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ORCID20th AISTATS 2017: Fort Lauderdale, FL, USA
- Aarti Singh, Xiaojin (Jerry) Zhu:
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA. Proceedings of Machine Learning Research 54, PMLR 2017 - Tianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar:
Minimax Gaussian Classification & Clustering. 1-9 - Mark Rowland, Aldo Pacchiano, Adrian Weller:
Conditions beyond treewidth for tightness of higher-order LP relaxations. 10-18 - Catalin Ionescu, Alin-Ionut Popa, Cristian Sminchisescu:
Large-Scale Data-Dependent Kernel Approximation. 19-27 - Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy:
Clustering from Multiple Uncertain Experts. 28-36 - Renbo Zhao, Vincent Yan Fu Tan, Huan Xu:
Online Nonnegative Matrix Factorization with General Divergences. 37-45 - Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
ASAGA: Asynchronous Parallel SAGA. 46-54 - Jonathan Scarlett, Volkan Cevher
:
Lower Bounds on Active Learning for Graphical Model Selection. 55-64 - Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi:
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. 65-74 - Pierre Gaillard, Olivier Wintenberger:
Sparse Accelerated Exponential Weights. 75-82 - Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan:
On the Learnability of Fully-Connected Neural Networks. 83-91 - Ibrahim M. Alabdulmohsin:
An Information-Theoretic Route from Generalization in Expectation to Generalization in Probability. 92-100 - Lijie Chen, Jian Li, Mingda Qiao:
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. 101-110 - Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. 111-120 - Andrew Stevens, Yunchen Pu, Yannan Sun, Gregory Spell, Lawrence Carin:
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis. 121-129 - Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi:
Consistent and Efficient Nonparametric Different-Feature Selection. 130-138 - Francois Fagan, Jalaj Bhandari, John P. Cunningham:
Annular Augmentation Sampling. 139-147 - Lihua Lei, Michael I. Jordan:
Less than a Single Pass: Stochastically Controlled Stochastic Gradient. 148-156 - Roy J. Adams, Benjamin M. Marlin:
Learning Time Series Detection Models from Temporally Imprecise Labels. 157-165 - Himabindu Lakkaraju, Cynthia Rudin:
Learning Cost-Effective and Interpretable Treatment Regimes. 166-175 - Marc Abeille, Alessandro Lazaric:
Linear Thompson Sampling Revisited. 176-184 - James Newling, François Fleuret:
A Sub-Quadratic Exact Medoid Algorithm. 185-193 - Daniel McDonald:
Minimax Density Estimation for Growing Dimension. 194-203 - Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama:
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. 204-212 - Alexander Zimin, Christoph H. Lampert:
Learning Theory for Conditional Risk Minimization. 213-222 - Yuxin Chen, Seyed Hamed Hassani, Andreas Krause:
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests. 223-231 - Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin:
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data. 232-241 - Benjamin Cowley, João D. Semedo, Amin Zandvakili, Matthew A. Smith, Adam Kohn, Byron M. Yu:
Distance Covariance Analysis. 242-251 - Sohail Bahmani, Justin Romberg:
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation. 252-260 - Pierre Alquier, The Tien Mai, Massimiliano Pontil:
Regret Bounds for Lifelong Learning. 261-269 - Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic:
Poisson intensity estimation with reproducing kernels. 270-279 - Alnur Ali, Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam:
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation. 280-288 - Ian Fellows, Mark Handcock:
Removing Phase Transitions from Gibbs Measures. 289-297 - Rebecca C. Steorts, Matt Barnes, Willie Neiswanger:
Performance Bounds for Graphical Record Linkage. 298-306 - Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Regret Bounds for Transfer Learning in Bayesian Optimisation. 307-315 - Tianyi Zhou, Hua Ouyang, Jeff A. Bilmes, Yi Chang, Carlos Guestrin:
Scaling Submodular Maximization via Pruned Submodularity Graphs. 316-324 - Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski:
Localized Lasso for High-Dimensional Regression. 325-333 - Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes:
Encrypted Accelerated Least Squares Regression. 334-343 - Daniel L. Pimentel-Alarcón, Robert D. Nowak:
Random Consensus Robust PCA. 344-352 - Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto:
Gray-box Inference for Structured Gaussian Process Models. 353-361 - Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. 362-371 - Nathan Kallus:
A Framework for Optimal Matching for Causal Inference. 372-381 - Jonathan Huggins, James Zou:
Quantifying the accuracy of approximate diffusions and Markov chains. 382-391 - Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Stochastic Rank-1 Bandits. 392-401 - Géraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale:
On the Troll-Trust Model for Edge Sign Prediction in Social Networks. 402-411 - Vincent Cohen-Addad, Varun Kanade:
Online Optimization of Smoothed Piecewise Constant Functions. 412-420 - Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. 421-429 - Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz:
ConvNets with Smooth Adaptive Activation Functions for Regression. 430-439 - Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, Eric Vigoda:
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models. 440-449 - Pan Li, Arya Mazumdar, Olgica Milenkovic:
Efficient Rank Aggregation via Lehmer Codes. 450-459 - Aapo Hyvärinen, Hiroshi Morioka:
Nonlinear ICA of Temporally Dependent Stationary Sources. 460-469 - Atsushi Nitanda, Taiji Suzuki:
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. 470-478 - Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. 479-488 - Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei:
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. 489-498 - Matthew M. Graham, Amos J. Storkey:
Asymptotically exact inference in differentiable generative models. 499-508 - Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi:
Decentralized Collaborative Learning of Personalized Models over Networks. 509-517 - Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai:
Contextual Bandits with Latent Confounders: An NMF Approach. 518-527 - Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. 528-536 - Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama:
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. 537-546 - Marina Vinyes, Guillaume Obozinski:
Fast column generation for atomic norm regularization. 547-556 - Thomas Brouwer, Pietro Liò:
Bayesian Hybrid Matrix Factorisation for Data Integration. 557-566 - Youssef Mroueh, Etienne Marcheret, Vaibhava Goel:
Co-Occurring Directions Sketching for Approximate Matrix Multiply. 567-575 - Ronan Fruit, Alessandro Lazaric:
Exploration-Exploitation in MDPs with Options. 576-584 - Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi:
Local Perturb-and-MAP for Structured Prediction. 585-594 - Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell:
Gradient Boosting on Stochastic Data Streams. 595-603 - Joon Kwon, Vianney Perchet:
Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates. 604-613 - Miaoyan Wang, Yun S. Song:
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD). 614-622 - Meelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach:
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. 623-631 - Feras Saad, Vikash Mansinghka:
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. 632-641 - Dezhi Hong, Quanquan Gu, Kamin Whitehouse:
High-dimensional Time Series Clustering via Cross-Predictability. 642-651 - Alexey Zaytsev, Evgeny Burnaev:
Minimax Approach to Variable Fidelity Data Interpolation. 652-661 - Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. 662-671 - Yuancheng Zhu, Zhe Liu, Siqi Sun:
Learning Nonparametric Forest Graphical Models with Prior Information. 672-680 - Kaushik Sinha, Omid Keivani:
Sparse Randomized Partition Trees for Nearest Neighbor Search. 681-689 - Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan:
Horde of Bandits using Gaussian Markov Random Fields. 690-699 - Francois Belletti, Evan Randall Sparks, Alexandre M. Bayen, Joseph Gonzalez:
Random projection design for scalable implicit smoothing of randomly observed stochastic processes. 700-708 - Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu:
Trading off Rewards and Errors in Multi-Armed Bandits. 709-717 - Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. 718-727 - Tor Lattimore, Csaba Szepesvári:
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. 728-737 - Ghassen Jerfel, Mehmet Emin Basbug, Barbara E. Engelhardt:
Dynamic Collaborative Filtering With Compound Poisson Factorization. 738-747 - Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Rank Aggregation and Prediction with Item Features. 748-756 - Chengming Jiang, Huiqing Xie, Zhaojun Bai:
Robust and Efficient Computation of Eigenvectors in a Generalized Spectral Method for Constrained Clustering. 757-766 - Asish Ghoshal, Jean Honorio:
Information-theoretic limits of Bayesian network structure learning. 767-775 - Colin Wei, Iain Murray:
Markov Chain Truncation for Doubly-Intractable Inference. 776-784 - Yi Hong, Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer:
Regression Uncertainty on the Grassmannian. 785-793 - Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. 794-802 - Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Unsupervised Sequential Sensor Acquisition. 803-811 - Songtao Lu, Mingyi Hong, Zhengdao Wang:
A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization. 812-821 - Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim:
Hierarchically-partitioned Gaussian Process Approximation. 822-831 - Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan:
Scalable Learning of Non-Decomposable Objectives. 832-840 - Tianfan Fu, Zhihua Zhang:
CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC. 841-850 - Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison-Based Nearest Neighbor Search. 851-859 - Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. 860-868 - Dmytro Perekrestenko, Volkan Cevher, Martin Jaggi:
Faster Coordinate Descent via Adaptive Importance Sampling. 869-877 - Mohammad Emtiyaz Khan, Wu Lin:
Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. 878-887 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek:
Hit-and-Run for Sampling and Planning in Non-Convex Spaces. 888-895 - Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling:
DP-EM: Differentially Private Expectation Maximization. 896-904 - Juho Piironen, Aki Vehtari:
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. 905-913 - Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski:
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. 914-922 - Pan Xu, Tingting Zhang, Quanquan Gu:
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. 923-932 - Amit Moscovich, Ariel Jaffe, Boaz Nadler:
Minimax-optimal semi-supervised regression on unknown manifolds. 933-942 - Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett:
Improved Strongly Adaptive Online Learning using Coin Betting. 943-951 - Qiang Liu, Jason D. Lee:
Black-box Importance Sampling. 952-961 - Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi:
Fairness Constraints: Mechanisms for Fair Classification. 962-970 - Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo:
Frequency Domain Predictive Modelling with Aggregated Data. 971-980 - Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. 981-990 - Ryan Rogers, Daniel Kifer:
A New Class of Private Chi-Square Hypothesis Tests. 991-1000 - Anna Korba, Stéphan Clémençon, Eric Sibony:
A Learning Theory of Ranking Aggregation. 1001-1010 - Albert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin:
Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere. 1011-1019 - Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cédric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamás Sarlós, Jamal Atif:
Structured adaptive and random spinners for fast machine learning computations. 1020-1029 - Aleksandar Botev, Bowen Zheng, David Barber:
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. 1030-1038 - Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. 1039-1047 - Nicholas Ruozzi:
A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case. 1048-1056 - Ruoxi Sun, Evan Archer, Liam Paninski:
Scalable Variational Inference for Super Resolution Microscopy. 1057-1065 - Donald Goldfarb, Garud Iyengar, Chaoxu Zhou:
Linear Convergence of Stochastic Frank Wolfe Variants. 1066-1074 - Seong-Hwan Jun, Samuel W. K. Wong, James V. Zidek, Alexandre Bouchard-Côté:
Sequential Graph Matching with Sequential Monte Carlo. 1075-1084 - Nishant A. Mehta:
Fast rates with high probability in exp-concave statistical learning. 1085-1093 - Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues:
Generalization Error of Invariant Classifiers. 1094-1103 - Stefanos Poulis, Sanjoy Dasgupta:
Learning with Feature Feedback: from Theory to Practice. 1104-1113 - Ciara Pike-Burke, Steffen Grünewälder:
Optimistic Planning for the Stochastic Knapsack Problem. 1114-1122 - Raman Sankaran, Francis R. Bach, Chiranjib Bhattacharyya:
Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted L1-norms. 1123-1131 - Shaofei Wang, Steffen Wolf, Charless C. Fowlkes, Julian Yarkony:
Tracking Objects with Higher Order Interactions via Delayed Column Generation. 1132-1140 - Wei Ping, Alexander Ihler:
Belief Propagation in Conditional RBMs for Structured Prediction. 1141-1149 - Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro:
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. 1150-1158 - Xiangru Lian, Mengdi Wang, Ji Liu:
Finite-sum Composition Optimization via Variance Reduced Gradient Descent. 1159-1167 - Beilun Wang, Ji Gao, Yanjun Qi:
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. 1168-1177 - Lu Tian, Quanquan Gu:
Communication-efficient Distributed Sparse Linear Discriminant Analysis. 1178-1187 - Alp Yurtsever, Madeleine Udell, Joel A. Tropp, Volkan Cevher:
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage. 1188-1196 - Heinrich Jiang, Samory Kpotufe:
Modal-set estimation with an application to clustering. 1197-1206 - Martin Slawski:
Compressed Least Squares Regression revisited. 1207-1215 - Bo Xie, Yingyu Liang, Le Song:
Diverse Neural Network Learns True Target Functions. 1216-1224 - Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf:
Local Group Invariant Representations via Orbit Embeddings. 1225-1235 - Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer:
Relativistic Monte Carlo. 1236-1245 - Marc Abeille, Alessandro Lazaric:
Thompson Sampling for Linear-Quadratic Control Problems. 1246-1254 - Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon:
Fast Classification with Binary Prototypes. 1255-1263 - Johan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica:
Prediction Performance After Learning in Gaussian Process Regression. 1264-1272 - Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas:
Communication-Efficient Learning of Deep Networks from Decentralized Data. 1273-1282 - Shengyang Sun, Changyou Chen, Lawrence Carin:
Learning Structured Weight Uncertainty in Bayesian Neural Networks. 1283-1292 - David A. Moore, Stuart Russell:
Signal-based Bayesian Seismic Monitoring. 1293-1301 - Youngsuk Park, David Hallac, Stephen P. Boyd, Jure Leskovec:
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields. 1302-1310 - Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse:
Discovering and Exploiting Additive Structure for Bayesian Optimization. 1311-1319 - Samory Kpotufe:
Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning. 1320-1328 - Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart:
Spatial Decompositions for Large Scale SVMs. 1329-1337 - Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood:
Inference Compilation and Universal Probabilistic Programming. 1338-1348 - Aniruddha Bhargava, Ravi Ganti, Robert D. Nowak:
Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications. 1349-1357 - Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
Information Projection and Approximate Inference for Structured Sparse Variables. 1358-1366 - Yihan Gao, Aditya G. Parameswaran, Jian Peng:
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting. 1367-1374 - Yichen Wang, Xiaojing Ye, Haomin Zhou, Hongyuan Zha, Le Song:
Linking Micro Event History to Macro Prediction in Point Process Models. 1375-1384 - Da Tang, Tony Jebara:
Initialization and Coordinate Optimization for Multi-way Matching. 1385-1393 - Vivek F. Farias, Andrew A. Li:
Optimal Recovery of Tensor Slices. 1394-1402 - Alexander Rakhlin, Karthik Sridharan:
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. 1403-1411 - Justin Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts:
Distribution of Gaussian Process Arc Lengths. 1412-1420 - Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Distributed Adaptive Sampling for Kernel Matrix Approximation. 1421-1429 - Ping Li:
Binary and Multi-Bit Coding for Stable Random Projections. 1430-1438 - Forough Arabshahi, Anima Anandkumar:
Spectral Methods for Correlated Topic Models. 1439-1447 - Alexandru Niculescu-Mizil, Ehsan Abbasnejad:
Label Filters for Large Scale Multilabel Classification. 1448-1457 - Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Embeddings. 1458-1467 - Alan Malek, Sumeet Katariya, Yinlam Chow, Mohammad Ghavamzadeh:
Sequential Multiple Hypothesis Testing with Type I Error Control. 1468-1476 - Ariadna Quattoni, Xavier Carreras, Matthias Gallé:
A Maximum Matching Algorithm for Basis Selection in Spectral Learning. 1477-1485 - Amir Massoud Farahmand, André Barreto, Daniel Nikovski:
Value-Aware Loss Function for Model-based Reinforcement Learning. 1486-1494 - Cheng Tang, Claire Monteleoni:
Convergence Rate of Stochastic k-means. 1495-1503 - Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein:
Automated Inference with Adaptive Batches. 1504-1513 - Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon:
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. 1514-1522 - Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. 1523-1531 - Asish Ghoshal, Jean Honorio:
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions. 1532-1540 - Ankit Anand, Ritesh Noothigattu, Parag Singla, Mausam:
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models. 1541-1549 - Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. 1550-1559 - Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh:
Scalable Greedy Feature Selection via Weak Submodularity. 1560-1568
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