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ORCID30th ICML 2013: Atlanta, GA, USA
- Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013. JMLR Workshop and Conference Proceedings 28, JMLR.org 2013
Cycle 1 Papers
- Raphael Sznitman, Aurélien Lucchi, Peter I. Frazier, Bruno Jedynak, Pascal Fua:
An Optimal Policy for Target Localization with Application to Electron Microscopy. 1-9 - Krikamol Muandet, David Balduzzi, Bernhard Schölkopf:
Domain Generalization via Invariant Feature Representation. 10-18 - Byron Boots, Geoffrey J. Gordon:
A Spectral Learning Approach to Range-Only SLAM. 19-26 - Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani:
Near-Optimal Bounds for Cross-Validation via Loss Stability. 27-35 - Nishant Ajay Mehta, Alexander G. Gray:
Sparsity-Based Generalization Bounds for Predictive Sparse Coding. 36-44 - Xiaowei Zhang, Delin Chu:
Sparse Uncorrelated Linear Discriminant Analysis. 45-52 - Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. 53-61 - Philipp Hennig:
Fast Probabilistic Optimization from Noisy Gradients. 62-70 - Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. 71-79 - Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray:
Stochastic Alternating Direction Method of Multipliers. 80-88 - Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. 89-97 - Sinead Williamson, Avinava Dubey, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. 98-106 - Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla:
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. 107-114 - James Bergstra, Daniel Yamins, David D. Cox:
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. 115-123 - Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms. 124-132 - Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen:
Cost-Sensitive Tree of Classifiers. 133-141 - Xi Li, Guosheng Lin, Chunhua Shen, Anton van den Hengel, Anthony R. Dick:
Learning Hash Functions Using Column Generation. 142-150 - Wei Chen, Yajun Wang, Yang Yuan:
Combinatorial Multi-Armed Bandit: General Framework and Applications. 151-159 - Yuxin Chen, Andreas Krause:
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization. 160-168 - Huyen Do, Alexandros Kalousis:
Convex formulations of radius-margin based Support Vector Machines. 169-177 - William L. Hamilton, Mahdi Milani Fard, Joelle Pineau:
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations. 178-186 - Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A Machine Learning Framework for Programming by Example. 187-195 - Ross B. Girshick, Hyun Oh Song, Trevor Darrell:
Discriminatively Activated Sparselets. 196-204 - Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman:
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification. 205-213 - Quannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu:
Fixed-Point Model For Structured Labeling. 214-221 - Boqing Gong, Kristen Grauman, Fei Sha:
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. 222-230 - Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur:
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization. 231-239 - Fang Han, Han Liu:
Principal Component Analysis on non-Gaussian Dependent Data. 240-248 - Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. 249-257 - Sébastien Bubeck, Tengyao Wang, Nitin Viswanathan:
Multiple Identifications in Multi-Armed Bandits. 258-265 - Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro:
Learning Optimally Sparse Support Vector Machines. 266-274 - Creighton Heaukulani, Zoubin Ghahramani:
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks. 275-283 - Shuo Xiang, Xiaoshen Tong, Jieping Ye:
Efficient Sparse Group Feature Selection via Nonconvex Optimization. 284-292 - Min Xiao, Yuhong Guo:
Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model. 293-301 - Wenlin Chen, Kilian Q. Weinberger, Yixin Chen:
Maximum Variance Correction with Application to A* Search. 302-310 - Eleanor Wong, Suyash P. Awate, P. Thomas Fletcher:
Adaptive Sparsity in Gaussian Graphical Models. 311-319 - Yuri Grinberg, Doina Precup:
Average Reward Optimization Objective In Partially Observable Domains. 320-328 - Mladen Kolar, Han Liu:
Feature Selection in High-Dimensional Classification. 329-337 - Harsh H. Pareek, Pradeep Ravikumar:
Human Boosting. 338-346 - Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias:
Efficient Dimensionality Reduction for Canonical Correlation Analysis. 347-355 - Drausin Wulsin, Emily B. Fox, Brian Litt:
Parsing epileptic events using a Markov switching process model for correlated time series. 356-364 - Aaditya Ramdas, Aarti Singh:
Optimal rates for stochastic convex optimization under Tsybakov noise condition. 365-373 - Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling:
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning. 374-382 - Yudong Chen, Constantine Caramanis:
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery. 383-391 - Taiji Suzuki:
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method. 392-400 - Kilho Shin:
A New Frontier of Kernel Design for Structured Data. 401-409 - Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger:
Learning with Marginalized Corrupted Features. 410-418 - Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel:
Approximation properties of DBNs with binary hidden units and real-valued visible units. 419-426 - Martin Jaggi:
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. 427-435 - Tianqi Chen, Hang Li, Qiang Yang, Yong Yu:
General Functional Matrix Factorization Using Gradient Boosting. 436-444 - Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi:
Iterative Learning and Denoising in Convolutional Neural Associative Memories. 445-453 - Elad Gilboa, Yunus Saatçi, John P. Cunningham:
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. 454-461 - Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel:
Active Learning for Multi-Objective Optimization. 462-470 - Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux:
A Generalized Kernel Approach to Structured Output Learning. 471-479 - Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz:
Efficient Active Learning of Halfspaces: an Aggressive Approach. 480-488 - Braxton Osting, Christoph Brune, Stanley J. Osher:
Enhanced statistical rankings via targeted data collection. 489-497 - Trung Thanh Nguyen, Zhuoru Li, Tomi Silander, Tze-Yun Leong:
Online Feature Selection for Model-based Reinforcement Learning. 498-506 - Paul Ruvolo, Eric Eaton:
ELLA: An Efficient Lifelong Learning Algorithm. 507-515 - Harikrishna Narasimhan, Shivani Agarwal:
A Structural SVM Based Approach for Optimizing Partial AUC. 516-524 - K. S. Sesh Kumar, Francis R. Bach:
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs. 525-533 - Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan:
Adaptive Task Assignment for Crowdsourced Classification. 534-542 - Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. 543-551 - Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai:
Better Mixing via Deep Representations. 552-560 - Ke Zhai, Jordan L. Boyd-Graber:
Online Latent Dirichlet Allocation with Infinite Vocabulary. 561-569 - Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári:
Characterizing the Representer Theorem. 570-578 - Eric C. Hall, Rebecca Willett:
Dynamical Models and tracking regret in online convex programming. 579-587 - Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief:
Large-Scale Bandit Problems and KWIK Learning. 588-596 - Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson:
Vanishing Component Analysis. 597-605 - Matthew D. Golub, Steven M. Chase, Byron M. Yu:
Learning an Internal Dynamics Model from Control Demonstration. 606-614 - Daryl Lim, Gert R. G. Lanckriet, Brian McFee:
Robust Structural Metric Learning. 615-623 - Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer, Matthias Hein:
Constrained fractional set programs and their application in local clustering and community detection. 624-632 - Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang:
Efficient Semi-supervised and Active Learning of Disjunctions. 633-641 - MohamadAli Torkamani, Daniel Lowd:
Convex Adversarial Collective Classification. 642-650 - Yann Chevaleyre, Frédéric Koriche, Jean-Daniel Zucker:
Rounding Methods for Discrete Linear Classification. 651-659
Cycle 2 Papers
- Shuang-Hong Yang, Hongyuan Zha:
Mixture of Mutually Exciting Processes for Viral Diffusion. 1-9 - David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Vine Copulas for Multivariate Dependence. 10-18 - Michal Valko, Alexandra Carpentier, Rémi Munos:
Stochastic Simultaneous Optimistic Optimization. 19-27 - Alexandra Carpentier, Rémi Munos:
Toward Optimal Stratification for Stratified Monte-Carlo Integration. 28-36 - Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye:
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems. 37-45 - Truyen Tran, Dinh Q. Phung, Svetha Venkatesh:
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities. 46-54 - Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul:
A Variational Approximation for Topic Modeling of Hierarchical Corpora. 55-63 - Georg M. Goerg:
Forecastable Component Analysis. 64-72 - Gabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann:
Ellipsoidal Multiple Instance Learning. 73-81 - Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Local Low-Rank Matrix Approximation. 82-90 - Tanguy Urvoy, Fabrice Clérot, Raphaël Féraud, Sami Naamane:
Generic Exploration and K-armed Voting Bandits. 91-99 - Ha Quang Minh, Loris Bazzani, Vittorio Murino:
A unifying framework for vector-valued manifold regularization and multi-view learning. 100-108 - Gartheeban Ganeshapillai, John V. Guttag, Andrew Lo:
Learning Connections in Financial Time Series. 109-117 - Sida Wang, Christopher D. Manning:
Fast dropout training. 118-126 - Zhirong Yang, Jaakko Peltonen, Samuel Kaski:
Scalable Optimization of Neighbor Embedding for Visualization. 127-135 - Blaise Hanczar, Mohamed Nadif:
Precision-recall space to correct external indices for biclustering. 136-144 - Sharon Wulff, Ruth Urner, Shai Ben-David:
Monochromatic Bi-Clustering. 145-153 - Alain Droniou, Olivier Sigaud:
Gated Autoencoders with Tied Input Weights. 154-162 - Nicola Rebagliati:
Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings. 163-171 - Fang Han, Han Liu:
Transition Matrix Estimation in High Dimensional Time Series. 172-180 - Jason Weston, Ameesh Makadia, Hector Yee:
Label Partitioning For Sublinear Ranking. 181-189 - Huayan Wang, Daphne Koller:
Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing. 190-198 - Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. 199-207 - Ruichu Cai, Zhenjie Zhang, Zhifeng Hao:
SADA: A General Framework to Support Robust Causation Discovery. 208-216 - Kihyuk Sohn, Guanyu Zhou, Chansoo Lee, Honglak Lee:
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines. 217-225 - Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati:
Sequential Bayesian Search. 226-234 - Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch:
Sparse projections onto the simplex. 235-243 - Uri Shalit, Daphna Weinshall, Gal Chechik:
Modeling Musical Influence with Topic Models. 244-252 - Mrinal Kanti Das, Suparna Bhattacharya, Chiranjib Bhattacharyya, Kanchi Gopinath:
Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically. 253-261 - Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin:
Exploring the Mind: Integrating Questionnaires and fMRI. 262-270 - Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher:
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions. 271-279 - Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. 280-288 - Siddharth Gopal, Yiming Yang:
Distributed training of Large-scale Logistic models. 289-297 - Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing:
An Adaptive Learning Rate for Stochastic Variational Inference. 298-306 - Matus Telgarsky:
Margins, Shrinkage, and Boosting. 307-315 - Billy Chang, Uwe Krüger, Rafal Kustra, Junping Zhang:
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment. 316-324 - Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. 325-333 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. 334-342 - Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
Sparse coding for multitask and transfer learning. 343-351 - Ka Yu Hui:
Direct Modeling of Complex Invariances for Visual Object Features. 352-360 - Jan-Willem van de Meent, Jonathan E. Bronson, Frank D. Wood, Ruben L. Gonzalez, Chris Wiggins:
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. 361-369 - Liu Yang, Steve Hanneke:
Activized Learning with Uniform Classification Noise. 370-378
Cycle 3 Papers
- Sergey Levine, Vladlen Koltun:
Guided Policy Search. 1-9 - Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. 10-18 - Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Gossip-based distributed stochastic bandit algorithms. 19-27 - Tor Lattimore, Marcus Hutter, Peter Sunehag:
The Sample-Complexity of General Reinforcement Learning. 28-36 - Alon Zweig, Daphna Weinshall:
Hierarchical Regularization Cascade for Joint Learning. 37-45 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Multi-Class Classification with Maximum Margin Multiple Kernel. 46-54 - Michael Großhans, Christoph Sawade, Michael Brückner, Tobias Scheffer:
Bayesian Games for Adversarial Regression Problems. 55-63 - Xi Chen, Qihang Lin, Dengyong Zhou:
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. 64-72 - Mladen Kolar, Han Liu, Eric P. Xing:
Markov Network Estimation From Multi-attribute Data. 73-81 - Dan Zhang, Jingrui He, Luo Si, Richard D. Lawrence:
MILEAGE: Multiple Instance LEArning with Global Embedding. 82-90 - Ji Liu, Lei Yuan, Jieping Ye:
Guaranteed Sparse Recovery under Linear Transformation. 91-99 - Roland Memisevic, Georgios Exarchakis:
Learning invariant features by harnessing the aperture problem. 100-108 - Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic:
Efficient Ranking from Pairwise Comparisons. 109-117 - Prateek Jain, Abhradeep Thakurta:
Differentially Private Learning with Kernels. 118-126 - Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. 127-135 - Javier Almingol, Luis Montesano, Manuel Lopes
:
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space. 136-144 - Rustem Takhanov, Vladimir Kolmogorov:
Inference algorithms for pattern-based CRFs on sequence data. 145-153 - Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori:
One-Bit Compressed Sensing: Provable Support and Vector Recovery. 154-162 - Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Tensor Analyzers. 163-171 - Toby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis R. Bach:
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression. 172-180 - Kwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers:
Learning from Human-Generated Lists. 181-189 - Huayan Wang, Daphne Koller:
A Fast and Exact Energy Minimization Algorithm for Cycle MRFs. 190-198 - Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel:
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. 199-207 - Nicholas J. Bryan, Gautham J. Mysore:
An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation. 208-216 - Miles Lopes:
Estimating Unknown Sparsity in Compressed Sensing. 217-225 - Tamara Broderick, Brian Kulis, Michael I. Jordan:
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. 226-234 - Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf:
The Most Generative Maximum Margin Bayesian Networks. 235-243 - Quoc V. Le, Tamás Sarlós, Alexander J. Smola:
Fastfood - Computing Hilbert Space Expansions in loglinear time. 244-252 - Rita Chattopadhyay, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye:
Joint Transfer and Batch-mode Active Learning. 253-261 - Yuan Qi, Yandong Guo:
Message passing with l1 penalized KL minimization. 262-270 - Marco Cuturi, Alexandre d'Aspremont:
Mean Reversion with a Variance Threshold. 271-279 - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Top-down particle filtering for Bayesian decision trees. 280-288 - Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon:
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. 289-297 - Hua Wang, Feiping Nie, Heng Huang:
Robust and Discriminative Self-Taught Learning. 298-306 - Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello:
Safe Policy Iteration. 307-315 - Mariya Ishteva, Haesun Park, Le Song:
Unfolding Latent Tree Structures using 4th Order Tensors. 316-324 - Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork:
Learning Fair Representations. 325-333 - Le Song, Mariya Ishteva, Ankur P. Parikh, Eric P. Xing, Haesun Park:
Hierarchical Tensor Decomposition of Latent Tree Graphical Models. 334-342 - Tom Schaul, Sixin Zhang, Yann LeCun:
No more pesky learning rates. 343-351 - Hua Wang, Feiping Nie, Heng Huang:
Multi-View Clustering and Feature Learning via Structured Sparsity. 352-360 - Harm van Seijen, Richard S. Sutton:
Planning by Prioritized Sweeping with Small Backups. 361-369 - Sebastian Brechtel, Tobias Gindele, Rüdiger Dillmann:
Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation. 370-378 - Arnak S. Dalalyan, Mohamed Hebiri, Katia Meziani, Joseph Salmon:
Learning Heteroscedastic Models by Convex Programming under Group Sparsity. 379-387 - Kai Zhang, Vincent Wenchen Zheng, Qiaojun Wang, James Tin-Yau Kwok, Qiang Yang, Ivan Marsic:
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels. 388-395 - Zeyuan Allen Zhu, Silvio Lattanzi, Vahab S. Mirrokni:
A Local Algorithm for Finding Well-Connected Clusters. 396-404 - Wei Bi, James Tin-Yau Kwok:
Efficient Multi-label Classification with Many Labels. 405-413 - Yuxin Chen, Yuejie Chi:
Spectral Compressed Sensing via Structured Matrix Completion. 414-422 - Ming Yang, Yingming Li, Zhongfei Zhang:
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model. 423-431 - Kyunghyun Cho:
Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images. 432-440 - Purushottam Kar, Bharath K. Sriperumbudur, Prateek Jain, Harish Karnick:
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions. 441-449 - Mahsa Baktashmotlagh, Mehrtash Tafazzoli Harandi, Abbas Bigdeli, Brian C. Lovell, Mathieu Salzmann:
Non-Linear Stationary Subspace Analysis with Application to Video Classification. 450-458 - Jean Honorio, Tommi S. Jaakkola:
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. 459-467 - Emanuele Coviello, Adeel Mumtaz, Antoni B. Chan, Gert R. G. Lanckriet:
That was fast! Speeding up NN search of high dimensional distributions. 468-476 - Max Vladymyrov, Miguel Á. Carreira-Perpiñán:
Entropic Affinities: Properties and Efficient Numerical Computation. 477-485 - Cijo Jose, Prasoon Goyal, Parv Aggrwal, Manik Varma:
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction. 486-494 - Aviv Tamar, Dotan Di Castro, Shie Mannor:
Temporal Difference Methods for the Variance of the Reward To Go. 495-503 - Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
\(\propto\)SVM for Learning with Label Proportions. 504-512 - Philipp Krähenbühl, Vladlen Koltun:
Parameter Learning and Convergent Inference for Dense Random Fields. 513-521 - Paul Mineiro, Nikos Karampatziakis:
Loss-Proportional Subsampling for Subsequent ERM. 522-530 - Xiangrui Meng:
Scalable Simple Random Sampling and Stratified Sampling. 531-539 - Li Cheng:
Riemannian Similarity Learning. 540-548 - Yangqing Jia, Oriol Vinyals, Trevor Darrell:
On Compact Codes for Spatially Pooled Features. 549-557 - Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Dynamic Covariance Models for Multivariate Financial Time Series. 558-566 - Alex Gittens, Michael W. Mahoney:
Revisiting the Nystrom method for improved large-scale machine learning. 567-575 - Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto:
Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals. 576-584 - Wenzhuo Yang, Huan Xu:
A Unified Robust Regression Model for Lasso-like Algorithms. 585-593 - Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona:
Quickly Boosting Decision Trees - Pruning Underachieving Features Early. 594-602 - Aditya Krishna Menon, Harikrishna Narasimhan, Shivani Agarwal, Sanjay Chawla:
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance. 603-611 - Jason Chuang, Sonal Gupta, Christopher D. Manning, Jeffrey Heer:
Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment. 612-620 - Lijun Zhang, Jinfeng Yi, Rong Jin, Ming Lin, Xiaofei He:
Online Kernel Learning with a Near Optimal Sparsity Bound. 621-629 - Tzu-Kuo Huang, Jeff G. Schneider:
Spectral Learning of Hidden Markov Models from Dynamic and Static Data. 630-638 - Sung Ju Hwang, Kristen Grauman, Fei Sha:
Analogy-preserving Semantic Embedding for Visual Object Categorization. 639-647 - Michael Izbicki:
Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training. 648-656 - Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh:
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach. 657-665 - Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf:
Modeling Information Propagation with Survival Theory. 666-674 - Ofer Dekel, Elad Hazan:
Better Rates for Any Adversarial Deterministic MDP. 675-683 - Christos Dimitrakakis, Nikolaos Tziortziotis:
ABC Reinforcement Learning. 684-692 - Robert J. Durrant, Ata Kabán:
Sharp Generalization Error Bounds for Randomly-projected Classifiers. 693-701 - Aryeh Kontorovich, Boaz Nadler, Roi Weiss:
On learning parametric-output HMMs. 702-710 - Daphna Weinshall, Gal Levi, Dmitri Hanukaev:
LDA Topic Model with Soft Assignment of Descriptors to Words. 711-719 - Hanna Kamyshanska, Roland Memisevic:
On autoencoder scoring. 720-728 - Sotirios Chatzis:
Infinite Markov-Switching Maximum Entropy Discrimination Machines. 729-737 - Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. 738-746 - Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Stavros Korokythakis:
Sparse PCA through Low-rank Approximations. 747-755 - Dinah Shender, John D. Lafferty:
Computation-Risk Tradeoffs for Covariance-Thresholded Regression. 756-764 - Dmitry M. Malioutov, Kush R. Varshney:
Exact Rule Learning via Boolean Compressed Sensing. 765-773 - Yudong Chen, Constantine Caramanis, Shie Mannor:
Robust Sparse Regression under Adversarial Corruption. 774-782 - Julien Mairal:
Optimization with First-Order Surrogate Functions. 783-791 - Hema Swetha Koppula, Ashutosh Saxena:
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation. 792-800 - Philip M. Long, Rocco A. Servedio:
Consistency versus Realizable H-Consistency for Multiclass Classification. 801-809 - Sivan Sabato, Adam Kalai:
Feature Multi-Selection among Subjective Features. 810-818 - Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang:
Domain Adaptation under Target and Conditional Shift. 819-827 - Ben London, Bert Huang, Ben Taskar, Lise Getoor:
Collective Stability in Structured Prediction: Generalization from One Example. 828-836 - Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel:
Stable Coactive Learning via Perturbation. 837-845 - Xinggang Wang, Baoyuan Wang, Xiang Bai, Wenyu Liu, Zhuowen Tu:
Max-Margin Multiple-Instance Dictionary Learning. 846-854 - Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Fast Semidifferential-based Submodular Function Optimization. 855-863 - Mehmet Gönen, Suleiman A. Khan, Samuel Kaski:
Kernelized Bayesian Matrix Factorization. 864-872 - Robert Gens, Pedro M. Domingos:
Learning the Structure of Sum-Product Networks. 873-880 - Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Quantile Regression for Large-scale Applications. 881-887 - Xiangrui Meng, Michael W. Mahoney:
Robust Regression on MapReduce. 888-896 - Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama:
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. 897-905 - Wei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou:
One-Pass AUC Optimization. 906-914 - Jeremy Jancsary, Sebastian Nowozin, Carsten Rother:
Learning Convex QP Relaxations for Structured Prediction. 915-923 - David Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall:
Concurrent Reinforcement Learning from Customer Interactions. 924-932 - Peng Sun, Jie Zhou:
Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner. 933-941 - Ilja Kuzborskij, Francesco Orabona:
Stability and Hypothesis Transfer Learning. 942-950 - Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger:
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. 951-959 - Tue Herlau, Morten Mørup, Mikkel N. Schmidt:
Modeling Temporal Evolution and Multiscale Structure in Networks. 960-968 - Changyou Chen, Vinayak A. Rao, Wray L. Buntine, Yee Whye Teh:
Dependent Normalized Random Measures. 969-977 - Minjie Xu, Jun Zhu, Bo Zhang:
Fast Max-Margin Matrix Factorization with Data Augmentation. 978-986 - Ashish Gupta, Murat Ayhan, Anthony Maida:
Natural Image Bases to Represent Neuroimaging Data. 987-994 - Nir Ailon, Yudong Chen, Huan Xu:
Breaking the Small Cluster Barrier of Graph Clustering. 995-1003 - Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich:
Approximate Inference in Collective Graphical Models. 1004-1012 - Colorado Reed, Zoubin Ghahramani:
Scaling the Indian Buffet Process via Submodular Maximization. 1013-1021 - Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro:
Mini-Batch Primal and Dual Methods for SVMs. 1022-1030 - Darren Homrighausen, Daniel J. McDonald:
The lasso, persistence, and cross-validation. 1031-1039 - Arun Tejasvi Chaganty, Percy Liang:
Spectral Experts for Estimating Mixtures of Linear Regressions. 1040-1048 - Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Distribution to Distribution Regression. 1049-1057 - Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus:
Regularization of Neural Networks using DropConnect. 1058-1066 - Andrew Gordon Wilson, Ryan Prescott Adams:
Gaussian Process Kernels for Pattern Discovery and Extrapolation. 1067-1075 - Zhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger:
Anytime Representation Learning. 1076-1084 - Tan Nguyen, Scott Sanner:
Algorithms for Direct 0-1 Loss Optimization in Binary Classification. 1085-1093 - Róbert Busa-Fekete, Balázs Szörényi, Weiwei Cheng, Paul Weng, Eyke Hüllermeier:
Top-k Selection based on Adaptive Sampling of Noisy Preferences. 1094-1102 - Yusuf Erol, Lei Li, Bharath Ramsundar, Stuart Russell:
The Extended Parameter Filter. 1103-1111 - Nina Balcan, Avrim Blum, Yishay Mansour:
Exploiting Ontology Structures and Unlabeled Data for Learning. 1112-1120 - Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He:
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions. 1121-1129 - Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier:
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. 1130-1138 - Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton:
On the importance of initialization and momentum in deep learning. 1139-1147 - Kostadin Georgiev, Preslav Nakov:
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines. 1148-1156 - Emile Richard, Francis R. Bach, Jean-Philippe Vert:
Intersecting singularities for multi-structured estimation. 1157-1165 - David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani:
Structure Discovery in Nonparametric Regression through Compositional Kernel Search. 1166-1174 - Lisa Friedland, David D. Jensen, Michael Lavine:
Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events. 1175-1183 - Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor:
Smooth Operators. 1184-1192 - Sergiu Goschin, Ari Weinstein, Michael L. Littman:
The Cross-Entropy Method Optimizes for Quantiles. 1193-1201 - Weicong Ding, Mohammad Hossein Rohban, Prakash Ishwar, Venkatesh Saligrama:
Topic Discovery through Data Dependent and Random Projections. 1202-1210 - Marc G. Bellemare, Joel Veness, Michael Bowling:
Bayesian Learning of Recursively Factored Environments. 1211-1219 - Alekh Agarwal:
Selective sampling algorithms for cost-sensitive multiclass prediction. 1220-1228 - Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou:
The Bigraphical Lasso. 1229-1237 - Zohar Shay Karnin, Tomer Koren, Oren Somekh:
Almost Optimal Exploration in Multi-Armed Bandits. 1238-1246 - Galen Andrew, Raman Arora, Jeff A. Bilmes, Karen Livescu:
Deep Canonical Correlation Analysis. 1247-1255 - Misha Denil, David Matheson, Nando de Freitas:
Consistency of Online Random Forests. 1256-1264 - Matt Wytock, J. Zico Kolter:
Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting. 1265-1273 - Minmin Chen, Alice X. Zheng, Kilian Q. Weinberger:
Fast Image Tagging. 1274-1282 - Matthew Tesch, Jeff G. Schneider, Howie Choset:
Expensive Function Optimization with Stochastic Binary Outcomes. 1283-1291 - Krzysztof J. Geras, Charles Sutton:
Multiple-source cross-validation. 1292-1300 - Ke Zhou, Hongyuan Zha, Le Song:
Learning Triggering Kernels for Multi-dimensional Hawkes Processes. 1301-1309 - Razvan Pascanu, Tomás Mikolov, Yoshua Bengio:
On the difficulty of training recurrent neural networks. 1310-1318 - Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Maxout Networks. 1319-1327 - Mohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei, Hal Daumé III, Larry S. Davis:
Predictable Dual-View Hashing. 1328-1336 - Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro, Andrew Y. Ng:
Deep learning with COTS HPC systems. 1337-1345 - James C. Ross, Jennifer G. Dy:
Nonparametric Mixture of Gaussian Processes with Constraints. 1346-1354 - Sashank J. Reddi, Barnabás Póczos:
Scale Invariant Conditional Dependence Measures. 1355-1363 - Stéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell:
Learning Policies for Contextual Submodular Prediction. 1364-1372 - Minje Kim, Paris Smaragdis:
Manifold Preserving Hierarchical Topic Models for Quantization and Approximation. 1373-1381 - Kohei Ogawa, Yoshiki Suzuki, Ichiro Takeuchi:
Safe Screening of Non-Support Vectors in Pathwise SVM Computation. 1382-1390 - Bernardo Ávila Pires, Csaba Szepesvári, Mohammad Ghavamzadeh:
Cost-sensitive Multiclass Classification Risk Bounds. 1391-1399 - Jinfeng Yi, Lijun Zhang, Rong Jin, Qi Qian, Anil K. Jain:
Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion. 1400-1408 - Umut Simsekli, Ali Taylan Cemgil, Yusuf Kenan Yilmaz:
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models. 1409-1417 - Volodymyr Kuleshov:
Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. 1418-1425 - Amr Ahmed, Liangjie Hong, Alexander J. Smola:
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. 1426-1434 - Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles L. Isbell Jr.:
Tree-Independent Dual-Tree Algorithms. 1435-1443 - Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil:
Multilinear Multitask Learning. 1444-1452 - Pooria Joulani, András György, Csaba Szepesvári:
Online Learning under Delayed Feedback. 1453-1461 - Ziyu Wang, Shakir Mohamed, Nando de Freitas:
Adaptive Hamiltonian and Riemann Manifold Monte Carlo. 1462-1470 - Eric Sodomka, Elizabeth Hilliard, Michael L. Littman, Amy Greenwald:
Coco-Q: Learning in Stochastic Games with Side Payments. 1471-1479 - Jeffrey Ho, Yuchen Xie, Baba C. Vemuri:
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning. 1480-1488 - Panos Toulis, Edward K. Kao:
Estimation of Causal Peer Influence Effects. 1489-1497
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