default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID33rd 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 - Oren Anava, Shie Mannor:
Heteroscedastic Sequences: Beyond Gaussianity. 755-763 - 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 - Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári:
Conservative Bandits. 1254-1262 - Elad Hazan, Haipeng Luo:
Variance-Reduced and Projection-Free Stochastic Optimization. 1263-1271 - Jiaming Song, Zhe Gan, Lawrence Carin:
Factored Temporal Sigmoid Belief Networks for Sequence Learning. 1272-1281 - Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao:
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking. 1282-1291 - David Balduzzi, Muhammad Ghifary:
Strongly-Typed Recurrent Neural Networks. 1292-1300 - Nathan Korda, Balázs Szörényi, Shuai Li:
Distributed Clustering of Linear Bandits in Peer to Peer Networks. 1301-1309 - Han Zhao, Tameem Adel, Geoffrey J. Gordon, Brandon Amos:
Collapsed Variational Inference for Sum-Product Networks. 1310-1318 - Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone:
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search. 1319-1328 - Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel:
Benchmarking Deep Reinforcement Learning for Continuous Control. 1329-1338 - Hu Ding, Yu Liu, Lingxiao Huang, Jian Li:
K-Means Clustering with Distributed Dimensions. 1339-1348 - Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor S. Lempitsky:
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images. 1349-1357 - Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi:
Fast Constrained Submodular Maximization: Personalized Data Summarization. 1358-1367 - Zhaoran Wang, Quanquan Gu, Han Liu:
On the Statistical Limits of Convex Relaxations. 1368-1377 - Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher:
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing. 1378-1387 - Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. 1388-1396 - Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz:
Solving Ridge Regression using Sketched Preconditioned SVRG. 1397-1405 - Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus, Csaba Szepesvári:
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. 1406-1415 - Emmanouil Antonios Platanios, Avinava Dubey, Tom M. Mitchell:
Estimating Accuracy from Unlabeled Data: A Bayesian Approach. 1416-1425 - Chiranjib Bhattacharyya, Navin Goyal, Ravindran Kannan, Jagdeep Pani:
Non-negative Matrix Factorization under Heavy Noise. 1426-1434 - Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier:
Extreme F-measure Maximization using Sparse Probability Estimates. 1435-1444 - Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther:
Auxiliary Deep Generative Models. 1445-1453 - Olivier Canévet, Cijo Jose, François Fleuret:
Importance Sampling Tree for Large-scale Empirical Expectation. 1454-1462 - Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Starting Small - Learning with Adaptive Sample Sizes. 1463-1471 - Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. 1472-1481 - Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. 1482-1491 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams:
Predictive Entropy Search for Multi-objective Bayesian Optimization. 1492-1501 - Rong Ge, James Zou:
Rich Component Analysis. 1502-1510 - José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. 1511-1520 - Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra:
One-Shot Generalization in Deep Generative Models. 1521-1529 - Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon:
Optimal Classification with Multivariate Losses. 1530-1538 - Cédric Malherbe, Emile Contal, Nicolas Vayatis:
A ranking approach to global optimization. 1539-1547 - Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. 1548-1557 - Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther:
Autoencoding beyond pixels using a learned similarity metric. 1558-1566 - Christopher De Sa, Christopher Ré, Kunle Olukotun:
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. 1567-1576 - Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi:
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling. 1577-1586 - Rémy Degenne, Vianney Perchet:
Anytime optimal algorithms in stochastic multi-armed bandits. 1587-1595 - William Hoiles, Mihaela van der Schaar:
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design. 1596-1604 - Gaurav Pandey, Ambedkar Dukkipati:
On collapsed representation of hierarchical Completely Random Measures. 1605-1613 - André F. T. Martins, Ramón Fernandez Astudillo:
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification. 1614-1623 - Sébastien Bubeck, Yin Tat Lee:
Black-box Optimization with a Politician. 1624-1631 - Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami:
Gaussian process nonparametric tensor estimator and its minimax optimality. 1632-1641 - Andres Muñoz Medina, Scott Yang:
No-Regret Algorithms for Heavy-Tailed Linear Bandits. 1642-1650 - Edwin V. Bonilla, Daniel M. Steinberg, Alistair Reid:
Extended and Unscented Kitchen Sinks. 1651-1659 - Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li:
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization. 1660-1669 - Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims:
Recommendations as Treatments: Debiasing Learning and Evaluation. 1670-1679 - Jinsung Yoon, Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar:
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission. 1680-1689 - Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier:
An optimal algorithm for the Thresholding Bandit Problem. 1690-1698 - Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier:
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. 1699-1707 - Christos Louizos, Max Welling:
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. 1708-1716 - Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha:
Learning Granger Causality for Hawkes Processes. 1717-1726 - Yishu Miao, Lei Yu, Phil Blunsom:
Neural Variational Inference for Text Processing. 1727-1736 - Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux:
Dictionary Learning for Massive Matrix Factorization. 1737-1746 - Aäron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu:
Pixel Recurrent Neural Networks. 1747-1756 - Özgür Simsek, Simón Algorta, Amit Kothiyal:
Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As Well. 1757-1765 - Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Gaussian quadrature for matrix inverse forms with applications. 1766-1775 - Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. 1776-1785 - Raman Arora, Poorya Mianjy, Teodor V. Marinov:
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares. 1786-1794 - Mehmet Emin Basbug, Barbara E. Engelhardt:
Hierarchical Compound Poisson Factorization. 1795-1803 - He He, Jordan L. Boyd-Graber:
Opponent Modeling in Deep Reinforcement Learning. 1804-1813 - Xiangyu Wang, David B. Dunson, Chenlei Leng:
No penalty no tears: Least squares in high-dimensional linear models. 1814-1822 - Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq:
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. 1823-1832 - Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz:
On Graduated Optimization for Stochastic Non-Convex Problems. 1833-1841 - Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
Meta-Learning with Memory-Augmented Neural Networks. 1842-1850 - Ran Dai, Rina Barber:
The knockoff filter for FDR control in group-sparse and multitask regression. 1851-1859 - Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
Softened Approximate Policy Iteration for Markov Games. 1860-1868 - Robert M. Gower, Donald Goldfarb, Peter Richtárik:
Stochastic Block BFGS: Squeezing More Curvature out of Data. 1869-1878 - Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff:
Differential Geometric Regularization for Supervised Learning of Classifiers. 1879-1888 - Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu:
Exploiting Cyclic Symmetry in Convolutional Neural Networks. 1889-1898 - Tom Zahavy, Nir Ben-Zrihem, Shie Mannor:
Graying the black box: Understanding DQNs. 1899-1908 - Abram L. Friesen, Pedro M. Domingos:
The Sum-Product Theorem: A Foundation for Learning Tractable Models. 1909-1918 - Amar Shah, Zoubin Ghahramani:
Pareto Frontier Learning with Expensive Correlated Objectives. 1919-1927 - Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu:
Asynchronous Methods for Deep Reinforcement Learning. 1928-1937 - Nate Veldt, David F. Gleich, Michael W. Mahoney:
A Simple and Strongly-Local Flow-Based Method for Cut Improvement. 1938-1947 - Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin:
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models. 1948-1957 - Masanori Kawakita, Jun'ichi Takeuchi:
Barron and Cover's Theory in Supervised Learning and its Application to Lasso. 1958-1966 - Tomer Michaeli, Weiran Wang, Karen Livescu:
Nonparametric Canonical Correlation Analysis. 1967-1976 - Alexander Rakhlin, Karthik Sridharan:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. 1977-1985 - Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves:
Associative Long Short-Term Memory. 1986-1994 - Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas:
Dueling Network Architectures for Deep Reinforcement Learning. 1995-2003 - Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu:
Persistence weighted Gaussian kernel for topological data analysis. 2004-2013 - Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov:
Learning Convolutional Neural Networks for Graphs. 2014-2023 - Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse H. Engel, Awni Y. Hannun, Sanjeev Satheesh:
Persistent RNNs: Stashing Recurrent Weights On-Chip. 2024-2033 - Mikael Henaff, Arthur Szlam, Yann LeCun:
Recurrent Orthogonal Networks and Long-Memory Tasks. 2034-2042 - Stefan Bauer, Bernhard Schölkopf, Jonas Peters:
The Arrow of Time in Multivariate Time Series. 2043-2051 - Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari:
Mixture Proportion Estimation via Kernel Embeddings of Distributions. 2052-2060 - Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast DPP Sampling for Nystrom with Application to Kernel Methods. 2061-2070 - Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard:
Complex Embeddings for Simple Link Prediction. 2071-2080 - Sharad Vikram, Sanjoy Dasgupta:
Interactive Bayesian Hierarchical Clustering. 2081-2090 - Miltiadis Allamanis, Hao Peng, Charles Sutton:
A Convolutional Attention Network for Extreme Summarization of Source Code. 2091-2100 - Michael Kapralov, Vamsi K. Potluru, David P. Woodruff:
How to Fake Multiply by a Gaussian Matrix. 2101-2110 - Marco Gaboardi, Hyun-Woo Lim, Ryan M. Rogers, Salil P. Vadhan:
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing. 2111-2120 - Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard:
Pliable Rejection Sampling. 2121-2129 - Borja Balle, Maziar Gomrokchi, Doina Precup:
Differentially Private Policy Evaluation. 2130-2138 - Philip S. Thomas, Emma Brunskill:
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. 2139-2148 - Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei:
Discrete Deep Feature Extraction: A Theory and New Architectures. 2149-2158 - Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. 2159-2168 - Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Training Deep Neural Networks via Direct Loss Minimization. 2169-2177 - Kyuyeon Hwang, Wonyong Sung:
Sequence to Sequence Training of CTC-RNNs with Partial Windowing. 2178-2187 - Andriy Mnih, Danilo Jimenez Rezende:
Variational Inference for Monte Carlo Objectives. 2188-2196 - Gal Dalal, Elad Gilboa, Shie Mannor:
Hierarchical Decision Making In Electricity Grid Management. 2197-2206 - Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer:
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization. 2207-2216 - Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee:
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units. 2217-2225 - Yichen Wang, Bo Xie, Nan Du, Le Song:
Isotonic Hawkes Processes. 2226-2234 - Hanxiao Liu, Yiming Yang:
Cross-Graph Learning of Multi-Relational Associations. 2235-2243 - Jiangwei Pan, Vinayak A. Rao, Pankaj K. Agarwal, Alan E. Gelfand:
Markov-modulated Marked Poisson Processes for Check-in Data. 2244-2253 - Tudor Achim, Ashish Sabharwal, Stefano Ermon:
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference. 2254-2262 - Periklis A. Papakonstantinou, Jia Xu, Guang Yang:
On the Power and Limits of Distance-Based Learning. 2263-2271 - Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon:
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. 2272-2280 - Farideh Fazayeli, Arindam Banerjee:
Generalized Direct Change Estimation in Ising Model Structure. 2281-2290 - Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Robust Principal Component Analysis with Side Information. 2291-2299 - Huan Gui, Jiawei Han, Quanquan Gu:
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation. 2300-2309 - Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Early and Reliable Event Detection Using Proximity Space Representation. 2310-2319 - Edo Liberty, Kevin J. Lang, Konstantin Shmakov:
Stratified Sampling Meets Machine Learning. 2320-2329 - Xinze Guan, Raviv Raich, Weng-Keen Wong:
Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model. 2330-2339 - Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco:
Generalization Properties and Implicit Regularization for Multiple Passes SGM. 2340-2348 - Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. 2349-2357 - Yuanzhi Li, Yingyu Liang, Andrej Risteski:
Recovery guarantee of weighted low-rank approximation via alternating minimization. 2358-2367 - Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio:
Deconstructing the Ladder Network Architecture. 2368-2376 - Ian Osband, Benjamin Van Roy, Zheng Wen:
Generalization and Exploration via Randomized Value Functions. 2377-2386 - Alex Kantchelian, J. D. Tygar, Anthony D. Joseph:
Evasion and Hardening of Tree Ensemble Classifiers. 2387-2396 - Caiming Xiong, Stephen Merity, Richard Socher:
Dynamic Memory Networks for Visual and Textual Question Answering. 2397-2406 - Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. 2407-2416 - Tatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola:
Learning Population-Level Diffusions with Generative RNNs. 2417-2426 - Xingyuan Pan, Vivek Srikumar:
Expressiveness of Rectifier Networks. 2427-2435 - Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. 2436-2444 - David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. 2445-2453 - Cong Han Lim, Steve Wright:
A Box-Constrained Approach for Hard Permutation Problems. 2454-2463 - Pourya Zadeh, Reshad Hosseini, Suvrit Sra:
Geometric Mean Metric Learning. 2464-2471 - Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang:
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. 2472-2481 - Cheng Li, Bingyu Wang, Virgil Pavlu, Javed A. Aslam:
Conditional Bernoulli Mixtures for Multi-label Classification. 2482-2491 - Yutian Chen, Zoubin Ghahramani:
Scalable Discrete Sampling as a Multi-Armed Bandit Problem. 2492-2501 - Krzysztof Choromanski, Vikas Sindhwani:
Recycling Randomness with Structure for Sublinear time Kernel Expansions. 2502-2510 - Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio:
Bidirectional Helmholtz Machines. 2511-2519 - Jacob D. Abernethy, Elad Hazan:
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. 2520-2528 - Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone:
Preconditioning Kernel Matrices. 2529-2538 - Jason M. Altschuler, Aditya Bhaskara, Gang Fu, Vahab S. Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam:
Greedy Column Subset Selection: New Bounds and Distributed Algorithms. 2539-2548 - Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron C. Courville:
Dynamic Capacity Networks. 2549-2558 - Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod:
Pricing a Low-regret Seller. 2559-2567 - Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. 2568-2577 - Thomas Bottesch, Thomas Bühler, Markus Kächele:
Speeding up k-means by approximating Euclidean distances via block vectors. 2578-2586 - Stephen Mussmann, Stefano Ermon:
Learning and Inference via Maximum Inner Product Search. 2587-2596 - Anton Rodomanov, Dmitry Kropotov:
A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums. 2597-2605 - Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton:
A Kernel Test of Goodness of Fit. 2606-2615 - Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood:
Interacting Particle Markov Chain Monte Carlo. 2616-2625 - Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. 2626-2634 - Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
A Theory of Generative ConvNet. 2635-2644 - Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. 2645-2654 - Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Computationally Efficient Nyström Approximation using Fast Transforms. 2655-2663 - Gabriel Peyré, Marco Cuturi, Justin Solomon:
Gromov-Wasserstein Averaging of Kernel and Distance Matrices. 2664-2672 - Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy:
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics. 2673-2681 - Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams:
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. 2682-2691 - Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov:
Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning. 2692-2701 - Hanjun Dai, Bo Dai, Le Song:
Discriminative Embeddings of Latent Variable Models for Structured Data. 2702-2711 - Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers:
Robust Random Cut Forest Based Anomaly Detection on Streams. 2712-2721 - Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. 2722-2731 - Chao Chen, Novi Quadrianto:
Clustering High Dimensional Categorical Data via Topographical Features. 2732-2740 - Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. 2741-2750 - Wenruo Bai, Rishabh K. Iyer, Kai Wei, Jeff A. Bilmes:
Algorithms for Optimizing the Ratio of Submodular Functions. 2751-2759 - Jonathan Ho, Jayesh K. Gupta, Stefano Ermon:
Model-Free Imitation Learning with Policy Optimization. 2760-2769 - Moustapha Cissé, Maruan Al-Shedivat, Samy Bengio:
ADIOS: Architectures Deep In Output Space. 2770-2779 - Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications. 2780-2789 - Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, Honglak Lee:
Control of Memory, Active Perception, and Action in Minecraft. 2790-2799 - Jina Suh, Xiaojin Zhu, Saleema Amershi:
The Label Complexity of Mixed-Initiative Classifier Training. 2800-2809 - Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. 2810-2819 - Nicolò Colombo, Nikos Vlassis:
Tensor Decomposition via Joint Matrix Schur Decomposition. 2820-2828 - Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. 2829-2838 - Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. 2839-2848 - Darryl Dexu Lin, Sachin S. Talathi, V. Sreekanth Annapureddy:
Fixed Point Quantization of Deep Convolutional Networks. 2849-2858 - Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. 2859-2867 - Po-Wei Wang, Matt Wytock, J. Zico Kolter:
Epigraph projections for fast general convex programming. 2868-2877 - Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. 2878-2886 - Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill:
Energetic Natural Gradient Descent. 2887-2895 - David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski:
Partition Functions from Rao-Blackwellized Tempered Sampling. 2896-2905 - Zhibing Zhao, Peter Piech, Lirong Xia:
Learning Mixtures of Plackett-Luce Models. 2906-2914 - David Abel, D. Ellis Hershkowitz, Michael L. Littman:
Near Optimal Behavior via Approximate State Abstraction. 2915-2923 - Lihua Lei, William Fithian:
Power of Ordered Hypothesis Testing. 2924-2932 - Pavol Bielik, Veselin Raychev, Martin T. Vechev:
PHOG: Probabilistic Model for Code. 2933-2942 - András György, Csaba Szepesvári:
Shifting Regret, Mirror Descent, and Matrices. 2943-2951 - Jelena Luketina, Tapani Raiko, Mathias Berglund, Klaus Greff:
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters. 2952-2960 - Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki:
Model-Free Trajectory Optimization for Reinforcement Learning. 2961-2970 - Yunlong Jiao, Anna Korba, Eric Sibony:
Controlling the distance to a Kemeny consensus without computing it. 2971-2980 - Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause:
Horizontally Scalable Submodular Maximization. 2981-2989 - Taco Cohen, Max Welling:
Group Equivariant Convolutional Networks. 2990-2999 - Nico Piatkowski, Katharina Morik:
Stochastic Discrete Clenshaw-Curtis Quadrature. 3000-3009 - Matthew Riemer, Aditya Vempaty, Flávio P. Calmon, Fenno F. Terry Heath III, Richard Hull, Elham Khabiri:
Correcting Forecasts with Multifactor Neural Attention. 3010-3019 - Fredrik D. Johansson, Uri Shalit, David A. Sontag:
Learning Representations for Counterfactual Inference. 3020-3029 - Yunseong Hwang, Anh Tong, Jaesik Choi:
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series. 3030-3039 - Brooks Paige, Frank D. Wood:
Inference Networks for Sequential Monte Carlo in Graphical Models. 3040-3049 - Benjamin Bloem-Reddy, John P. Cunningham:
Slice Sampling on Hamiltonian Trajectories. 3050-3058 - Çaglar Gülçehre, Marcin Moczulski, Misha Denil, Yoshua Bengio:
Noisy Activation Functions. 3059-3068 - Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon:
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. 3069-3077
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-11-08 20:17 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
