimaging.csail.mit.edu) to allow for controlled comparisons across algorithms. This dataset can foster development of new methods by making VLBI easily approachable to computer vision researchers.

At vlbiimaging.csail.mit.edu, they have a Dataset Designed to Train and Test Very Long Baseline Interferometry Image Reconstruction Algorithms. From the first page:

Welcome to the VLBI Reconstruction Dataset!

The goal of this website is to provide a testbed for developing new VLBI reconstruction algorithms. By supplying a large set of easy to understand training and testing data, we hope to make the problem more accessible to those less familiar with the VLBI field. Specifically, this website contains a:

• Large set of synthetic training data for many different VLBI arrays and targets
• Set of real data measurements provided in the same standard format
• Standardized data set for testing VLBI Image Reconstruction Algorithms
• Online quantitative evaluation of algorithm performance on simulated testing data
• Qualitative comparison of algorithm performance on the reconstruction of real data
• Online form to easily simulate realistic data using your own image and telescope parameters

What is VLBI Imaging?

Imaging distant celestial sources with high resolving power requires telescopes with prohibitively large diameters due to the inverse relationship between angular resolution and telescope diameter. However, by simultaneously collecting data from an array of telescopes located around the Earth, it is possible to emulate samples from a single telescope with a diameter equal to the maximum distance between telescopes in the array. Using multiple telescopes in this manner is referred to as very long baseline interferometry (VLBI).
Reconstructing an image using VLBI measurements is an ill-posed problem, and as such each there are an infinite number of possible images that explain the data. The challenge is to find an explanation that respects these prior assumptions while still satisfying the observed data. The goal of this website to aid in the process of developing these algorithms as well as evaluate their performance.

The ongoing international effort to create an Event Horizon Telescope capable of imaging the enviroment around a black hole’s event horizon calls for the use of VLBI reconstruction algorithms. The angular resolution necessary for these measurements requires overcoming many challenges, all of which make image reconstruction more difficult. For instance, at the mm/sub-mm wavelengths being observed, rapidly varying inhomogeneities in the atmosphere introduce additional measurement errors. Robust algorithms that are able to reconstruct images in this fine angular resolution regime are essential for scientific progress.

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