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33rd ICML 2016: New York City, NY, USA
- Maria-Florina Balcan, Kilian Q. Weinberger:
Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. JMLR Workshop and Conference Proceedings 48, JMLR.org 2016
Accepted Papers
- Nihar B. Shah, Dengyong Zhou:
No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing. 1-10 - Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. 11-20 - Adrian Weller:
Uprooting and Rerooting Graphical Models. 21-29 - Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger:
A Deep Learning Approach to Unsupervised Ensemble Learning. 30-39 - Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov:
Revisiting Semi-Supervised Learning with Graph Embeddings. 40-48 - Chelsea Finn, Sergey Levine, Pieter Abbeel:
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. 49-58 - Pengtao Xie, Jun Zhu, Eric P. Xing:
Diversity-Promoting Bayesian Learning of Latent Variable Models. 59-68 - Kirthevasan Kandasamy, Yaoliang Yu:
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. 69-78 - Young Lee, Kar Wai Lim, Cheng Soon Ong:
Hawkes Processes with Stochastic Excitations. 79-88 - Ashish Khetan, Sewoong Oh:
Data-driven Rank Breaking for Efficient Rank Aggregation. 89-98 - Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder:
Dropout distillation. 99-107 - Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Metadata-conscious anonymous messaging. 108-116 - Ji Liu, Xiaojin Zhu, Hrag Ohannessian:
The Teaching Dimension of Linear Learners. 117-126 - Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
Truthful Univariate Estimators. 127-135 - Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, Venu Govindaraju:
Why Regularized Auto-Encoders learn Sparse Representation? 136-144 - Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen:
k-variates++: more pluses in the k-means++. 145-154 - Jonathan Rosenski, Ohad Shamir, Liran Szlak:
Multi-Player Bandits - a Musical Chairs Approach. 155-163 - Greg Ver Steeg, Aram Galstyan:
The Information Sieve. 164-172 - Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse H. Engel, Linxi Fan, Christopher Fougner, Awni Y. Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu:
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin. 173-182 - Yue Zhang, Weihong Guo, Soumya Ray:
On the Consistency of Feature Selection With Lasso for Non-linear Targets. 183-191 - Jan Hendrik Metzen:
Minimum Regret Search for Single- and Multi-Task Optimization. 192-200 - Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing:
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. 201-210 - Max Vladymyrov, Miguel Á. Carreira-Perpiñán:
The Variational Nystrom method for large-scale spectral problems. 211-220 - Hongyang Li, Wanli Ouyang, Xiaogang Wang:
Multi-Bias Non-linear Activation in Deep Neural Networks. 221-229 - Giwoong Lee, Eunho Yang, Sung Ju Hwang:
Asymmetric Multi-task Learning based on Task Relatedness and Confidence. 230-238 - Lixin Fan:
Accurate Robust and Efficient Error Estimation for Decision Trees. 239-247 - Ohad Shamir:
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity. 248-256 - Ohad Shamir:
Convergence of Stochastic Gradient Descent for PCA. 257-265 - Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer:
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. 266-275 - Qiang Liu, Jason D. Lee, Michael I. Jordan:
A Kernelized Stein Discrepancy for Goodness-of-fit Tests. 276-284 - Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman:
Variable Elimination in the Fourier Domain. 285-294 - Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen M. Chu:
Low-Rank Matrix Approximation with Stability. 295-303 - Aditya Krishna Menon, Cheng Soon Ong:
Linking losses for density ratio and class-probability estimation. 304-313 - Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. 314-323 - Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. 324-333 - Roy J. Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin M. Marlin:
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams. 334-343 - Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. 344-353 - Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. 354-363 - Siddharth Gopal:
Adaptive Sampling for SGD by Exploiting Side Information. 364-372 - Rose Yu, Yan Liu:
Learning from Multiway Data: Simple and Efficient Tensor Regression. 373-381 - Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. 382-391 - Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou:
Online Stochastic Linear Optimization under One-bit Feedback. 392-401 - Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau:
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. 402-411 - Adish Singla
, Sebastian Tschiatschek, Andreas Krause:
Actively Learning Hemimetrics with Applications to Eliciting User Preferences. 412-420 - Wojciech Zaremba, Tomás Mikolov, Armand Joulin, Rob Fergus:
Learning Simple Algorithms from Examples. 421-429 - Adam Lerer, Sam Gross, Rob Fergus:
Learning Physical Intuition of Block Towers by Example. 430-438 - Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu:
Structure Learning of Partitioned Markov Networks. 439-448 - Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi:
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. 449-457 - Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Beyond CCA: Moment Matching for Multi-View Models. 458-467 - Shashanka Ubaru, Yousef Saad:
Fast methods for estimating the Numerical rank of large matrices. 468-477 - Junyuan Xie, Ross B. Girshick, Ali Farhadi:
Unsupervised Deep Embedding for Clustering Analysis. 478-487 - Shiva Prasad Kasiviswanathan, Hongxia Jin:
Efficient Private Empirical Risk Minimization for High-dimensional Learning. 488-497 - Milan Vojnovic, Se-Young Yun:
Parameter Estimation for Generalized Thurstone Choice Models. 498-506 - Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang:
Large-Margin Softmax Loss for Convolutional Neural Networks. 507-516 - Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi:
A Random Matrix Approach to Echo-State Neural Networks. 517-525 - Rie Johnson, Tong Zhang:
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings. 526-534 - Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi:
Optimality of Belief Propagation for Crowdsourced Classification. 535-544 - Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters:
Stability of Controllers for Gaussian Process Forward Models. 545-554 - Jihun Hamm, Yingjun Cao, Mikhail Belkin:
Learning privately from multiparty data. 555-563 - Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen:
Network Morphism. 564-572 - Roger B. Grosse, James Martens:
A Kronecker-factored approximate Fisher matrix for convolution layers. 573-582 - Sathya N. Ravi, Vamsi K. Ithapu, Sterling C. Johnson, Vikas Singh:
Experimental Design on a Budget for Sparse Linear Models and Applications. 583-592 - Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien:
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. 593-602 - Chao Gao, Yu Lu, Dengyong Zhou:
Exact Exponent in Optimal Rates for Crowdsourcing. 603-611 - Yuting Zhang, Kibok Lee, Honglak Lee:
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification. 612-621 - Jie Shen, Ping Li, Huan Xu:
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit. 622-631 - Frank E. Curtis:
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization. 632-641 - Umut Simsekli, Roland Badeau, A. Taylan Cemgil, Gaël Richard:
Stochastic Quasi-Newton Langevin Monte Carlo. 642-651 - Nan Jiang, Lihong Li:
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning. 652-661 - Chao Qu, Huan Xu, Chong Jin Ong:
Fast Rate Analysis of Some Stochastic Optimization Algorithms. 662-670 - Ke Li, Jitendra Malik:
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing. 671-679 - Hoang Minh Le, Andrew Kang, Yisong Yue, Peter Carr:
Smooth Imitation Learning for Online Sequence Prediction. 680-688 - Yuxin Chen, Govinda M. Kamath, Changho Suh, David Tse:
Community Recovery in Graphs with Locality. 689-698 - Zeyuan Allen Zhu, Elad Hazan:
Variance Reduction for Faster Non-Convex Optimization. 699-707 - Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni:
Loss factorization, weakly supervised learning and label noise robustness. 708-717 - Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof J. Geras, Gregor Urban, Özlem Aslan:
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. 718-726 - Masaaki Imaizumi, Kohei Hayashi:
Doubly Decomposing Nonparametric Tensor Regression. 727-736 - Fabian Pedregosa:
Hyperparameter optimization with approximate gradient. 737-746 - Shai Shalev-Shwartz:
SDCA without Duality, Regularization, and Individual Convexity. 747-754 - Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou:
A Neural Autoregressive Approach to Collaborative Filtering. 764-773 - Itay Safran, Ohad Shamir:
On the Quality of the Initial Basin in Overspecified Neural Networks. 774-782 - Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi:
Primal-Dual Rates and Certificates. 783-792 - Shai Shalev-Shwartz, Yonatan Wexler:
Minimizing the Maximal Loss: How and Why. 793-801 - Daniel L. Pimentel-Alarcón, Robert D. Nowak:
The Information-Theoretic Requirements of Subspace Clustering with Missing Data. 802-810 - Alon Cohen, Tamir Hazan, Tomer Koren:
Online Learning with Feedback Graphs Without the Graphs. 811-819 - Hadrien Glaude, Olivier Pietquin:
PAC learning of Probabilistic Automaton based on the Method of Moments. 820-829 - Igor Melnyk, Arindam Banerjee:
Estimating Structured Vector Autoregressive Models. 830-839 - Christopher Tosh:
Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends. 840-849 - Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda:
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. 850-858 - Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. 859-868 - Gregory J. Puleo, Olgica Milenkovic:
Correlation Clustering and Biclustering with Locally Bounded Errors. 869-877 - Yahel David, Nahum Shimkin:
PAC Lower Bounds and Efficient Algorithms for The Max \(K\)-Armed Bandit Problem. 878-887 - Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed M. Elgammal:
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation. 888-897 - Shane Carr, Roman Garnett, Cynthia Lo:
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces. 898-907 - Yossi Arjevani, Ohad Shamir:
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms. 908-916 - Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. 917-925 - David P. Wipf:
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation. 926-935 - James Newling, François Fleuret:
Fast k-means with accurate bounds. 936-944 - Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner:
Boolean Matrix Factorization and Noisy Completion via Message Passing. 945-954 - Nadav Cohen, Amnon Shashua:
Convolutional Rectifier Networks as Generalized Tensor Decompositions. 955-963 - Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi
, Ben Recht:
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. 964-973 - Kwang-Sung Jun, Robert D. Nowak:
Anytime Exploration for Multi-armed Bandits using Confidence Information. 974-982 - David Belanger, Andrew McCallum:
Structured Prediction Energy Networks. 983-992 - Yuchen Zhang, Jason D. Lee, Michael I. Jordan:
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time. 993-1001 - Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst:
Compressive Spectral Clustering. 1002-1011 - Hiroyuki Kasai, Bamdev Mishra:
Low-rank tensor completion: a Riemannian manifold preconditioning approach. 1012-1021 - Huishuai Zhang, Yuejie Chi, Yingbin Liang:
Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow. 1022-1031 - Carlo D'Eramo, Marcello Restelli, Alessandro Nuara:
Estimating Maximum Expected Value through Gaussian Approximation. 1032-1040 - Urvashi Oswal, Christopher R. Cox, Matthew A. Lambon Ralph, Timothy T. Rogers, Robert D. Nowak:
Representational Similarity Learning with Application to Brain Networks. 1041-1049 - Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. 1050-1059 - Scott E. Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele
, Honglak Lee:
Generative Adversarial Text to Image Synthesis. 1060-1069 - Sandhya Prabhakaran, Elham Azizi, Ambrose J. Carr, Dana Pe'er:
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. 1070-1079 - Zeyuan Allen Zhu, Yang Yuan:
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives. 1080-1089 - Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo:
Sparse Parameter Recovery from Aggregated Data. 1090-1099 - Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang:
Deep Structured Energy Based Models for Anomaly Detection. 1100-1109 - Zeyuan Allen Zhu, Zheng Qu, Peter Richtárik, Yang Yuan:
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling. 1110-1119 - Martín Arjovsky, Amar Shah, Yoshua Bengio:
Unitary Evolution Recurrent Neural Networks. 1120-1128 - Aonan Zhang, John W. Paisley:
Markov Latent Feature Models. 1129-1137 - Yingfei Wang, Chu Wang, Warren B. Powell:
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks. 1138-1147 - Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell A. Poldrack:
A Simple and Provable Algorithm for Sparse Diagonal CCA. 1148-1157 - Huikang Liu, Weijie Wu, Anthony Man-Cho So:
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods. 1158-1167 - Devansh Arpit, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju:
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks. 1168-1176 - Chongxuan Li, Jun Zhu, Bo Zhang:
Learning to Generate with Memory. 1177-1186 - Basura Fernando, Stephen Gould:
Learning End-to-end Video Classification with Rank-Pooling. 1187-1196 - Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell:
Learning to Filter with Predictive State Inference Machines. 1197-1205 - Mostafa Rahmani, George K. Atia:
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling. 1206-1214 - Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
DCM Bandits: Learning to Rank with Multiple Clicks. 1215-1224 - Moritz Hardt, Ben Recht, Yoram Singer:
Train faster, generalize better: Stability of stochastic gradient descent. 1225-1234 - Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm. 1235-1244 - Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen:
Contextual Combinatorial Cascading Bandits. 1245-1253