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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 - Ralf Eggeling, Mikko Koivisto, Ivo Grosse:
Dealing with small data: On the generalization of context trees. 1245-1253 - Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin:
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood. 1254-1263 - Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon:
A Bayesian nonparametric procedure for comparing algorithms. 1264-1272 - Taiji Suzuki:
Convergence rate of Bayesian tensor estimator and its minimax optimality. 1273-1282 - Yifan Wu, András György, Csaba Szepesvári:
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments. 1283-1291 - Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Nested Sequential Monte Carlo Methods. 1292-1301 - Rishit Sheth, Yuyang Wang, Roni Khardon:
Sparse Variational Inference for Generalized GP Models. 1302-1311 - Tom Schaul, Daniel Horgan, Karol Gregor, David Silver:
Universal Value Function Approximators. 1312-1320 - Julien Pérolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin:
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games. 1321-1329 - Dravyansh Sharma, Ashish Kapoor, Amit Deshpande:
On Greedy Maximization of Entropy. 1330-1338 - Yi Wang, Bin Li, Yang Wang, Fang Chen:
Metadata Dependent Mondrian Processes. 1339-1347 - Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu:
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. 1348-1357 - Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki:
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood. 1358-1366 - Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung:
Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets. 1367-1375 - Peter Kairouz, Sewoong Oh, Pramod Viswanath:
The Composition Theorem for Differential Privacy. 1376-1385 - Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Convex Formulation for Learning from Positive and Unlabeled Data. 1386-1394 - Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura:
Threshold Influence Model for Allocating Advertising Budgets. 1395-1404 - Amit Daniely, Alon Gonen, Shai Shalev-Shwartz:
Strongly Adaptive Online Learning. 1405-1411 - Miao Xu, Rong Jin, Zhi-Hua Zhou:
CUR Algorithm for Partially Observed Matrices. 1412-1421 - Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. 1422-1431 - Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz:
MRA-based Statistical Learning from Incomplete Rankings. 1432-1441 - Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners. 1442-1451 - David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin:
Towards a Learning Theory of Cause-Effect Inference. 1452-1461 - Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra:
DRAW: A Recurrent Neural Network For Image Generation. 1462-1471 - Ehsan Amid, Antti Ukkonen:
Multiview Triplet Embedding: Learning Attributes in Multiple Maps. 1472-1480 - Marc Peter Deisenroth, Jun Wei Ng:
Distributed Gaussian Processes. 1481-1490 - Gongguo Tang, Parikshit Shah:
Guaranteed Tensor Decomposition: A Moment Approach. 1491-1500 - Zirui Zhou, Qi Zhang, Anthony Man-Cho So:
\(\ell_{1, p}\)-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods. 1501-1510 - Qiuyi Han, Kevin S. Xu, Edoardo M. Airoldi:
Consistent estimation of dynamic and multi-layer block models. 1511-1520 - Manel Tagorti, Bruno Scherrer:
On the Rate of Convergence and Error Bounds for LSTD(\(\lambda\)). 1521-1529 - Danilo Jimenez Rezende, Shakir Mohamed:
Variational Inference with Normalizing Flows. 1530-1538 - Benn Macdonald, Catherine F. Higham, Dirk Husmeier:
Controversy in mechanistic modelling with Gaussian processes. 1539-1547 - Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco:
Convex Learning of Multiple Tasks and their Structure. 1548-1557 - Margarita Osadchy, Tamir Hazan, Daniel Keren:
K-hyperplane Hinge-Minimax Classifier. 1558-1566 - Boris Lesner, Bruno Scherrer:
Non-Stationary Approximate Modified Policy Iteration. 1567-1575 - Mathieu Serrurier, Henri Prade:
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees. 1576-1584 - Chong You, René Vidal:
Geometric Conditions for Subspace-Sparse Recovery. 1585-1593 - Amar Shah, David A. Knowles, Zoubin Ghahramani:
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process. 1594-1603 - Xiaodan Zhu, Parinaz Sobhani, Hongyu Guo:
Long Short-Term Memory Over Recursive Structures. 1604-1612 - Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra:
Weight Uncertainty in Neural Network. 1613-1622 - Jiaqian Yu, Matthew B. Blaschko:
Learning Submodular Losses with the Lovasz Hinge. 1623-1631 - Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke:
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. 1632-1641 - Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu:
Hashing for Distributed Data. 1642-1650 - Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing:
Large-scale Distributed Dependent Nonparametric Trees. 1651-1659 - Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier:
Qualitative Multi-Armed Bandits: A Quantile-Based Approach. 1660-1668 - Li Xu, Jimmy S. J. Ren, Qiong Yan, Renjie Liao, Jiaya Jia:
Deep Edge-Aware Filters. 1669-1678 - Shiau Hong Lim, Yudong Chen, Huan Xu:
A Convex Optimization Framework for Bi-Clustering. 1679-1688 - Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli:
Is Feature Selection Secure against Training Data Poisoning? 1689-1698 - José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani:
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. 1699-1707 - Michaël Perrot, Amaury Habrard:
A Theoretical Analysis of Metric Hypothesis Transfer Learning. 1708-1717 - Yujia Li, Kevin Swersky, Richard S. Zemel:
Generative Moment Matching Networks. 1718-1727 - Megasthenis Asteris, Anastasios Kyrillidis, Alexandros G. Dimakis, Han-Gyol Yi, Bharath Chandrasekaran:
Stay on path: PCA along graph paths. 1728-1736 - Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan:
Deep Learning with Limited Numerical Precision. 1737-1746 - Jie Wang, Jieping Ye:
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices. 1747-1756 - Taco Cohen, Max Welling:
Harmonic Exponential Families on Manifolds. 1757-1765 - Christopher Clark, Amos J. Storkey:
Training Deep Convolutional Neural Networks to Play Go. 1766-1774 - Andrew Gordon Wilson, Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). 1775-1784 - Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille, Raquel Urtasun:
Learning Deep Structured Models. 1785-1794 - Haim Avron, Lior Horesh:
Community Detection Using Time-Dependent Personalized PageRank. 1795-1803 - Josip Djolonga, Andreas Krause:
Scalable Variational Inference in Log-supermodular Models. 1804-1813 - Chris M. Lloyd, Tom Gunter, Michael A. Osborne, Stephen J. Roberts:
Variational Inference for Gaussian Process Modulated Poisson Processes. 1814-1822 - Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin:
Scalable Deep Poisson Factor Analysis for Topic Modeling. 1823-1832 - Nico Görnitz, Mikio L. Braun, Marius Kloft:
Hidden Markov Anomaly Detection. 1833-1842 - Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo:
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes. 1843-1851 - Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal:
Convex Calibrated Surrogates for Hierarchical Classification. 1852-1860 - José Miguel Hernández-Lobato, Ryan P. Adams:
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. 1861-1869 - Christopher Berlind, Ruth Urner:
Active Nearest Neighbors in Changing Environments. 1870-1879 - Hanxiao Liu, Yiming Yang:
Bipartite Edge Prediction via Transductive Learning over Product Graphs. 1880-1888 - John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz:
Trust Region Policy Optimization. 1889-1897 - Mingming Gong, Kun Zhang, Bernhard Schölkopf, Dacheng Tao, Philipp Geiger:
Discovering Temporal Causal Relations from Subsampled Data. 1898-1906 - Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon:
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons. 1907-1916 - Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing:
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. 1917-1925 - Behnam Neyshabur, Nathan Srebro:
On Symmetric and Asymmetric LSHs for Inner Product Search. 1926-1934 - Yunlong Jiao, Jean-Philippe Vert:
The Kendall and Mallows Kernels for Permutations. 1935-1944 - Purnima Rajan, Weidong Han, Raphael Sznitman, Peter I. Frazier, Bruno Jedynak:
Bayesian Multiple Target Localization. 1945-1953 - Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
Submodularity in Data Subset Selection and Active Learning. 1954-1963 - Philip Bachman, Doina Precup:
Variational Generative Stochastic Networks with Collaborative Shaping. 1964-1972 - Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. 1973-1982 - Feng Nan, Joseph Wang, Venkatesh Saligrama:
Feature-Budgeted Random Forest. 1983-1991 - Maxwell W. Libbrecht, Michael M. Hoffman, Jeff A. Bilmes, William Stafford Noble:
Entropic Graph-based Posterior Regularization. 1992-2001 - Tam Le, Marco Cuturi:
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations. 2002-2011 - Or Zuk, Avishai Wagner:
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements. 2012-2020 - Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand:
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction. 2021-2029 - Wenzhao Lian, Ricardo Henao, Vinayak A. Rao, Joseph E. Lucas, Lawrence Carin:
A Multitask Point Process Predictive Model. 2030-2038 - Michael Zhu, Stefano Ermon:
A Hybrid Approach for Probabilistic Inference using Random Projections. 2039-2047 - Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. 2048-2057 - Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better than Your Teacher. 2058-2066 - Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, Yoshua Bengio:
Gated Feedback Recurrent Neural Networks. 2067-2075 - Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar:
Context-based Unsupervised Data Fusion for Decision Making. 2076-2084 - Rémi Lebret, Pedro H. O. Pinheiro, Ronan Collobert:
Phrase-based Image Captioning. 2085-2094 - Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat:
Celeste: Variational inference for a generative model of astronomical images. 2095-2103 - Adarsh Prasad, Harsh H. Pareek, Pradeep Ravikumar:
Distributional Rank Aggregation, and an Axiomatic Analysis. 2104-2112 - Dougal Maclaurin, David Duvenaud, Ryan P. Adams:
Gradient-based Hyperparameter Optimization through Reversible Learning. 2113-2122 - Miltiadis Allamanis, Daniel Tarlow, Andrew D. Gordon, Yi Wei:
Bimodal Modelling of Source Code and Natural Language. 2123-2132 - Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos:
Cheap Bandits. 2133-2142 - Frédéric Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman:
Subsampling Methods for Persistent Homology. 2143-2151 - Bernardino Romera-Paredes, Philip H. S. Torr:
An embarrassingly simple approach to zero-shot learning. 2152-2161 - Xinyang Yi, Constantine Caramanis, Eric Price:
Binary Embedding: Fundamental Limits and Fast Algorithm. 2162-2170 - Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams:
Scalable Bayesian Optimization Using Deep Neural Networks. 2171-2180 - Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim:
How Hard is Inference for Structured Prediction? 2181-2190 - Oren Anava, Elad Hazan, Assaf Zeevi:
Online Time Series Prediction with Missing Data. 2191-2199 - Jason Pacheco, Erik B. Sudderth:
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. 2200-2208 - Yacine Jernite, Alexander M. Rush, David A. Sontag:
A Fast Variational Approach for Learning Markov Random Field Language Models. 2209-2217 - Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Removing systematic errors for exoplanet search via latent causes. 2218-2226 - Yves-Laurent Kom Samo, Stephen J. Roberts:
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes. 2227-2236 - Kook Jin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth:
Correlation Clustering in Data Streams. 2237-2246 - Qingming Tang, Siqi Sun, Jinbo Xu:
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior. 2247-2255 - Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. 2256-2265 - Andrew Trask, David Gilmore, Matthew Russell:
Modeling Order in Neural Word Embeddings at Scale. 2266-2275 - Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani:
Distributed Inference for Dirichlet Process Mixture Models. 2276-2284 - Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen:
Compressing Neural Networks with the Hashing Trick. 2285-2294 - Rong Ge, James Zou:
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. 2295-2303 - Roger B. Grosse, Ruslan Salakhutdinov:
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix. 2304-2313 - Harm Vanseijen, Richard S. Sutton:
A Deeper Look at Planning as Learning from Replay. 2314-2322 - Alina Beygelzimer, Satyen Kale, Haipeng Luo:
Optimal and Adaptive Algorithms for Online Boosting. 2323-2331 - Christopher De Sa, Christopher Ré, Kunle Olukotun:
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems. 2332-2341 - Rafal Józefowicz, Wojciech Zaremba, Ilya Sutskever:
An Empirical Exploration of Recurrent Network Architectures. 2342-2350 - Ju Sun, Qing Qu, John Wright:
Complete Dictionary Recovery Using Nonconvex Optimization. 2351-2360 - Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton:
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. 2361-2369 - Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. 2370-2379 - Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh:
High Confidence Policy Improvement. 2380-2388 - Zelda Mariet, Suvrit Sra:
Fixed-point algorithms for learning determinantal point processes. 2389-2397 - Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Performance Measures. 2398-2407 - James Martens, Roger B. Grosse:
Optimizing Neural Networks with Kronecker-factored Approximate Curvature. 2408-2417 - Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon:
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. 2418-2426 - Anh T. Pham, Raviv Raich, Xiaoli Z. Fern, Jesús Pérez Arriaga:
Multi-instance multi-label learning in the presence of novel class instances. 2427-2435 - Liva Ralaivola, Massih-Reza Amini:
Entropy-Based Concentration Inequalities for Dependent Variables. 2436-2444 - Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon:
PU Learning for Matrix Completion. 2445-2453 - Necdet S. Aybat, Zi Wang, Garud Iyengar:
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization. 2454-2462 - Congyuan Yang, Daniel P. Robinson, René Vidal:
Sparse Subspace Clustering with Missing Entries. 2463-2472 - Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton T. Morrison, Emily Butler, Kobus Barnard:
Moderated and Drifting Linear Dynamical Systems. 2473-2482 - Taehoon Lee, Sungroh Yoon:
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions. 2483-2492 - Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. 2493-2502 - Lucas Theis, Matthew D. Hoffman:
A trust-region method for stochastic variational inference with applications to streaming data. 2503-2511 - Kevin Winner, Garrett Bernstein, Daniel Sheldon:
Inference in a Partially Observed Queuing Model with Applications in Ecology. 2512-2520 - Ruitong Huang, András György, Csaba Szepesvári:
Deterministic Independent Component Analysis. 2521-2530 - Maxime Gasse, Alexandre Aussem, Haytham Elghazel:
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. 2531-2539 - Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. 2540-2548 - Bin Gu, Charles X. Ling:
A New Generalized Error Path Algorithm for Model Selection. 2549-2558
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