ICML Videos Link
8:30 to 10:00
Keynote Speaker: Carlos Guestrin
Monday, June 17, 10:30 to 12:10
Track A: Deep Learning 1
Session Chair: Marc’Aurelio Ranzato
Room: International 7, 8, 9, 10
Webcast Link
853, On autoencoder scoring,
Hanna Kamyshanska; Roland Memisevic
abstract/pdf
1124, On the difficulty of training Recurrent Neural Networks,
Razvan Pascanu; Tomas Mikolov; Yoshua Bengio
abstract/pdf/supplementary
1125, Maxout Networks,
Ian Goodfellow; David Warde-Farley; Mehdi Mirza; Aaron Courville; Yoshua Bengio
abstract/pdf
576, Collaborative hyperparameter tuning,
Rémi Bardenet; Mátyás Brendel; Balazs Kegl; Michele Sebag
abstract/pdf/supplementary
Spotlight Presentations:
136, Learning mid-level representations of objects by harnessing the aperture problem,
Roland Memisevic; Georgios Exarchakis
abstract/pdf
274, Approximation properties of DBNs with binary hidden units and real-valued visible units,
Oswin Krause; Asja Fischer; Tobias Glasmachers; Christian Igel
abstract/pdf
375, Better Mixing via Deep Representations,
Yoshua Bengio; Gregoire Mesnil; Yann Dauphin; Salah Rifai
abstract/pdf
532, Fast dropout training,
Sida Wang; Christopher Manning
abstract/pdf
Monday, June 17, 10:30 to 12:10
Track B: Compressed Sensing 1
Session Chair: Zico Kolter
Room: International 4, 5
210, Feature Selection in High-Dimensional Classification,
Mladen Kolar; Han Liu
abstract/pdf
105, Markov Network Estimation From Multi-attribute Data,
Mladen Kolar; Han Liu; Eric Xing
abstract/pdf
875, Exact Rule Learning via Boolean Compressed Sensing,
Dmitry Malioutov; Kush Varshney
abstract/pdf
118, Sparse Recovery under Linear Transformation,
Ji Liu; Lei Yuan; Jieping Ye
abstract/pdf
246, Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery,
Yudong Chen; Constantine Caramanis
abstract/pdf/supplementary
Monday, June 17, 10:30 to 12:10
Track C: Reinforcement Learning 1
Session Chair: Csaba Szepesvari
Room: International 1, 2, 3
1149, Learning Policies for Contextual Submodular Prediction,
Stephane Ross; Jiaji Zhou; Yisong Yue; Debadeepta Dey; Drew Bagnell
abstract/pdf/supplementary
412, Learning an Internal Dynamics Model from Control Demonstration,
Matthew Golub; Steven Chase; Byron Yu
abstract/pdf
423, Safe Policy Iteration,
Matteo Pirotta; Marcello Restelli; Alessio Pecorino; Daniele Calandriello
abstract/pdf/supplementary
755, Temporal Difference Methods for the Variance of the Reward To Go,
Aviv Tamar; Dotan Di Castro; Shie Mannor
abstract/pdf/supplementary
Spotlight Presentations:
465, Value Iteration with incremental representation learning for continuous POMDPs,
Sebastian Brechtel; Tobias Gindele; R diger Dillmann
abstract/pdf
39, The Sample-Complexity of General Reinforcement Learning,
Tor Lattimore; Marcus Hutter; Peter Sunehag
abstract/pdf
338, Online Feature Selection for Model-based Reinforcement Learning,
Trung Nguyen; Zhuoru Li; Tomi Silander; Tze Yun Leong
abstract/pdf/supplementary
1073, Bayesian Learning of Recursively Factored Environments,
Marc Bellemare; Joel Veness; Michael Bowling
abstract/pdf
Monday, June 17, 10:30 to 12:10
Track D: Social Networks
Session Chair: Andreas Krause
Room: International 6
1062, Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events,
Lisa Friedland; David Jensen; Michael Lavine
abstract/pdf/supplementary
475, Mixture of Mutually Exciting Processes for Viral Diffusion,
Shuang-Hong Yang; Hongyuan Zha
abstract/pdf/supplementary
172, Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks,
Creighton Heaukulani; Ghahramani Zoubin
abstract/pdf
828, Modeling Information Propagation with Survival Theory,
Manuel Gomez-Rodriguez; Jure Leskovec; Bernhard Schölkopf
abstract/pdf
Spotlight Presentations:
1123, Learning Triggering Kernels for Multi-dimensional Hawkes Processes,
Ke Zhou; Le Song; Hongyuan Zha
abstract/pdf
369, Causal Estimation of Peer Influence Effects,
Edward Kao; Panos Toulis; Edoardo Airoldi; Donald Rubin
974, Modeling Temporal Evolution and Multiscale Structure in Networks,
Tue Herlau; Morten Mørup; Mikkel Schmidt
abstract/pdf/supplementary
544, Scalable Optimization of Neighbor Embedding for Visualization,
Zhirong Yang; Jaakko Peltonen; Samuel Kaski
abstract/pdf/supplementary
Session III: 2:00 to 3:40
Monday, June 17, 2:00 to 3:40
Track A: Deep Learning 2
Session Chair: Ruslan Salakhutdinov
Room: International 7, 8, 9, 10
Webcast Link
925, Learning the Structure of Sum-Product Networks,
Robert Gens; Domingos Pedro
abstract/pdf
1129, Deep learning with COTS HPC systems,
Adam Coates; Brody Huval; Tao Wang; David Wu; Bryan Catanzaro; Ng Andrew
abstract/pdf
93, Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines,
Kihyuk Sohn; Guanyu Zhou; Chansoo Lee; Honglak Lee
abstract/pdf
1026, Regularization of Neural Networks using DropConnect,
Li Wan; Matthew Zeiler; Sixin Zhang; Yann Le Cun; Rob Fergus
abstract/pdf/supplementary
Spotlight Presentations:
502, Thurstonian Boltzmann Machines: Learning from Multiple Inequalities,
Truyen Tran; Dinh Phung; Svetha Venkatesh
abstract/pdf/supplementary
279, Iterative Learning and Denoising in Convolutional Neural Associative Memories,
Amin Karbasi; Amir Hesam Salavati; Amin Shokrollahi,
abstract/pdf/supplementary
457, No more pesky learning rates,
Tom Schaul; Sixin Zhang; Yann LeCun
abstract/pdf/supplementary
73, Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures,
James Bergstra; Daniel Yamins; David Cox
abstract/pdf
Monday, June 17, 2:00 to 3:40
Track B: Compressed Sensing 2
Session Chair: Tong Zhang
Room: International 4, 5
680, Learning Heteroscedastic Models by Convex Programming under Group Sparsity,
Arnak Dalalyan; Mohamed Hebiri; Katia Meziani; Joseph Salmon
abstract/pdf/supplementary
58, Noisy Sparse Subspace Clustering,
Yu-Xiang Wang; Huan Xu
abstract/pdf/supplementary
263, One-Bit Compressed Sensing: Provable Support and Vector Recovery,
Sivakant Gopi; Praneeth Netrapalli; Prateek Jain; Aditya Nori
abstract/pdf/supplementary
403, Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations,
Krishnakumar Balasubramanian; Kai Yu; Guy Lebanon
abstract/pdf/supplementary
Spotlight Presentations:
599, Sparse projections onto the simplex,
Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch
abstract/pdf
1056, Intersecting singularities for multi-structured estimation,
Emile Richard; Francis BACH; Jean-Philippe Vert
abstract/pdf/supplementary
29, Sparse Uncorrelated Linear Discriminant Analysis,
Xiaowei Zhang; Delin Chu
abstract/pdf
350, Estimating Unknown Sparsity in Compressed Sensing,
Miles Lopes
abstract/pdf/supplementary
Monday, June 17, 2:00 to 3:40
Track C: Reinforcement learning 2
Session Chair: Lihong Li
Room: International 1, 2, 3
955, Concurrent Reinforcement Learning from Customer Interaction Sequences,
David Silver; Leonard Newnham; David Barker; Suzanne Weller; Jason McFall
abstract/pdf
100, Modelling Sparse Dynamical Systems with Compressed Predictive State Representations,
William Hamilton; Mahdi Milani Fard,; Joelle Pineau,
abstract/pdf
1199, Coco-Q: Learning in Stochastic Games with Side Payments,
Elizabeth Hilliard; Eric Sodomka; Michael Littman; Amy Greenwald
abstract/pdf
26, Guided Policy Search,
Sergey Levine; Vladlen Koltun
abstract/pdf/supplementary
1069, The Cross-Entropy Method Optimizes for Quantiles,
Sergiu Goschin; Ari Weinstein; Michael Littman
abstract/pdf
Monday, June 17, 2:00 to 3:40
Track D: Topic Modeling 1
Session Chair: Emily Fox
Room: International 6
617, A Practical Algorithm for Topic Modeling with Provable Guarantees,
Sanjeev Arora; Rong Ge; Yonatan Halpern; David Mimno; Ankur Moitra; David Sontag; Yichen Wu; Michael Zhu
abstract/pdf/supplementary
376, Online Latent Dirichlet Allocation with Infinite Vocabulary,
KE ZHAI; Jordan Boyd-Graber
abstract/pdf
76, Gibbs Max-Margin Topic Models with Fast Sampling Algorithms,
Jun Zhu; Ning Chen; Hugh Perkins; Bo Zhang
abstract/pdf
Spotlight Presentations:
606, Modeling Musical Influence with Topic Models,
Uri Shalit; Daphna Weinshall; Gal Chechik
abstract/pdf/supplementary
1184, Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling,
Amr Ahmed; Liangjie Hong; Alexander Smola
abstract/pdf/supplementary
61, Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models,
Sinead Williamson; Avinava Dubey; Eric Xing
abstract/pdf/supplementary
354, MAD-Bayes: MAP-based Asymptotic Derivations from Bayes,
Tamara Broderick; Brian Kulis; Michael Jordan
abstract/pdf/supplementary
801, Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment,
Jason Chuang; Sonal Gupta; Christopher Manning; Jeffrey Heer
abstract/pdf/supplementary
Monday, June 17, 4:00 to 5:40
Track A: Deep Learning and Neuroscience
Session Chair: Yoshua Bengio
Room: International 7, 8, 9, 10
Webcast Link
1051, On the importance of initialization and momentum in deep learning, Ilya Sutskever; James Martens; George Dahl; Geoffrey Hinton abstract/pdf/supplementary
1055, A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines,
Kostadin Georgiev; Preslav Nakov
abstract/pdf
219, Parsing epileptic events using a Markov switching process model for correlated time series,
Drausin Wulsin; Emily Fox; Brian Litt
abstract/pdf
611, Exploring the Mind: Integrating Questionnaires and fMRI,
Esther Salazar; Ryan Bogdan; Adam Gorka; Ahmad Hariri; Lawrence Carin
abstract/pdf
Spotlight Presentations:
552, Gated Autoencoders with Tied Input Weights,
Alain Droniou; Olivier Sigaud
abstract/pdf
696, Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images,
Kyunghyun Cho
abstract/pdf
983, Natural Image Bases to Represent Neuroimaging Data,
Ashish Gupta; Murat Ayhan; Anthony Maida
abstract/pdf/supplementary
658, Direct Modeling of Complex Invariances for Visual Object Features,
Ka Yu Hui
abstract/pdf
Monday, June 17, 4:00 to 5:40
Track B: Compressed Sensing 3
Session Chair: Alekh Agarwal
Room: International 4, 5
693, Spectral Compressed Sensing via Structured Matrix Completion,
Yuxin Chen; Yuejie Chi
abstract/pdf/supplementary
870, Sparse PCA through Low-rank Approximations,
Dimitris Papailiopoulos; Alexandros Dimakis; Stavros Korokythakis
abstract/pdf
179, Efficient Sparse Group Feature Selection via Nonconvex Optimization,
Shuo Xiang; Xiaotong Shen; Jieping Ye
abstract/pdf/supplementary
500, A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems,
Pinghua Gong; Changshui Zhang; Zhaosong Lu; Jianhua Huang; Jieping Ye
abstract/pdf
876, Robust Sparse Regression under Adversarial Corruption,
Yudong Chen; Constantine Caramanis; Shie Mannor
abstract/pdf/supplementary
Monday, June 17, 4:00 to 5:40
Track C: Reinforcement Learning and Time Series
Session Chair: Joelle Pineau
Room: International 1, 2, 3
840, ABC Reinforcement Learning,
Christos Dimitrakakis; Nikolaos Tziortziotis
abstract/pdf
393, Mean Reversion with a Variance Threshold,
Marco Cuturi; Alexandre d’Aspremont
abstract/pdf
1029, Gaussian Process Kernels for Pattern Discovery and Extrapolation,
Andrew Wilson; Ryan Adams
abstract/pdf/supplementary
Spotlight Presentations:
207, Average Reward Optimization Objective In Partially Observable Domains,
Yuri Grinberg; Doina Precup
abstract/pdf/supplementary
463, Planning by Prioritized Sweeping with Small Backups,
Harm van Seijen; Rich Sutton
abstract/pdf
780, Dynamic Covariance Models for Multivariate Financial Time Series,
Yue Wu; Jose Miguel Hernandez-Lobato; Ghahramani Zoubin
abstract/pdf
300, Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression,
Toby Hocking; Guillem Rigaill; Jean-Philippe VERT; Francis BACH
abstract/pdf
670, Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data,
Jan-Willem Van de Meent; Jonathan Bronson; Frank Wood; Ruben Gonzalez, Jr.; Chris Wiggins
abstract/pdf
529, Learning Connections in Financial Time Series,
Gartheeban Ganeshapillai; John Guttag; Andrew Lo
abstract/pdf
1042, The Extended Parameter Filter, Yusuf Bugra Erol;
Lei Li; Bharath Ramsundar; Russell Stuart
abstract/pdf/supplementary
563, Transition Matrix Estimation in High Dimensional Time Series,
Fang Han; Han Liu
abstract/pdf
Monday, June 17, 4:00 to 5:40
Track D: Topic Modeling 2
Session Chair: Elad Hazan
Room: International 6
977, Dependent Normalized Random Measures,
Changyou Chen; Vinayak Rao; Yee Whye Teh; Wray Buntine
abstract/pdf/supplementary
1070, Topic Discovery through Data Dependent and Random Projections,
Weicong Ding; Mohammad Hossein Rohban; Prakash Ishwar; Venkatesh Saligrama
abstract/pdf/supplementary
821, Factorial Multi-Task Learning : A Bayesian Nonparametric Approach,
Sunil Gupta; Dinh Phung; Svetha Venkatesh
abstract/pdf
Spotlight Presentations:
1003, Scaling the Indian Buffet Process via Submodular Maximization,
Colorado Reed; Ghahramani Zoubin
abstract/pdf/supplementary
506, A Variational Approximation for Topic Modeling of Hierarchical Corpora,
Do-kyum Kim; Geoffrey Voelker; Lawrence Saul
abstract/pdf/supplementary
1156, Manifold Preserving Hierarchical Topic Models for Quantization and Approximation,
Minje Kim; Paris Smaragdis
abstract/pdf
607, Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically ,
Mrinal Das; Suparna Bhattacharya; Chiranjib Bhattacharyya; Gopinath Kanchi
abstract/pdf/supplementary
852, Latent Dirichlet Allocation Topic Model with Soft Assignment of Descriptors to Words,
Daphna Weinshall; Gal Levi; Dmitri Hanukaev
abstract/pdf
692, Efficient Multi-label Classification with Many Labels,
Wei Bi; James Kwok
abstract/pdf
242, A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning,
Arash Afkanpour; Andras Gyorgy; Csaba Szepesvari; Michael Bowling
abstract/pdf
112, MILEAGE: Multiple Instance LEArning with Global Embedding,
Dan Zhang; Jingrui He; Luo Si; Richard Lawrence
abstract/pdf/supplementary
Monday, June 17, 7:30 to 8:00
Poster Setup
Room: Skyline, 10th Floor
Monday, June 17, 8:00 to 10:00
Poster Session
Room: Skyline, 10th Floor