ICML Videos Link
8:30 to 10:00
Keynote Speaker: Santosh Vempala
Tuesday, June 18, 10:30 to 12:10
Track A: Online Learning 1
Session Chair: Ofer Dekel
Room: International 7, 8, 9, 10
Webcast Link
805, Online Kernel Learning with a Near Optimal Sparsity Bound,
Lijun Zhang; Rong Jin; Xiaofei He
abstract/pdf/supplementary
710, On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions,
Prateek Jain; Bharath Sriperumbudur; Purushottam Kar; Harish Karnick
abstract/pdf/supplementary
178, Thompson Sampling for Contextual Bandits with Linear Payoffs,
Shipra Agrawal; Navin Goyal
abstract/pdf/supplementary
1189, Online Learning under Delayed Feedback,
Pooria Joulani; Andras Gyorgy; Csaba Szepesvari
abstract/pdf
1102, Almost Optimal Exploration in Multi-Armed Bandits,
Zohar Karnin; Tomer Koren; Oren Somekh
abstract/pdf/
Tuesday, June 18, 10:30 to 12:10
Track B: Feature Learning
Session Chair: Yoshua Bengio
Room: International 4, 5
508, Forecastable Component Analysis,
Georg Goerg
abstract/pdf/supplementary
107, Discriminatively Activated Sparselets,
Ross Girshick; Hyun Oh Song; Trevor Darrell
abstract/pdf/supplementary
458, Multi-View Clustering and Feature Learning via Structured Sparsity,
Hua Wang; Feiping Nie; Heng Huang
abstract/pdf
126, Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation,
Boqing Gong; Kristen Grauman; Fei Sha
abstract/pdf
Spotlight Presentations:
1202, On Nonlinear Generalization of Sparse Coding and Dictionary Learning,
Jeffrey Ho; Yuchen Xie; Baba Vemuri
abstract/pdf
891, Feature Multi-Selection among Subjective Features,
Sivan Sabato; Adam Kalai
abstract/pdf
21, Sparsity-Based Generalization Bounds for Predictive Sparse Coding ,
Nishant Mehta; Alexander Gray
abstract/pdf/supplementary
788, A Unified Robust Regression Model for Lasso-like Algorithms,
Wenzhuo Yang; Huan Xu
abstract/pdf
Tuesday, June 18, 10:30 to 12:10
Track C: General SVM and Decision Tree Methods
Session Chair: Rich Caruana
Room: International 1, 2, 3
74, Multi-Class Classification with Maximum Margin Multiple Kernel,
Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh
abstract/pdf/supplementary
397, Top-down particle filtering for Bayesian decision trees,
Balaji Lakshminarayanan; Daniel Roy; Yee Whye Teh
abstract/pdf/supplementary
77, Cost-Sensitive Tree of Classifiers,
Zhixiang Xu; Matt Kusner; Kilian Weinberger; Minmin Chen
abstract/pdf
Spotlight Presentations:
794, On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance,
Aditya Menon; Harikrishna Narasimhan; Shivani Agarwal; Sanjay Chawla
abstract/pdf/supplementary
790, Quickly Boosting Decision Trees — Pruning Underachieving Features Early,
Ron Appel; Thomas Fuchs; Piotr Dollar; Pietro Perona
abstract/pdf/supplementary
1185, Tree-Independent Dual-Tree Algorithms,
Ryan Curtin; William March; Parikshit Ram; David Anderson; Alexander Gray; Charles Isbell
abstract/pdf
767, Loss-Proportional Subsampling for Subsequent ERM,
Paul Mineiro; Nikos Karampatziakis
abstract/pdf
961, Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner,
Peng Sun; Jie Zhou
abstract/pdf/supplementary
1157, Safe Screening of Non-Support Vectors in Pathwise SVM Computation,
Kohei Ogawa; Yoshiki Suzuki; Ichiro Takeuchi
abstract/pdf/supplementary
95, Convex formulations of radius-margin based Support Vector Machines,
Huyen Do; Alexandros Kalousis
abstract/pdf
111, The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification,
Ofir Pele; Ben Taskar; Amir Globerson; Michael Werman
abstract/pdf
Tuesday, June 18, 10:30 to 12:10
Track D: Spectral Learning and Tensors
Session Chair: Daniel Hsu
Room: International 6
806, Spectral Learning of Hidden Markov Models from Dynamic and Static Data,
Tzu-Kuo Huang; Jeff Schneider
abstract/pdf
146, Learning Linear Bayesian Networks with Latent Variables, Animashree Anandkumar; Daniel Hsu;
Adel Javanmard; Sham Kakade
abstract/pdf
1018, Spectral Experts for Estimating Mixtures of Linear Regressions,
Arun Tejasvi Chaganty; Percy Liang
abstract/pdf/supplementary
850, On learning parametric-output HMMs, Aryeh Kontorovich;
Boaz Nadler; Roi Weiss
abstract/pdf/supplementary
Spotlight Presentations:
283, Tensor Analyzers, Yichuan Tang; Ruslan Salakhutdinov ;
Geoffrey Hinton
abstract/pdf/supplementary
439, Unfolding Latent Tree Structures using 4th Order Tensors,
Mariya Ishteva; Haesun Park; Le Song
abstract/pdf/supplementary
448, Hierarchical Tensor Decomposition of Latent Tree Graphical Models, Le Song;
Mariya Ishteva; Ankur Parikh; Eric Xing; Haesun Park
abstract/pdf
786, Infinite Positive Semidefinite Tensor Factorization with Application to Music Signal Analysis,
Kazuyoshi Yoshii; Ryota Tomioka; Daichi Mochihashi; Masataka Goto
abstract/pdf/supplementary
Tuesday, June 18, 2:00 to 3:40
Track A: Online Learning 2
Session Chair: Satyen Kale
Room: International 7, 8, 9, 10
Webcast Link
367, Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning,
Odalric-Ambrym Maillard; Phuong Nguyen; Ronald Ortner; Daniil Ryabko
abstract/pdf
89, Combinatorial Multi-Armed Bandit: General Framework, Results and Applications,
Wei Chen; Yajun Wang; Yang Yuan
abstract/pdf/supplementary
387, Dynamical Models and tracking regret in online convex programming,
Eric Hall; Rebecca Willett
abstract/pdf
833, Better Rates for Any Adversarial Deterministic MDPs,
Ofer Dekel; Elad Hazan
abstract/pdf
Spotlight Presentations:
169, Multiple Identifications in Multi-Armed Bandits,
Sebastian Bubeck; Tengyao Wang; Nitin Viswanathan
abstract/pdf
37, Gossip-based distributed stochastic bandit algorithms,
Balazs Szorenyi; Robert Busa-Fekete; Istvan Hegedus; Robert Ormandi; Mark Jelasity; Balazs Kegl
abstract/pdf/supplementary
247, Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method,
Taiji Suzuki
abstract/pdf/supplementary
Tuesday, June 18, 2:00 to 3:40
Track B: Structured Labeling
Session Chair: Simon Lacoste-Julien
Room: International 4, 5
306, Learning from Human-Generated Lists,
Kwang-Sung Jun; Jerry Zhu; Burr Settles; Timothy Rogers
abstract/pdf
346, A Structural SVM Based Approach for Optimizing Partial AUC,
Harikrishna Narasimhan; Shivani Agarwal
abstract/pdf/supplementary
106, A Machine Learning Framework for Programming by Example,
Aditya Menon; Omer Tamuz; Sumit Gulwani; Butler Lampson; Adam Kalai
abstract/pdf
454, Convex Adversarial Collective Classification,
Mohamad Ali Torkamani; Daniel Lowd
abstract/pdf/supplementary
Spotlight Presentations:
954, Learning Convex QP Relaxations for Structured Prediction,
Jeremy Jancsary; Sebastian Nowozin; Carsten Rother
abstract/pdf
117, Fixed-Point Model For Structured Labeling,
Quannan Li; Jingdong Wang; David Wipf; Zhuowen Tu
abstract/pdf
310, A Generalized Kernel Approach to Structured Output Learning,
Hachem Kadri; Mohammad Ghavamzadeh; Philippe Preux
abstract/pdf
1049, Optimizing the F-measure in Multi-label Classification: Plug-in Rule Approach versus Structured Loss Minimization,
Krzysztof Dembczynski; Wojciech Kotlowski; Arkadiusz Jachnik; Willem Waegeman; Eyke Huellermeier
abstract/pdf/
Tuesday, June 18, 2:00 to 3:40
Track C: Dimensionality Reduction
Session Chair: Nina Balcan
Room: International 1, 2, 3
141,Principal Component Analysis on non-Gaussian Dependent Data,
Fang Han; Han Liu
abstract/pdf
1103, Deep Canonical Correlation Analysis,
Galen Andrew; Jeff Bilmes; Raman Arora; Karen Livescu
abstract/pdf
654, Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment,
Billy Chang; Uwe Kruger; Rafal Kustra; Junping Zhang
abstract/pdf/supplementary
408, Vanishing Component Analysis,
Roi Livni; David Lehavi; Sagi Schein; Hila Nachliely; Shai Shalev-Shwartz; Amir Globerson
abstract/pdf
Spotlight Presentations:
1183, Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration,
Volodymyr Kuleshov
abstract/pdf
215, Efficient Dimensionality Reduction for Canonical Correlation Analysis,
Haim Avron; Christos Boutsidis ; Sivan Toledo ; Anastasios Zouzias
abstract/pdf
205, Adaptive Sparsity in Gaussian Graphical Models ,
Eleanor Wong; Suyash Awate; P. Thomas Fletcher
abstract/pdf
357, The Most Generative Maximum Margin Bayesian Networks,
Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf
abstract/pdf/supplementary
Tuesday, June 18, 2:00 to 3:40
Track D: Statistical Methods
Session Chair: Anima Anandkumar
Room: International 6
872, Computation-Risk Tradeoffs for Covariance-Thresholded Regression,
Dinah Shender; John Lafferty
abstract/pdf
769, Scalable Simple Random Sampling and Stratified Sampling,
Xiangrui Meng
abstract/pdf
1015, The lasso, persistence, and cross-validation,
Darren Homrighausen; Daniel McDonald
abstract/pdf
889, Consistency versus Realizable H-Consistency for Multiclass Classification,
Phil Long; Rocco Servedio
abstract/pdf/supplementary
Spotlight Presentations:
738, Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy,
Jean Honorio; Jaakkola Tommi
abstract/pdf
1137, Scale Invariant Conditional Dependence Measures,
Sashank J Reddi; Barnabas Poczos
abstract/pdf/supplementary
865, Infinite Markov-Switching Maximum Entropy Discrimination Machines,
Sotirios Chatzis
abstract/pdf
1020, Distribution to Distribution Regression,
Junier Oliva; Barnabas Poczos; Jeff Schneider
abstract/pdf/supplementary
Tuesday, June 18, 4:00 to 5:40
Track A: Nearest Neighbor and Metric Learning
Session Chair: Brian Kulis
Room: International 7, 8, 9, 10
Webcast Link
746, Entropic Affinities: Properties and Efficient Numerical Computation,
Max Vladymyrov; Miguel Carreira-Perpinan
abstract/pdf/supplementary
86, Learning Hash Functions Using Column Generation,
Xi Li; Guosheng Lin; Chunhua Shen; Anton van den Hengel; Anthony Dick
abstract/pdf
415, Robust Structural Metric Learning,
Daryl Lim; Gert Lanckriet; Brian McFee
abstract/pdf
785, Revisiting the Nystrom method for improved large-scale machine learning,
Alex Gittens; Michael Mahoney
abstract/pdf
Spotlight Presentations:
743, That was fast! Speeding up NN search of high dimensional distributions,
Emanuele Coviello; Adeel Mumtaz; Antoni Chan; Gert Lanckriet
abstract/pdf/supplementary
339, Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning,
Daniel Tarlow; Kevin Swersky; Ilya Sutskever; Laurent Charlin; Rich Zemel
abstract/pdf/supplementary
1126, Predictable Dual-View Hashing,
Mohammad Rastegari; Jonghyun Choi; Shobeir Fakhraei; Daume Hal; Larry Davis
abstract/pdf
526, A unifying framework for vector-valued manifold regularization and multi-view learning, Minh Ha Quang; Loris Bazzani; Vittorio Murino
abstract/pdf/supplementary
Tuesday, June 18, 4:00 to 5:40
Track B: General Methods
Session Chair: Shai Ben-David
International 4, 5
183, Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model,
Min Xiao; Yuhong Guo
abstract/pdf
186, Maximum Variance Correction with Application to A* Search,
Wenlin Chen; Kilian Weinberger; Yixin Chen
abstract/pdf
270, Learning with Marginalized Corrupted Features,
Laurens van der Maaten; Minmin Chen; Stephen Tyree; Kilian Weinberger
abstract/pdf
Spotlight Presentations:
285, Scaling Multidimensional Gaussian Processes using Projected Additive Approximations,
Elad Gilboa; Yunus Saatci; John Cunningham
abstract/pdf/supplementary
1131, Nonparametric Mixture of Gaussian Processes with Constraints,
James Ross; Jennifer Dy
abstract/pdf
967, Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models,
Mohammad Emtiyaz Khan; Aleksandr Aravkin; Michael Friedlander; Matthias Seeger
abstract/pdf
484, Gaussian Process Vine Copulas for Multivariate Dependence,
David Lopez-Paz; Jose Miguel Hernandez-Lobato; Ghahramani Zoubin
abstract/pdf
1060, Structure Discovery in Nonparametric Regression through Compositional Kernel Search,
David Duvenaud; James Lloyd; Roger Grosse; Joshua Tenenbaum; Ghahramani Zoubin
abstract/pdf
594, Sequential Bayesian Search,
Zheng Wen; Branislav Kveton; Brian Eriksson; Sandilya Bhamidipati
abstract/pdf/supplementary
923, Kernelized Bayesian Matrix Factorization,
Mehmet Gönen; Suleiman Khan; Samuel Kaski
abstract/pdf/supplementary
583, SADA: A General Framework to Support Robust Causation Discovery,
Ruichu Cai; Zhenjie Zhang; Zhifeng Hao
abstract/pdf
Tuesday, June 18, 4:00 to 5:40
Track C: Transfer Learning
Session Chair: Tobias Scheffer
Room: International 1, 2, 3
13, Domain Generalization via Invariant Feature Representation,
Krikamol Muandet; David Balduzzi, ; Bernhard Schoelkopf
abstract/pdf/supplementary
868, A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers,
Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant
abstract/pdf/supplementary
657, Sparse coding for multitask and transfer learning,
Andreas Maurer; Massi Pontil; Bernardino Romera-Paredes
abstract/pdf/supplementary
91, Bayesian Games for Adversarial Regression Problems,
Michael Groáhans; Christoph Sawade; Michael Brückner; Tobias Scheffer
abstract/pdf/supplementary
Spotlight Presentations:
372, Joint Transfer and Batch-mode Active Learning,
Rita Chattopadhyay; Wei Fan; Ian Davidson; Sethuraman Panchanathan; Jieping Ye
abstract/pdf/supplementary
1187, Multilinear Multitask Learning,
Bernardino Romera-Paredes; Hane Aung; Nadia Bianchi-Berthouze; Massimiliano Pontil
abstract/pdf/supplementary
965, Stability and Hypothesis Transfer Learning,
Ilja Kuzborskij; Francesco Orabona
abstract/pdf
695, Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model,
Ming Yang; Li Yingming; Zhang Zhongfei (Mark)
abstract/pdf
Tuesday, June 18, 4:00 to 5:40
Track D: Statistical Learning and Inference
Session Chair: Nati Srebro
Room: International 6
360, Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs,
Sesh Kumar K. S.; Francis Bach
abstract/pdf/supplementary
760, ∝ SVM for Learning with Label Proportions,
Felix Yu; Dong Liu; Sanjiv Kumar; Jebara Tony; Shih-Fu Chang
abstract/pdf/supplementary
1105, Consistency of Online Random Forests,
Misha Denil; David Matheson; De Freitas Nando
abstract/pdf/supplementary
Spotlight Presentations:
259, Inference algorithms for pattern-based CRFs on sequence data,
Rustem Takhanov; Vladimir Kolmogorov
abstract/pdf
389, Relaxed expectation propagation based on l1-penalized KL minimization,
Yuan Qi; Yandong Guo
abstract/pdf/supplementary
324, A Fast and Exact Energy Minimization Algorithm for Cycle MRFs,
Huayan Wang; Koller Daphne
abstract/pdf/supplementary
571, Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing, Huayan Wang; Koller Daphne
abstract/pdf/supplementary
993, Approximate Inference in Collective Graphical Models,
Daniel Sheldon; Tao Sun; Akshat Kumar; Tom Dietterich
abstract/pdf
628, An Adaptive Learning Rate for Stochastic Variational Inference,
Rajesh Ranganath; Chong Wang; Blei David; Eric Xing
abstract/pdf/supplementary
1101, The Bigraphical Lasso,
Alfredo Kalaitzis; John Lafferty; Neil Lawrence
abstract/pdf/supplementary
1030, Anytime Representation Learning,
Zhixiang Xu; Matt Kusner; Gao Huang; Kilian Weinberger
abstract/pdf
Tuesday, June 18, 6:00 to 7:00
Annual IMLS Business Meeting
Room: International 7, 8, 9, 10
Tuesday, June 18, 7:30 to 8:00
Poster Setup
Room: Skyline, 10th Floor