ICML Videos Link
8:30 to 10:00,
Keynote Speaker: Vincent Vanhoucke
Wednesday, June 19, 10:30 to 12:10
Track A: Invited Orals
Session Chair: Michael Littman
Room: International 7, 8, 9, 10
Webcast Link
Classic Paper Prize Talk: Semi-supervised learning using gaussian fields and harmonic functions, ICML 2003, by Xiaojin (Jerry) Zhu, Zoubin Ghahramani, and John Lafferty
Classic Paper Prize Talk: Online convex programming and generalized infinitesimal gradient ascent, ICML 2003, by Martin Zinkevich
What do we learn from Kaggle competitions?,
Ben Hamner
abstract
394, Large-Scale Bandit Problems and KWIK Learning,
Jacob Abernethy; Kareem Amin; Michael Kearns; Moez Draief
abstract/pdf/supplementary
Wednesday, June 19, 10:30 to 12:10
Track B: Optimization
Session Chair: Fei Sha
Room: International 4, 5
48, Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes,
Ohad Shamir; Tong Zhang
abstract/pdf
232, Optimal rates for stochastic convex optimization under Tsybakov noise condition,
Aaditya Ramdas; Aarti Singh
abstract/pdf/supplementary
918, Fast Semidifferential-based Submodular Function Optimization,
Rishabh Iyer; Stefanie Jegelka; Jeff Bilmes
abstract/pdf/supplementary
612, A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions,
Quoc Tran Dinh; Anastasios Kyrillidis; Volkan Cevher
abstract/pdf/supplementary
Spotlight Presentations:
1008, Mini-Batch Primal and Dual Methods for SVMs,
Martin Takac; Avleen Bijral; Peter Richtarik; Nati Srebro
abstract/pdf/supplementary
53, Stochastic Alternating Direction Method of Multipliers,
Hua Ouyang; Niao He; Long Tran; Alexander Gray
abstract/pdf/supplementary
884, Optimization with First-Order Surrogate Functions,
Julien Mairal
abstract/pdf/supplementary
41, Fast Probabilistic Optimization from Noisy Gradients,
Philipp Hennig
abstract/pdf
Wednesday, June 19, 10:30 to 12:10
Track C: Clustering
Session Chair: Maya Gupta
Room: International 1, 2, 3
691, A Local Algorithm for Finding Well-Connected Clusters,
Silvio Lattanzi; Vahab Mirrokni; Zeyuan Allen Zhu
abstract/pdf
550, Monochromatic Bi-Clustering ,
Sharon Wulff; Ruth Urner; Shai Ben-David
abstract/pdf/supplementary
424, Constrained fractional set programs and their application in local clustering and community detection,
Thomas Bühler; Shyam Sundar Rangapuram; Simon Setzer; Matthias Hein
abstract/pdf/supplementary
987, Breaking the Small Cluster Barrier of Graph Clustering,
Nir Ailon; Yudong Chen; Huan Xu
abstract/pdf/supplementary
Spotlight Presentations:
555, Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings,
Nicola Rebagliati
abstract/pdf
190, Clustering and Learning Behaviors using a Sparse Latent Space,
Lui Montesano; Manuel Lopes; Javier Almingol
abstract/pdf/supplementary
545, Precision-recall space to correct external indices for biclustering,
Blaise Hanczar; Mohamed Nadif
abstract/pdf
1171, Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion,
Jinfeng Yi; Rong Jin; Qi Qian; Anil Jain
abstract/pdf
Wednesday, June 19, 10:30 to 12:10
Track D: Learning Theory 1
Session Chair: Phil Long
Room: International 6
644, Margins, Shrinkage and Boosting,
Matus Telgarsky
abstract/pdf/supplementary
849, Sharp Generalization Error Bounds for Randomly-projected Classifiers,
Robert Durrant; Ata Kaban
abstract/pdf
63, Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction,
Sébastien Giguère; Francois Laviolette; Mario Marchand; Khadidja Sylla
abstract/pdf/supplementary
898, Collective Stability and Structured Prediction: Generalization from One Example,
Ben London; Bert Huang; Ben Taskar; Lise Getoor
abstract/pdf/supplementary
Spotlight Presentations:
54, Hierarchical Regularization Cascade for Joint Learning,
Alon Zweig; Daphna Weinshall
abstract/pdf/supplementary
443, Learning Fair Representations,
Rich Zemel; Yu Wu; Kevin Swersky; Toniann Pitassi; Cynthia Dwork
abstract/pdf/supplementary
173, Differentially Private Learning with Kernels,
Prateek Jain; Abhradeep Thakurta
abstract/pdf/supplementary
461, Rounding Methods for Discrete Linear Classification,
Yann Chevaleyre; Frederick Koriche; Jean-Daniel Zucker
abstract/pdf/supplementary
Wednesday, June 19, 2:00 to 3:40
Track A: Dimensionality Reduction and Semi-Supervised Learning
Session Chair: Jerry Zhu
Room: International 7, 8, 9, 10
Webcast Link
27, Squared-loss Mutual Information Regularization,
Gang Niu; Wittawat Jitkrittum; Bo Dai, ; Hirotaka Hachiya; Masashi Sugiyama
abstract/pdf/supplementary
509, Ellipsoidal Multiple Instance Learning,
Gabriel Krummenacher; Cheng Soon Ong; Joachim Buhmann
abstract/pdf/supplementary
933, Infinitesimal Annealing for Training Semi-Supervised Support Vector Machine,
Kohei Ogawa; Motoki Imamura; Ichiro Takeuchi; Masashi Sugiyama
abstract/pdf/supplementary
1108, Sparse Gaussian Conditional Random Fields: Algorithms, and Application to Energy Forecasting,
Matt Wytock; Zico Kolter
abstract/pdf/supplementary
1198, Adaptive Hamiltonian and Riemann Manifold Monte Carlo,
Ziyu Wang; Shakir Mohamed; De Freitas Nando
abstract/pdf
Wednesday, June 19, 2:00 to 3:40
Track B: Optimization and Integration
Session Chair: David Sontag
Room: International 4, 5
487, Stochastic Simultaneous Optimistic Optimization,
Michal Valko; Alexandra Carpentier; Remi Munos
abstract/pdf
36, Block-Coordinate Frank-Wolfe Optimization for Structural SVMs, Simon Lacoste-Julien; Martin Jaggi; Mark Schmidt; Patrick Pletscher
abstract/pdf/supplementary
656, Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization, Stefano Ermon; Carla Gomes; Ashish Sabharwal; Bart Selman
abstract/pdf/supplementary
Spotlight Presentations:
1116, Expensive Function Optimization with Stochastic Binary Outcomes,
Matthew Tesch; Jeff Schneider; Howie Choset
abstract/pdf
1047, O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions,
Lijun Zhang; Tianbao Yang; Rong Jin; Xiaofei He
abstract/pdf/supplementary
275, Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization,
Martin Jaggi
abstract/pdf/supplementary
1036, Algorithms for Direct 0–1 Loss Optimization in Binary Classification,
Tan Nguyen; Scott Sanner
abstract/pdf
488, Toward Optimal Stratification for Stratified Monte-Carlo Integration,
Alexandra Carpentier; Remi Munos
abstract/pdf
Wednesday, June 19, 2:00 to 3:40
Track C: Vision
Session Chair: Kilian Weinberger
Room: International 1, 2, 3
10, An Optimal Policy for Target Localization with Application to Electron Microscopy, Raphael Sznitman; Aurelien Lucchi; Peter Frazier; Bruno Jedynak; Pascal Fua
abstract/pdf
1115, Fast Image Tagging,
Minmin Chen; Alice Zheng; Kilian Weinberger
abstract/pdf
348, An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation,
Nicholas Bryan; Gautham Mysore
abstract/pdf
Spotlight Presentations:
904, Max-Margin Multiple-Instance Dictionary Learning,
Xinggang Wang; Zhuowen Tu
abstract/pdf
761, Parameter Learning and Convergent Inference for Dense Random Fields,
Philipp Kraehenbuehl; Vladlen Koltun
abstract/pdf
418, Can We Recognize Tiger by Bus Images? _ Robust and Discriminative Self-Taught Image Categorization,
Hua Wang; Feiping Nie; Heng Huang
abstract/pdf/supplementary
886, Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Forecasting,
Hema Koppula; Ashutosh Saxena
abstract/pdf
15, A Spectral Learning Approach to Range-Only SLAM, Byron Boots;
Geoff Gordon
abstract/pdf/supplementary
726, Non-Linear Stationary Subspace Analysis with Application to Video Classification,
Mahsa Baktashmotlagh; Mehrtash Harandi; Abbas Bigdeli; Brian Lovell; Mathieu Salzmann
abstract/pdf/supplementary
776, On Compact Codes for Spatially Pooled Features,
Yangqing Jia; Oriol Vinyals; Trevor Darrell
abstract/pdf/supplementary
812, Analogy-preserving Semantic Embedding for Visual Object Categorization,
Sung Ju Hwang; Kristen Grauman; Fei Sha
abstract/pdf
Wednesday, June 19, 2:00 to 3:40
Track D: Learning Theory II
Session Chair: Mehryar Mohri
Room: International 1, 2, 3
1043, Exploiting Ontology Structures and Unlabeled Data for Learning,
Nina Balcan; Avrim Blum; Yishay Mansour
abstract/pdf/supplementary
952, One-Pass AUC Optimization, Wei Gao; Rong Jin; Shenghuo Zhu; Zhi-Hua Zhou
abstract/pdf
16, Near-Optimal Bounds for Cross-Validation via Loss Stability,
Ravi Kumar; Daniel Lokshtanov; Sergei Vassilvitskii; Andrea Vattani,
abstract/pdf
820, Algebraic classifiers: a generic approach to fast cross-validation,parallel training,
Michael Izbicki
abstract/pdf/supplementary
Spotlight Presentations:
1041, Top-k Selection based on Adaptive Sampling of Noisy Preferences,
Robert Busa-Fekete; Weiwei Cheng; Paul Weng; Eyke Huellermeier; Balazs Szorenyi
abstract/pdf/supplementary
319, Enhanced statistical rankings via targeted data collection,
Braxton Osting; Christoph Brune; Stanley Osher
abstract/pdf
138, Efficient Ranking from Pairwise Comparisons,
Fabian Wauthier; Michael Jordan; Nebojsa Jojic
abstract/pdf/supplementary
903, Stable Coactive Learning via Perturbation,
Karthik Raman; Thorsten Joachims,; Pannaga Shivaswamy; Tobias Schnabel
abstract/pdf/supplementary
Wednesday, June 19, 4:00 to 5:40
Track A: Crowd Sourcing and Large Scale Learning
Session Chair: Gert Lanckriet
Room: International 7, 8, 9, 10
Webcast Link
96, Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing,
Xi Chen; Qihang Lin; Dengyong Zhou
abstract/pdf/supplementary
926, Quantile Regression for Large-scale Applications,
Michael Mahoney; Jiyan Yang; Xiangrui Meng
abstract/pdf
621, Distributed training of Large-scale Logistic models,
Siddharth Gopal; Yiming Yang
abstract/pdf
570, Label Partitioning For Sublinear Ranking,
Jason Weston; Ameesh Makadia; Hector Yee
abstract/pdf
Spotlight Presentations:
213, Human Boosting,
Harsh Pareek; Pradeep Ravikumar
abstract/pdf/supplementary
655, Large-Scale Learning with Less RAM via Randomization,
Daniel Golovin; D. Sculley; Brendan McMahan; Michael Young
abstract/pdf
930, Robust Regression on MapReduce,
Michael Mahoney; Xiangrui Meng
abstract/pdf
366, Adaptive Task Assignment for Crowdsourced Classification,
Chien-Ju Ho; Shahin Jabbari; Jennifer Wortman Vaughan
abstract/pdf
Wednesday, June 19, 4:00 to 5:40
Track B: Kernel Methods
Session Chair: Percy Liang
Room: International 4, 5
751, Local Deep Kernel Learning for Efficient Non-linear SVM Prediction,
Cijo Jose; Prasoon Goyal; Parv Aggrwal; Manik Varma
abstract/pdf
361, Fastfood – Computing Hilbert Space Expansions in loglinear time,
Quoc Le; Tamas Sarlos; Alexander Smola
abstract/pdf/supplementary
1063, Smooth Operators and an RKHS Integration Approach,
Steffen Grunewalder; Gretton Arthur; John Shawe-Taylor
abstract/pdf/supplementary
895, Domain Adaptation under Target and Conditional Shift,
Kun Zhang; Bernhard Schoelkopf; Krikamol Muandet; Zhikun Wang
abstract/pdf/supplementary
Spotlight Presentations:
171, Learning Optimally Sparse Support Vector Machines,
Andrew Cotter; Shai Shalev-Shwartz; Nati Srebro
abstract/pdf/supplementary
262, A New Frontier of Kernel Design for Structured Data,
Kilho Shin
abstract/pdf
384 ,Characterizing the Representer Theorem,
Yaoliang Yu; Hao Cheng; Dale Schuurmans; Csaba Szepesvari
abstract/pdf
683, Covariate Shift in Hilber Space: A Solution Via Sorrogate Kernels,
Kai Zhang; Vincent Zheng; QIaojun Wang; James Kwok; Qiang Yang
abstract/pdf
Wednesday, June 19, 4:00 to 5:40
Track C: Matrix Factorization
Session Chair: Rich Zemel
Room: Internationa 1, 2, 3
135, Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization,
Abhishek Kumar; Vikas Sindhwani; Prabhanjan Kambadur
abstract/pdf
278, General Functional Matrix Factorization Using Gradient Boosting,
Tianqi Chen; Hang Li; Qiang Yang; Yong Yu
abstract/pdf
981, Fast Max-Margin Matrix Factorization with Data Augmentation,
Minjie Xu; Jun Zhu; Bo Zhang
abstract/pdf/supplementary
513, Local Low-Rank Matrix Approximation,
Joonseok Lee; Seungyeon Kim; Guy Lebanon; Yoram Singer
abstract/pdf
Spotlight Presentations:
1174, Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models,
Umut Simsekli; Yusuf Kenan Yilmaz; Ali Taylan Cemgil
abstract/pdf
343, ELLA: An Efficient Lifelong Learning Algorithm,
Paul Ruvolo; Eric Eaton
abstract/pdf/supplementary
772, Riemannian Similarity Learning,
Li Cheng
abstract/pdf
1120, Multiple-Source Cross Validation,
Krzysztof Geras; Charles Sutton
abstract/pdf
Wednesday, June 19, 4:00 to 5:40
Track D: Learning Theory 3
Session Chair: Jeff Bilmes
Room: International 6
675, Activized Learning with Uniform Classification Noise,
Liu Yang; Steve Hanneke
abstract/pdf
316, Efficient Active Learning of Halfspaces: an Aggressive Approach,
Alon Gonen; Sivan Sabato; Shai Shalev-Shwartz
abstract/pdf
1091, Selective sampling algorithms for cost-sensitive multiclass prediction,
Alekh Agarwa
abstract/pdf/supplementary
521, Generic Exploration and K-armed Voting Bandits,
Tanguy Urvoy; Fabrice Clerot; Raphael Feraud; Sami Naamane
abstract/pdf/supplementary
Spotlight Presentations:
433, Efficient Semi-supervised and Active Learning of Disjunctions,
Nina Balcan; Christopher Berlind; Steven Ehrlich; Yingyu Liang
abstract/pdf/supplementary
1158, Cost-sensitive Multiclass Classification Risk Bounds,
Bernardo Pires; Csaba Szepesvari; Mohammad Ghavamzadeh
abstract/pdf
308, Active Learning for Multi-Objective Optimization,
Marcela Zuluaga; Guillaume Sergent; Andreas Krause; Markus Pueschel
abstract/pdf/supplementary
90, Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization,
Yuxin Chen; Andreas Krause
abstract/pdf/supplementary
Wednesday, June 19, 6:00 to 8:00
ICML Banquet
Room: International 7, 8, 9, 10
Wednesday, June 19, 7:30 to 8:00
Poster Setup
Room: Skyline, 10th Floor