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ORCID31st ICML 2014: Beijing, China
- Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014. JMLR Workshop and Conference Proceedings 32, JMLR.org 2014
Cycle 1 Papers
- Rajhans Samdani, Kai-Wei Chang, Dan Roth:
A Discriminative Latent Variable Model for Online Clustering. 1-9 - Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. 10-18 - Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. 19-27 - Zongzhang Zhang, David Hsu, Wee Sun Lee:
Covering Number for Efficient Heuristic-based POMDP Planning. 28-36 - Wenzhuo Yang, Melvyn Sim, Huan Xu:
The Coherent Loss Function for Classification. 37-45 - Wenliang Zhong, James Tin-Yau Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. 46-54 - Yuxin Chen, Hiroaki Shioi, Cesar Fuentes Montesinos, Lian Pin Koh, Serge A. Wich, Andreas Krause:
Active Detection via Adaptive Submodularity. 55-63 - Shai Shalev-Shwartz, Tong Zhang:
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. 64-72 - Qihang Lin, Lin Xiao:
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization. 73-81 - Pedro H. O. Pinheiro, Ronan Collobert:
Recurrent Convolutional Neural Networks for Scene Labeling. 82-90 - Ping Ma, Michael W. Mahoney, Bin Yu:
A Statistical Perspective on Algorithmic Leveraging. 91-99 - Aditya Gopalan, Shie Mannor, Yishay Mansour:
Thompson Sampling for Complex Online Problems. 100-108 - Souhaib Ben Taieb, Rob J. Hyndman:
Boosting multi-step autoregressive forecasts. 109-117 - Arun Rajkumar, Shivani Agarwal:
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data. 118-126 - Timothy A. Mann, Shie Mannor:
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations. 127-135 - Odalric-Ambrym Maillard, Shie Mannor:
Latent Bandits. 136-144 - Trung V. Nguyen, Edwin V. Bonilla:
Fast Allocation of Gaussian Process Experts. 145-153 - Siddharth Gopal, Yiming Yang:
Von Mises-Fisher Clustering Models. 154-162 - Frédéric Chazal, Marc Glisse, Catherine Labruère, Bertrand Michel:
Convergence rates for persistence diagram estimation in Topological Data Analysis. 163-171 - Fabian Gieseke, Justin Heinermann, Cosmin E. Oancea, Christian Igel:
Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. 172-180 - Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. 181-189 - Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei, Ming Zhang:
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis. 190-198 - Maxim Rabinovich, David M. Blei:
The Inverse Regression Topic Model. 199-207 - Stanley H. Chan, Edoardo M. Airoldi:
A Consistent Histogram Estimator for Exchangeable Graph Models. 208-216 - Benjamin Letham, Wei Sun, Anshul Sheopuri:
Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data. 217-225 - Haipeng Luo, Robert E. Schapire:
Towards Minimax Online Learning with Unknown Time Horizon. 226-234 - Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry:
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. 235-243 - Aaditya Ramdas, Javier Peña:
Margins, Kernels and Non-linear Smoothed Perceptrons. 244-252 - Shike Mei, Jun Zhu, Jerry Zhu:
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models. 253-261 - Mehryar Mohri, Andres Muñoz Medina:
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve. 262-270 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Low-density Parity Constraints for Hashing-Based Discrete Integration. 271-279 - Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori:
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. 280-287 - Tien-Vu Nguyen, Dinh Quoc Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Bui:
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts. 288-296 - Rémi Lajugie, Francis R. Bach, Sylvain Arlot:
Large-Margin Metric Learning for Constrained Partitioning Problems. 297-305 - Justin Solomon, Raif M. Rustamov, Leonidas J. Guibas, Adrian Butscher:
Wasserstein Propagation for Semi-Supervised Learning. 306-314 - Aonan Zhang, Jun Zhu, Bo Zhang:
Max-Margin Infinite Hidden Markov Models. 315-323 - Yong Liu, Shali Jiang, Shizhong Liao:
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. 324-332 - Shashank Singh, Barnabás Póczos:
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation. 333-341 - Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:
Boosting with Online Binary Learners for the Multiclass Bandit Problem. 342-350 - Tasuku Soma, Naonori Kakimura, Kazuhiro Inaba, Ken-ichi Kawarabayashi:
Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm. 351-359 - Hossein Azari Soufiani, David C. Parkes, Lirong Xia:
Computing Parametric Ranking Models via Rank-Breaking. 360-368 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade:
Tracking Adversarial Targets. 369-377 - Tianlin Shi, Jun Zhu:
Online Bayesian Passive-Aggressive Learning. 378-386 - David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller:
Deterministic Policy Gradient Algorithms. 387-395 - Wenzhao Lian, Vinayak A. Rao, Brian Eriksson, Lawrence Carin:
Modeling Correlated Arrival Events with Latent Semi-Markov Processes. 396-404 - Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. 405-413 - Ferdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler:
Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost. 414-422 - Chun-Liang Li, Hsuan-Tien Lin:
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. 423-431 - Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola:
On Measure Concentration of Random Maximum A-Posteriori Perturbations. 432-440 - Philip Thomas:
Bias in Natural Actor-Critic Algorithms. 441-448 - François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. 449-457 - Zhixing Li, Siqiang Wen, Juanzi Li, Peng Zhang, Jie Tang:
On Modelling Non-linear Topical Dependencies. 458-466 - Benigno Uria, Iain Murray, Hugo Larochelle:
A Deep and Tractable Density Estimator. 467-475 - Prateek Jain, Abhradeep Guha Thakurta:
(Near) Dimension Independent Risk Bounds for Differentially Private Learning. 476-484 - Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. 485-493 - Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. 494-502 - Ji Liu, Jieping Ye, Ryohei Fujimaki:
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint. 503-511 - Travis Dick, András György, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Changing Cost Sequences. 512-520 - Richard Combes, Alexandre Proutière:
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms. 521-529 - Arun Shankar Iyer, J. Saketha Nath, Sunita Sarawagi:
Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection. 530-538 - Azadeh Khaleghi, Daniil Ryabko:
Asymptotically consistent estimation of the number of change points in highly dependent time series. 539-547 - Uri Shalit, Gal Chechik:
Coordinate-descent for learning orthogonal matrices through Givens rotations. 548-556 - Anshumali Shrivastava, Ping Li:
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. 557-565 - Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. 566-574 - Cho-Jui Hsieh, Peder A. Olsen:
Nuclear Norm Minimization via Active Subspace Selection. 575-583 - Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. 584-592 - Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit S. Dhillon:
Large-scale Multi-label Learning with Missing Labels. 593-601 - Rashish Tandon, Pradeep Ravikumar:
Learning Graphs with a Few Hubs. 602-610 - Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle:
Agnostic Bayesian Learning of Ensembles. 611-619 - Samaneh Azadi, Suvrit Sra:
Towards an optimal stochastic alternating direction method of multipliers. 620-628 - Shiwei Lan, Bo Zhou, Babak Shahbaba:
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions. 629-637 - Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté:
Efficient Continuous-Time Markov Chain Estimation. 638-646 - Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman
, Ning Zhang, Eric Tzeng, Trevor Darrell:
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. 647-655 - Dani Yogatama, Noah A. Smith:
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers. 656-664 - Misha Denil, David Matheson, Nando de Freitas:
Narrowing the Gap: Random Forests In Theory and In Practice. 665-673 - Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. 674-682 - David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Admixture of Poisson MRFs: A Topic Model with Word Dependencies. 683-691 - Harm van Seijen, Richard S. Sutton:
True Online TD(lambda). 692-700 - Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. 701-709 - Amirmohammad Rooshenas, Daniel Lowd:
Learning Sum-Product Networks with Direct and Indirect Variable Interactions. 710-718 - Jascha Sohl-Dickstein, Mayur Mudigonda, Michael Robert DeWeese:
Hamiltonian Monte Carlo Without Detailed Balance. 719-726 - Jacob Steinhardt, Percy Liang:
Filtering with Abstract Particles. 727-735 - Taiji Suzuki:
Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers. 736-744 - Jian Zhou, Olga G. Troyanskaya:
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction. 745-753 - Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An Efficient Approach for Assessing Hyperparameter Importance. 754-762
Cycle 2 Papers
- Ke Sun, Stéphane Marchand-Maillet:
An Information Geometry of Statistical Manifold Learning. 1-9 - Masrour Zoghi, Shimon Whiteson, Rémi Munos, Maarten de Rijke:
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem. 10-18 - Raffay Hamid, Ying Xiao, Alex Gittens, Dennis DeCoste:
Compact Random Feature Maps. 19-27 - Aryeh Kontorovich:
Concentration in unbounded metric spaces and algorithmic stability. 28-36 - Daniel J. Hsu, Sivan Sabato:
Heavy-tailed regression with a generalized median-of-means. 37-45 - Michal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák:
Spectral Bandits for Smooth Graph Functions. 46-54 - Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang:
Robust Principal Component Analysis with Complex Noise. 55-63 - Qi-Xing Huang, Yuxin Chen, Leonidas J. Guibas:
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation. 64-72 - Cun Mu, Bo Huang, John Wright, Donald Goldfarb:
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery. 73-81 - Sanmay Das, Allen Lavoie:
Automated inference of point of view from user interactions in collective intelligence venues. 82-90 - Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye:
Rank-One Matrix Pursuit for Matrix Completion. 91-99 - Yuxin Chen, Leonidas J. Guibas, Qi-Xing Huang:
Near-Optimal Joint Object Matching via Convex Relaxation. 100-108 - Dmitry Malioutov, Nikolai Slavov:
Convex Total Least Squares. 109-117 - Pratik Jawanpuria, Manik Varma, J. Saketha Nath:
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection. 118-126 - Xiaotong Yuan, Ping Li, Tong Zhang:
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization. 127-135 - Jean Honorio, Tommi S. Jaakkola:
A Unified Framework for Consistency of Regularized Loss Minimizers. 136-144 - Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye:
Geodesic Distance Function Learning via Heat Flow on Vector Fields. 145-153 - Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause:
Near-Optimally Teaching the Crowd to Classify. 154-162 - Walid Krichene, Benjamin Drighès, Alexandre M. Bayen:
On the convergence of no-regret learning in selfish routing. 163-171 - Jérémie Mary, Philippe Preux, Olivier Nicol:
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques. 172-180 - Aviv Tamar, Shie Mannor, Huan Xu:
Scaling Up Robust MDPs using Function Approximation. 181-189 - Wei Ping, Qiang Liu, Alexander Ihler:
Marginal Structured SVM with Hidden Variables. 190-198 - Yariv Dror Mizrahi, Misha Denil, Nando de Freitas:
Linear and Parallel Learning of Markov Random Fields. 199-207 - Yarin Gal, Zoubin Ghahramani:
Pitfalls in the use of Parallel Inference for the Dirichlet Process. 208-216 - Yuan Zhou, Xi Chen, Jian Li:
Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing. 217-225 - Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski:
Deep Generative Stochastic Networks Trainable by Backprop. 226-234 - Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye:
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models. 235-243 - Yudong Chen, Jiaming Xu:
Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting. 244-252 - Emile Contal, Vianney Perchet, Nicolas Vayatis:
Gaussian Process Optimization with Mutual Information. 253-261 - Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek:
Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. 262-270 - Mathias Niepert, Pedro M. Domingos:
Exchangeable Variable Models. 271-279 - Shai Ben-David, Nika Haghtalab:
Clustering in the Presence of Background Noise. 280-288 - Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye:
Safe Screening with Variational Inequalities and Its Application to Lasso. 289-297 - Shan-Hung Wu, Hao-Heng Chien, Kuan-Hua Lin, Philip S. Yu:
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks. 298-306 - Joan Bruna Estrach, Arthur Szlam, Yann LeCun:
Signal recovery from Pooling Representations. 307-315 - Emma Brunskill, Lihong Li:
PAC-inspired Option Discovery in Lifelong Reinforcement Learning. 316-324 - Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang:
Multi-label Classification via Feature-aware Implicit Label Space Encoding. 325-333 - Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani:
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications. 334-342 - Stéphan Clémençon, Sylvain Robbiano:
Anomaly Ranking as Supervised Bipartite Ranking. 343-351 - Gunnar E. Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra:
Hierarchical Quasi-Clustering Methods for Asymmetric Networks. 352-360 - Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Rectangular Tiling Process. 361-369 - Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. 370-378 - José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. 379-387 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for High-Dimensional Linear Regression. 388-396 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. 397-405 - Yuan Fang, Kevin Chen-Chuan Chang, Hady Wirawan Lauw:
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. 406-414 - Chengtao Li, Jun Zhu, Jianfei Chen:
Bayesian Max-margin Multi-Task Learning with Data Augmentation. 415-423