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ORCID32nd ICML 2015: Lille, France
- Francis R. Bach, David M. Blei:
Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. JMLR Workshop and Conference Proceedings 37, JMLR.org 2015 - Peilin Zhao, Tong Zhang:
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization. 1-9 - Nihar B. Shah, Dengyong Zhou, Yuval Peres:
Approval Voting and Incentives in Crowdsourcing. 10-19 - Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko:
A low variance consistent test of relative dependency. 20-29 - Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock:
An Aligned Subtree Kernel for Weighted Graphs. 30-39 - Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens:
Spectral Clustering via the Power Method - Provably. 40-48 - Ke Sun, Jun Wang, Alexandros Kalousis, Stéphane Marchand-Maillet:
Information Geometry and Minimum Description Length Networks. 49-58 - Jean-Baptiste Tristan, Joseph Tassarotti, Guy L. Steele Jr.:
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation. 59-68 - Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li:
Adaptive Stochastic Alternating Direction Method of Multipliers. 69-77 - Alekh Agarwal, Léon Bottou:
A Lower Bound for the Optimization of Finite Sums. 78-86 - Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah A. Smith:
Learning Word Representations with Hierarchical Sparse Coding. 87-96 - Mingsheng Long
, Yue Cao, Jianmin Wang, Michael I. Jordan:
Learning Transferable Features with Deep Adaptation Networks. 97-105 - Takayuki Osogami:
Robust partially observable Markov decision process. 106-115 - Han Zhao, Mazen Melibari, Pascal Poupart:
On the Relationship between Sum-Product Networks and Bayesian Networks. 116-124 - Aditya Krishna Menon, Brendan van Rooyen, Cheng Soon Ong, Bob Williamson:
Learning from Corrupted Binary Labels via Class-Probability Estimation. 125-134 - Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection. 135-143 - Ohad Shamir:
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate. 144-152 - Doron Kukliansky, Ohad Shamir:
Attribute Efficient Linear Regression with Distribution-Dependent Sampling. 153-161 - Ethan Fetaya, Shimon Ullman:
Learning Local Invariant Mahalanobis Distances. 162-168 - Zhuang Ma, Yichao Lu, Dean P. Foster:
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis. 169-178 - Nan Jiang, Alex Kulesza, Satinder Singh:
Abstraction Selection in Model-based Reinforcement Learning. 179-188 - Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Surrogate Functions for Maximizing Precision at the Top. 189-198 - Harikrishna Narasimhan, Purushottam Kar, Prateek Jain:
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. 199-208 - Olivier Bachem, Mario Lucic, Andreas Krause:
Coresets for Nonparametric Estimation - the Case of DP-Means. 209-217 - Pratik Gajane, Tanguy Urvoy, Fabrice Clérot:
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits. 218-227 - Mohammad Taha Bahadori, David C. Kale, Yingying Fan, Yan Liu:
Functional Subspace Clustering with Application to Time Series. 228-237 - Rose Yu, Dehua Cheng, Yan Liu:
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams. 238-247 - Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté:
Atomic Spatial Processes. 248-256 - Elad Hazan, Roi Livni, Yishay Mansour:
Classification with Low Rank and Missing Data. 257-266 - Oran Richman, Shie Mannor:
Dynamic Sensing: Better Classification under Acquisition Constraints. 267-275 - Pinghua Gong, Jieping Ye:
A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis. 276-284 - Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. 285-294 - Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. 295-304 - Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
Theory of Dual-sparse Regularized Randomized Reduction. 305-314 - Ambuj Tewari, Sougata Chaudhuri:
Generalization error bounds for learning to rank: Does the length of document lists matter? 315-323 - Toby Hocking, Guillem Rigaill, Guillaume Bourque:
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data. 324-332 - Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Mind the duality gap: safer rules for the Lasso. 333-342 - Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan:
A General Analysis of the Convergence of ADMM. 343-352 - Yuchen Zhang, Xiao Lin:
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization. 353-361 - Yuchen Zhang, Xiao Lin:
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss. 362-370 - Yuxin Chen, Changho Suh:
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons. 371-380 - Stephen H. Bach, Bert Huang, Jordan L. Boyd-Graber, Lise Getoor:
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. 381-390 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed:
Structural Maxent Models. 391-399 - Debarghya Ghoshdastidar, Ambedkar Dukkipati:
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning. 400-409 - Ben London, Bert Huang, Lise Getoor:
The Benefits of Learning with Strongly Convex Approximate Inference. 410-418 - Bo Xin, David P. Wipf:
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA. 419-427 - Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi:
Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View. 428-437 - Katharina Blechschmidt, Joachim Giesen, Sören Laue:
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains. 438-447 - Sergey Ioffe, Christian Szegedy:
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 448-456 - Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. 457-465 - Dawen Liang, John W. Paisley:
Landmarking Manifolds with Gaussian Processes. 466-474 - Aonan Zhang, John W. Paisley:
Markov Mixed Membership Models. 475-483 - Wenzhuo Yang, Huan Xu:
A Unified Framework for Outlier-Robust PCA-like Algorithms. 484-493 - Wenzhuo Yang, Huan Xu:
Streaming Sparse Principal Component Analysis. 494-503 - Wenzhuo Yang, Huan Xu:
A Divide and Conquer Framework for Distributed Graph Clustering. 504-513 - Senjian An, Farid Boussaïd, Mohammed Bennamoun:
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? 514-523 - K. Lakshmanan, Ronald Ortner, Daniil Ryabko:
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning. 524-532 - Michael Betancourt:
The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling. 533-540 - Dan Garber, Elad Hazan:
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets. 541-549 - Mrinal Kanti Das, Trapit Bansal, Chiranjib Bhattacharyya:
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models. 550-559 - Dan Garber, Elad Hazan, Tengyu Ma:
Online Learning of Eigenvectors. 560-568 - Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. 569-578 - Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz:
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup. 579-587 - Seppo Virtanen, Mark A. Girolami:
Ordinal Mixed Membership Models. 588-596 - Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han:
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network. 597-606 - Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola:
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. 607-616 - Garvesh Raskutti, Michael W. Mahoney:
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. 617-625 - Nathaniel Korda, Prashanth L. A.:
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence. 626-634 - Roi Weiss, Boaz Nadler:
Learning Parametric-Output HMMs with Two Aliased States. 635-644 - Yarin Gal, Yutian Chen, Zoubin Ghahramani:
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. 645-654 - Yarin Gal, Richard E. Turner:
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. 655-664 - Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal:
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top. 665-673 - Dominik Csiba, Zheng Qu, Peter Richtárik:
Stochastic Dual Coordinate Ascent with Adaptive Probabilities. 674-683 - Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar:
Vector-Space Markov Random Fields via Exponential Families. 684-692 - Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. 693-701 - Shashanka Ubaru, Arya Mazumdar, Yousef Saad:
Low Rank Approximation using Error Correcting Coding Matrices. 702-710 - Assaf Hallak, François Schnitzler, Timothy A. Mann, Shie Mannor:
Off-policy Model-based Learning under Unknown Factored Dynamics. 711-719 - Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen:
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification. 720-729 - Melih Kandemir:
Asymmetric Transfer Learning with Deep Gaussian Processes. 730-738 - Rongda Zhu, Quanquan Gu:
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing. 739-747 - Stephan Gouws, Yoshua Bengio, Greg Corrado:
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments. 748-756 - Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi:
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization. 757-766 - Branislav Kveton, Csaba Szepesvári, Zheng Wen, Azin Ashkan:
Cascading Bandits: Learning to Rank in the Cascade Model. 767-776 - James R. Foulds, Shachi H. Kumar, Lise Getoor:
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. 777-786 - Alina Ene, Huy L. Nguyen:
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions. 787-795 - Karthik S. Narayan, Ali Punjani, Pieter Abbeel:
Alpha-Beta Divergences Discover Micro and Macro Structures in Data. 796-804 - Johannes Heinrich, Marc Lanctot, David Silver:
Fictitious Self-Play in Extensive-Form Games. 805-813 - Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. 814-823 - Walid Krichene, Maximilian Balandat, Claire J. Tomlin, Alexandre M. Bayen:
The Hedge Algorithm on a Continuum. 824-832 - David Belanger, Sham M. Kakade:
A Linear Dynamical System Model for Text. 833-842 - Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov:
Unsupervised Learning of Video Representations using LSTMs. 843-852 - Tao Sun, Daniel Sheldon, Akshat Kumar:
Message Passing for Collective Graphical Models. 853-861 - Yining Wang, Jun Zhu:
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics. 862-870 - Xinran He, Theodoros Rekatsinas, James R. Foulds, Lise Getoor, Yan Liu:
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. 871-880 - Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle:
MADE: Masked Autoencoder for Distribution Estimation. 881-889 - Yuanbin Wu, Shiliang Sun:
An Online Learning Algorithm for Bilinear Models. 890-898 - Georgios Papachristoudis, John W. Fisher III:
Adaptive Belief Propagation. 899-907 - Insu Han, Dmitry Malioutov, Jinwoo Shin:
Large-scale log-determinant computation through stochastic Chebyshev expansions. 908-917 - Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger:
Differentially Private Bayesian Optimization. 918-927 - Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A Nearly-Linear Time Framework for Graph-Structured Sparsity. 928-937 - Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li:
Support Matrix Machines. 938-947 - Richard Nock, Giorgio Patrini, Arik Friedman:
Rademacher Observations, Private Data, and Boosting. 948-956 - Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger:
From Word Embeddings To Document Distances. 957-966 - Matthew Taddy, Chun-Sheng Chen, Jun Yu, Mitch Wyle:
Bayesian and Empirical Bayesian Forests. 967-976 - Jean Pouget-Abadie, Thibaut Horel:
Inferring Graphs from Cascades: A Sparse Recovery Framework. 977-986 - Ching-Pei Lee, Dan Roth:
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM. 987-996 - Yanan Sui, Alkis Gotovos, Joel W. Burdick, Andreas Krause:
Safe Exploration for Optimization with Gaussian Processes. 997-1005 - Avrim Blum, Moritz Hardt:
The Ladder: A Reliable Leaderboard for Machine Learning Competitions. 1006-1014 - Maurizio Filippone, Raphael Engler:
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). 1015-1024 - Roman Garnett, Shirley Ho, Jeff G. Schneider:
Finding Galaxies in the Shadows of Quasars with Gaussian Processes. 1025-1033 - Alon Cohen, Tamir Hazan:
Following the Perturbed Leader for Online Structured Learning. 1034-1042 - Jacob Steinhardt, Percy Liang:
Reified Context Models. 1043-1052 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek:
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing. 1053-1062 - Jacob Steinhardt, Percy Liang:
Learning Fast-Mixing Models for Structured Prediction. 1063-1072 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani:
A Probabilistic Model for Dirty Multi-task Feature Selection. 1073-1082 - Weiran Wang, Raman Arora, Karen Livescu, Jeff A. Bilmes:
On Deep Multi-View Representation Learning. 1083-1092 - Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas J. Guibas:
Learning Program Embeddings to Propagate Feedback on Student Code. 1093-1102 - Qiang Zhou, Qi Zhao:
Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems. 1103-1112 - Zheng Wen, Branislav Kveton, Azin Ashkan:
Efficient Learning in Large-Scale Combinatorial Semi-Bandits. 1113-1122 - Andre Manoel, Florent Krzakala, Eric W. Tramel, Lenka Zdeborová:
Swept Approximate Message Passing for Sparse Estimation. 1123-1132 - Alexandra Carpentier, Michal Valko:
Simple regret for infinitely many armed bandits. 1133-1141 - Wei-Lun Chao, Justin Solomon, Dominik L. Michels, Fei Sha:
Exponential Integration for Hamiltonian Monte Carlo. 1142-1151 - Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays. 1152-1161 - Mike Izbicki, Christian R. Shelton:
Faster cover trees. 1162-1170 - Tyler B. Johnson, Carlos Guestrin:
Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization. 1171-1179 - Yaroslav Ganin, Victor S. Lempitsky:
Unsupervised Domain Adaptation by Backpropagation. 1180-1189 - Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu:
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer. 1190-1198 - Hyunwoo J. Kim, Jia Xu, Baba C. Vemuri, Vikas Singh:
Manifold-valued Dirichlet Processes. 1199-1208 - Yu Wang, David P. Wipf, Qing Ling, Wei Chen, Ian J. Wassell:
Multi-Task Learning for Subspace Segmentation. 1209-1217 - Tim Salimans, Diederik P. Kingma, Max Welling:
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. 1218-1226 - Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka:
Scalable Model Selection for Large-Scale Factorial Relational Models. 1227-1235 - Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen, Justin Ward:
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets. 1236-1244