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ORCID22nd AISTATS 2019: Naha, Okinawa, Japan
- Kamalika Chaudhuri, Masashi Sugiyama:
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan. Proceedings of Machine Learning Research 89, PMLR 2019 - Fabian Pedregosa, Kilian Fatras, Mattia Casotto:
Proximal Splitting Meets Variance Reduction. 1-10 - Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Optimal Noise-Adding Mechanism in Additive Differential Privacy. 11-20 - Matey Neykov:
Tossing Coins Under Monotonicity. 21-30 - Matey Neykov:
Gaussian Regression with Convex Constraints. 31-38 - Adrian Rivera Cardoso, Huan Xu:
Risk-Averse Stochastic Convex Bandit. 39-47 - Antoine Dedieu:
Error bounds for sparse classifiers in high-dimensions. 48-56 - Alexis Bellot, Mihaela van der Schaar:
Boosting Transfer Learning with Survival Data from Heterogeneous Domains. 57-65 - Matthias Bauer, Andriy Mnih:
Resampled Priors for Variational Autoencoders. 66-75 - Marcel Hirt, Petros Dellaportas:
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. 76-86 - Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu, Lawrence Carin:
Scalable Thompson Sampling via Optimal Transport. 87-96 - Emma Pierson, Pang Wei Koh, Tatsunori B. Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang:
Inferring Multidimensional Rates of Aging from Cross-Sectional Data. 97-107 - Junliang Du, Antonio R. Linero:
Interaction Detection with Bayesian Decision Tree Ensembles. 108-117 - Matt Barnes, Artur Dubrawski:
On the Interaction Effects Between Prediction and Clustering. 118-126 - Yibo Lin, Zhao Song, Lin F. Yang
:
Towards a Theoretical Understanding of Hashing-Based Neural Nets. 127-137 - Pan Zhou, Xiao-Tong Yuan, Jiashi Feng:
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds. 138-147 - Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood:
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. 148-157 - Gunwoong Park, Hyewon Park:
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models. 158-166 - Michalis K. Titsias, Francisco J. R. Ruiz:
Unbiased Implicit Variational Inference. 167-176 - Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi:
Efficient Linear Bandits through Matrix Sketching. 177-185 - Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller:
Orthogonal Estimation of Wasserstein Distances. 186-195 - Simon S. Du, Wei Hu:
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity. 196-205 - Shinsaku Sakaue:
Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros. 206-215 - Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Block Stability for MAP Inference. 216-225 - Jiasen Yang, Vinayak A. Rao, Jennifer Neville:
A Stein-Papangelou Goodness-of-Fit Test for Point Processes. 226-235 - Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang:
KAMA-NNs: Low-dimensional Rotation Based Neural Networks. 236-245 - Quentin Berthet, Varun Kanade:
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain. 246-255 - Joseph Tassarotti, Jean-Baptiste Tristan, Michael L. Wick:
Sketching for Latent Dirichlet-Categorical Models. 256-265 - Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian:
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models. 266-275 - Rishabh K. Iyer, Jeffrey A. Bilmes:
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs. 276-285 - Dan Garber, Atara Kaplan:
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. 286-294 - Dan Garber:
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity. 295-303 - Filip Hanzely, Peter Richtárik:
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches. 304-312 - Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar:
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems. 313-322 - Alexandre Hollocou, Thomas Bonald, Marc Lelarge:
Modularity-based Sparse Soft Graph Clustering. 323-332 - Martin Jankowiak, Theofanis Karaletsos:
Pathwise Derivatives for Multivariate Distributions. 333-342 - Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang, Dimitris N. Metaxas:
Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning. 343-352 - Bo Chang, Shenyi Pan, Harry Joe:
Vine copula structure learning via Monte Carlo tree search. 353-361 - Jialin Dong, Yuanming Shi:
Blind Demixing via Wirtinger Flow with Random Initialization. 362-370 - Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo:
Performance Metric Elicitation from Pairwise Classifier Comparisons. 371-379 - Alexander Jung, Natalia Vesselinova:
Analysis of Network Lasso for Semi-Supervised Regression. 380-387 - Nikos Kargas, Nicholas D. Sidiropoulos:
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm. 388-396 - Jie Shen, Pranjal Awasthi, Ping Li:
Robust Matrix Completion from Quantized Observations. 397-407 - Zelda Mariet, Vitaly Kuznetsov:
Foundations of Sequence-to-Sequence Modeling for Time Series. 408-417 - Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie:
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit. 418-427 - Renbo Zhao, William B. Haskell, Vincent Y. F. Tan:
An Optimal Algorithm for Stochastic Three-Composite Optimization. 428-437 - Wang Chi Cheung, Vincent Y. F. Tan, Zixin Zhong:
A Thompson Sampling Algorithm for Cascading Bandits. 438-447 - Yi-Shan Wu, Po-An Wang, Chi-Jen Lu:
Lifelong Optimization with Low Regret. 448-456 - Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher:
Sparse Multivariate Bernoulli Processes in High Dimensions. 457-466 - Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Stochastic and Adversarial Bandits. 467-475 - Steven Kleinegesse, Michael U. Gutmann:
Efficient Bayesian Experimental Design for Implicit Models. 476-485 - Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Local Saddle Point Optimization: A Curvature Exploitation Approach. 486-495 - Alexander Marx, Jilles Vreeken:
Testing Conditional Independence on Discrete Data using Stochastic Complexity. 496-505 - Matthew Staib, Bryan Wilder, Stefanie Jegelka:
Distributionally Robust Submodular Maximization. 506-516 - Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang:
A Robust Zero-Sum Game Framework for Pool-based Active Learning. 517-526 - Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath:
Support and Invertibility in Domain-Invariant Representations. 527-536 - Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla:
Efficient Inference in Multi-task Cox Process Models. 537-546 - Emanuel Laude, Tao Wu, Daniel Cremers:
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. 547-556 - Bai Li, Changyou Chen, Hao Liu, Lawrence Carin:
On Connecting Stochastic Gradient MCMC and Differential Privacy. 557-566 - Brandon Carter, Jonas Mueller, Siddhartha Jain, David K. Gifford:
What made you do this? Understanding black-box decisions with sufficient input subsets. 567-576 - Sinong Wang, Jiashang Liu, Ness B. Shroff, Pengyu Yang:
Computation Efficient Coded Linear Transform. 577-585 - Oren Mangoubi, Aaron Smith:
Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators. 586-595 - Changhee Lee, William R. Zame, Ahmed M. Alaa, Mihaela van der Schaar:
Temporal Quilting for Survival Analysis. 596-605 - Mathieu Blondel, André F. T. Martins, Vlad Niculae:
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. 606-615 - Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson:
On Target Shift in Adversarial Domain Adaptation. 616-625 - Sven Schmit, Virag Shah, Ramesh Johari:
Optimal Testing in the Experiment-rich Regime. 626-633 - David A. Roberts, Marcus Gallagher, Thomas Taimre:
Reversible Jump Probabilistic Programming. 634-643 - Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira:
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability. 644-653 - Huijie Feng, Yang Ning:
High-dimensional Mixed Graphical Model with Ordinal Data: Parameter Estimation and Statistical Inference. 654-663 - Akifumi Okuno, Hidetoshi Shimodaira:
Robust Graph Embedding with Noisy Link Weights. 664-673 - Yue Yu, Jiaxiang Wu, Junzhou Huang:
Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. 674-683 - Yuchen Zhang, Percy Liang:
Defending against Whitebox Adversarial Attacks via Randomized Discretization. 684-693 - Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi:
Fisher Information and Natural Gradient Learning in Random Deep Networks. 694-702 - Matthew J. Holland:
Robust descent using smoothed multiplicative noise. 703-711 - Matthew J. Holland:
Classification using margin pursuit. 712-720 - Raef Bassily:
Linear Queries Estimation with Local Differential Privacy. 721-729 - Georgi Dikov, Justin Bayer:
Bayesian Learning of Neural Network Architectures. 730-738 - Raghu Bollapragada, Damien Scieur, Alexandre d'Aspremont:
Nonlinear Acceleration of Primal-Dual Algorithms. 739-747 - Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Gaussian Process Latent Variable Alignment Learning. 748-757 - Juho Lee, Lancelot F. James, Seungjin Choi, Francois Caron:
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure. 758-767 - Gaël Letarte, Emilie Morvant, Pascal Germain:
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. 768-776 - Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yagmur Güçlütürk, Max Hinne, Eric Maris, Marcel van Gerven:
Forward Amortized Inference for Likelihood-Free Variational Marginalization. 777-786 - Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel van Gerven, Eric Maris:
SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity. 787-795 - Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. 796-805 - Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr:
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. 806-815 - Hanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis A. Nunes Amaral:
A new evaluation framework for topic modeling algorithms based on synthetic corpora. 816-826 - Zoltán Szabó, Bharath K. Sriperumbudur:
On Kernel Derivative Approximation with Random Fourier Features. 827-836 - George Papamakarios, David C. Sterratt, Iain Murray:
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows. 837-848 - Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia:
Optimal Transport for Multi-source Domain Adaptation under Target Shift. 849-858 - Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner:
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. 859-868 - Masaaki Imaizumi, Kenji Fukumizu:
Deep Neural Networks Learn Non-Smooth Functions Effectively. 869-878 - Sunipa Dev, Jeff M. Phillips:
Attenuating Bias in Word vectors. 879-887 - Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. 888-896 - Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives. 897-906 - Tengyuan Liang, James Stokes:
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. 907-915 - Zhehui Chen, Xingguo Li, Lin Yang, Jarvis D. Haupt, Tuo Zhao:
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition. 916-925 - Yingru Liu, Dongliang Xie, Xin Wang:
Generalized Boltzmann Machine with Deep Neural Structure. 926-934 - Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. 935-943 - Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Correcting the bias in least squares regression with volume-rescaled sampling. 944-953 - Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru:
Conservative Exploration using Interleaving. 954-963 - Jalil Taghia, Thomas B. Schön:
Conditionally Independent Multiresolution Gaussian Processes. 964-973 - Jean Tarbouriech, Alessandro Lazaric:
Active Exploration in Markov Decision Processes. 974-982 - Xiaoyu Li, Francesco Orabona:
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes. 983-992 - Bingcong Li, Tianyi Chen, Georgios B. Giannakis:
Bandit Online Learning with Unknown Delays. 993-1002 - Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang:
Learning Invariant Representations with Kernel Warping. 1003-1012 - Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
$β^3$-IRT: A New Item Response Model and its Applications. 1013-1021 - Peter Schulam, Suchi Saria:
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning. 1022-1031 - Ryo Karakida, Shotaro Akaho, Shun-ichi Amari:
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach. 1032-1041 - John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse $L_p$-norm Regression With Optimal Probability. 1042-1050 - Marco Mondelli, Andrea Montanari:
On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition. 1051-1060 - Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc:
Autoencoding any Data through Kernel Autoencoders. 1061-1069 - Yifan Wu, Barnabás Póczos, Aarti Singh:
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. 1070-1078 - Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu:
Learning to Optimize under Non-Stationarity. 1079-1087 - Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi:
SPONGE: A generalized eigenproblem for clustering signed networks. 1088-1098 - Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov:
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex. 1099-1109 - Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt:
Are we there yet? Manifold identification of gradient-related proximal methods. 1110-1119 - Jayadev Acharya, Ziteng Sun, Huanyu Zhang:
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. 1120-1129 - Jingyu He, Saar Yalov, P. Richard Hahn:
XBART: Accelerated Bayesian Additive Regression Trees. 1130-1138 - Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick:
A Swiss Army Infinitesimal Jackknife. 1139-1147 - Daniel T. Zhang, Young Hun Jung, Ambuj Tewari:
Online Multiclass Boosting with Bandit Feedback. 1148-1156 - Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan:
Auto-Encoding Total Correlation Explanation. 1157-1166 - Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos:
Towards Efficient Data Valuation Based on the Shapley Value. 1167-1176 - Rafael Oliveira, Lionel Ott, Fabio Ramos:
Bayesian optimisation under uncertain inputs. 1177-1184 - Seyoon Ko, Joong-Ho Won:
Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. 1185-1194 - Sharan Vaswani, Francis R. Bach, Mark Schmidt:
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. 1195-1204 - Tasuku Soma:
No-regret algorithms for online k-submodular maximization. 1205-1214 - Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Amir Salman Avestimehr:
Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy. 1215-1225 - Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Renyi Differential Privacy and Analytical Moments Accountant. 1226-1235 - Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré:
Model Consistency for Learning with Mirror-Stratifiable Regularizers. 1236-1244 - Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban:
From Cost-Sensitive to Tight F-measure Bounds. 1245-1253 - Shunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano:
Feature subset selection for the multinomial logit model via mixed-integer optimization. 1254-1263 - Jian Zhang, Avner May, Tri Dao, Christopher Ré:
Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation. 1264-1274 - Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta:
Restarting Frank-Wolfe. 1275-1283 - Hiroshi Inoue:
Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level. 1284-1293 - Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc:
Infinite Task Learning in RKHSs. 1294-1302 - Quentin Berthet, Jordan S. Ellenberg:
Detection of Planted Solutions for Flat Satisfiability Problems. 1303-1312 - Kayvan Sadeghi, Alessandro Rinaldo:
Markov Properties of Discrete Determinantal Point Processes. 1313-1321 - Alihan Hüyük, Cem Tekin:
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms. 1322-1330 - Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg:
Distilling Policy Distillation. 1331-1340 - Clarice Poon, Nicolas Keriven, Gabriel Peyré:
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. 1341-1350 - Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann:
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. 1351-1360 - Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. 1361-1369 - Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani:
A Continuous-Time View of Early Stopping for Least Squares Regression. 1370-1378 - Dan Kushnir, Shirin Jalali, Iraj Saniee:
Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time. 1379-1387 - Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara:
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling. 1388-1397 - Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning Rules-First Classifiers. 1398-1406 - Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Wasserstein regularization for sparse multi-task regression. 1407-1416 - Atsushi Nitanda, Taiji Suzuki:
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. 1417-1426 - Anthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos:
Black Box Quantiles for Kernel Learning. 1427-1437 - Gilles Louppe, Joeri Hermans, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. 1438-1447 - Filip de Roos, Philipp Hennig:
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. 1448-1457 - Kfir Y. Levy, Andreas Krause:
Projection Free Online Learning over Smooth Sets. 1458-1466 - Tongfei Chen, Jirí Navrátil, Vijay S. Iyengar, Karthikeyan Shanmugam:
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. 1467-1475 - Ming Yu, Varun Gupta, Mladen Kolar:
Learning Influence-Receptivity Network Structure with Guarantee. 1476-1485 - Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi:
Iterative Bayesian Learning for Crowdsourced Regression. 1486-1495 - Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi:
Nonconvex Matrix Factorization from Rank-One Measurements. 1496-1505 - Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober:
Fast and Robust Shortest Paths on Manifolds Learned from Data. 1506-1515 - Peter O'Connor, Efstratios Gavves, Max Welling:
Training a Spiking Neural Network with Equilibrium Propagation. 1516-1523 - Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. 1524-1534 - Matias I. Müller, Cristian R. Rojas:
Gain estimation of linear dynamical systems using Thompson Sampling. 1535-1543 - Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen:
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit. 1544-1553 - Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone:
Calibrating Deep Convolutional Gaussian Processes. 1554-1563 - Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
Stochastic algorithms with descent guarantees for ICA. 1564-1573 - Aude Genevay, Lénaïc Chizat, Francis R. Bach, Marco Cuturi, Gabriel Peyré:
Sample Complexity of Sinkhorn Divergences. 1574-1583 - Yanzhi Chen, Michael U. Gutmann:
Adaptive Gaussian Copula ABC. 1584-1592 - Julian Katz-Samuels, Clayton Scott:
Top Feasible Arm Identification. 1593-1601 - Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo:
Direct Acceleration of SAGA using Sampled Negative Momentum. 1602-1610 - Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov:
Does data interpolation contradict statistical optimality? 1611-1619 - Charlie Nash, Nate Kushman, Christopher K. I. Williams:
Inverting Supervised Representations with Autoregressive Neural Density Models. 1620-1629 - Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. 1630-1639 - Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka:
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. 1640-1649 - Adrian Rivera Cardoso, Rachel Cummings:
Differentially Private Online Submodular Minimization. 1650-1658 - Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas:
Semi-supervised clustering for de-duplication. 1659-1667 - Pierre Perrault, Vianney Perchet, Michal Valko:
Finding the bandit in a graph: Sequential search-and-stop. 1668-1677 - Steve Hanneke, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. 1678-1686 - Ralf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa, Mikko Koivisto:
On Structure Priors for Learning Bayesian Networks. 1687-1695 - Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. 1696-1703 - Alexander F. Lapanowski, Irina Gaynanova:
Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring. 1704-1713 - Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani:
Learning Natural Programs from a Few Examples in Real-Time. 1714-1722 - Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots:
Truncated Back-propagation for Bilevel Optimization. 1723-1732 - Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. 1733-1742 - Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari:
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. 1743-1752 - Ondrej Kuzelka, Vyacheslav Kungurtsev:
Lifted Weight Learning of Markov Logic Networks Revisited. 1753-1761 - Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang:
Causal Discovery in the Presence of Missing Data. 1762-1770 - Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate:
Learning Tree Structures from Noisy Data. 1771-1782 - Andrea Locatelli, Alexandra Carpentier, Michal Valko:
Active multiple matrix completion with adaptive confidence sets. 1783-1791 - Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha P. Talukdar:
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. 1792-1801 - Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas:
Negative Momentum for Improved Game Dynamics. 1802-1811 - Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Deep learning with differential Gaussian process flows. 1812-1821 - Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. 1822-1831 - Hyunghoon Cho, Benjamin Demeo, Jian Peng, Bonnie Berger:
Large-Margin Classification in Hyperbolic Space. 1832-1840 - Pei Wang, Pushpi Paranamana, Patrick Shafto:
Generalizing the theory of cooperative inference. 1841-1850 - Stephen Pasteris, Fabio Vitale, Kevin S. Chan, Shiqiang Wang, Mark Herbster:
MaxHedge: Maximizing a Maximum Online. 1851-1859 - James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner:
The Gaussian Process Autoregressive Regression Model (GPAR). 1860-1869 - David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola:
Towards Optimal Transport with Global Invariances. 1870-1879 - Edouard Grave, Armand Joulin, Quentin Berthet:
Unsupervised Alignment of Embeddings with Wasserstein Procrustes. 1880-1890 - Onur Atan, William R. Zame, Mihaela van der Schaar:
Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials. 1891-1900 - Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski:
Probabilistic Forecasting with Spline Quantile Function RNNs. 1901-1910 - Sudeep Raja Putta, Abhishek Shetty:
Exponential Weights on the Hypercube in Polynomial Time. 1911-1919 - Alex Nowak-Vila, Francis R. Bach, Alessandro Rudi:
Sharp Analysis of Learning with Discrete Losses. 1920-1929 - Nikhil Garg, Ramesh Johari:
Designing Optimal Binary Rating Systems. 1930-1939 - Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. 1940-1949 - Weihao Gao, Ashok Vardhan Makkuva, Sewoong Oh, Pramod Viswanath:
Learning One-hidden-layer Neural Networks under General Input Distributions. 1950-1959 - Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. 1960-1968 - Mauricio A. Álvarez, Wil O. C. Ward, Cristian Guarnizo:
Non-linear process convolutions for multi-output Gaussian processes. 1969-1977 - Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Pratim Talukdar:
Lovasz Convolutional Networks. 1978-1987 - Rémy Degenne, Thomas Nedelec, Clément Calauzènes, Vianney Perchet:
Bridging the gap between regret minimization and best arm identification, with application to A/B tests. 1988-1996 - Andrés F. López-Lopera, S. T. John, Nicolas Durrande:
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. 1997-2006 - Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos:
Implicit Kernel Learning. 2007-2016 - Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause:
Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature. 2017-2027 - Jason Pacheco, John W. Fisher III:
Variational Information Planning for Sequential Decision Making. 2028-2036 - Chen Chen, Jaewoo Lee, Dan Kifer:
Renyi Differentially Private ERM for Smooth Objectives. 2037-2046 - Lin Chen, Mingrui Zhang, Amin Karbasi:
Projection-Free Bandit Convex Optimization. 2047-2056 - Francesco Croce, Maksym Andriushchenko, Matthias Hein:
Provable Robustness of ReLU networks via Maximization of Linear Regions. 2057-2066 - Jayadev Acharya, Clément L. Canonne, Cody Freitag, Himanshu Tyagi:
Test without Trust: Optimal Locally Private Distribution Testing. 2067-2076 - Mehrdad Ghadiri, Mark Schmidt:
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. 2077-2086 - Amit Deshpande, Anand Louis, Apoorv Vikram Singh:
On Euclidean k-Means Clustering with alpha-Center Proximity. 2087-2095 - Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai:
Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach. 2096-2105 - Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour:
Safe Convex Learning under Uncertain Constraints. 2106-2114 - Noureddine El Karoui, Elizabeth Purdom:
The non-parametric bootstrap and spectral analysis in moderate and high-dimension. 2115-2124 - Jaime Roquero Gimenez, Amirata Ghorbani, James Y. Zou:
Knockoffs for the Mass: New Feature Importance Statistics with False Discovery Guarantees. 2125-2133 - Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon:
Training Variational Autoencoders with Buffered Stochastic Variational Inference. 2134-2143 - Xavier Fontaine, Quentin Berthet, Vianney Perchet:
Regularized Contextual Bandits. 2144-2153 - Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone:
Risk-Sensitive Generative Adversarial Imitation Learning. 2154-2163 - Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. 2164-2173 - Alex Bird, Christopher K. I. Williams, Christopher Hawthorne:
Multi-Task Time Series Analysis applied to Drug Response Modelling. 2174-2183 - Jaime Roquero Gimenez, James Y. Zou:
Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization. 2184-2192 - Arno Solin, Manon Kok:
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features. 2193-2202 - Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. 2203-2211 - Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Matroids, Matchings, and Fairness. 2212-2220 - Wojciech Tarnowski, Piotr Warchol, Stanislaw Jastrzebski, Jacek Tabor, Maciej A. Nowak:
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function. 2221-2230 - Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. 2231-2240 - Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvitskii:
Consistent Online Optimization: Convex and Submodular. 2241-2250 - Zelda Mariet, Mike Gartrell, Suvrit Sra:
Learning Determinantal Point Processes by Corrective Negative Sampling. 2251-2260 - Emilien Dupont, Suhas Suresha:
Probabilistic Semantic Inpainting with Pixel Constrained CNNs. 2261-2270 - Sun Sun, Yaoliang Yu:
Least Squares Estimation of Weakly Convex Functions. 2271-2280 - Nathan Kallus, Xiaojie Mao, Angela Zhou:
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. 2281-2290 - Linfeng Liu, Liping Liu:
Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. 2291-2300 - Rui Xie, Zengyan Wang, Shuyang Bai, Ping Ma, Wenxuan Zhong:
Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series. 2301-2311 - Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar:
Interpretable Cascade Classifiers with Abstention. 2312-2320 - Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. 2321-2330 - Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
Revisiting Adversarial Risk. 2331-2339 - Rishabh K. Iyer, Jeffrey A. Bilmes:
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems. 2340-2349 - Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis:
Bernoulli Race Particle Filters. 2350-2358 - Kaspar Märtens, Michalis K. Titsias, Christopher Yau:
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. 2359-2367 - Anton Mallasto, Søren Hauberg, Aasa Feragen:
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. 2368-2377 - Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob:
Unbiased Smoothing using Particle Independent Metropolis-Hastings. 2378-2387 - Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan:
Two-temperature logistic regression based on the Tsallis divergence. 2388-2396 - Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei:
Avoiding Latent Variable Collapse with Generative Skip Models. 2397-2405 - Fan Bu, Sonia Xu, Katherine A. Heller, Alexander Volfovsky:
SMOGS: Social Network Metrics of Game Success. 2406-2414 - Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski:
Fast Algorithms for Sparse Reduced-Rank Regression. 2415-2424 - Hilal Asi, John C. Duchi:
Modeling simple structures and geometry for better stochastic optimization algorithms. 2425-2434 - Anshuka Rangi, Massimo Franceschetti:
Online learning with feedback graphs and switching costs. 2435-2444 - Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost-Exact Matching for Causal Inference. 2445-2453 - Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed:
Statistical Optimal Transport via Factored Couplings. 2454-2465 - I (Eli) Chien, Huozhi Zhou, Pan Li:
HS2: Active learning over hypergraphs with pointwise and pairwise queries. 2466-2475 - Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba E. Ba:
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. 2476-2484 - Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie:
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. 2485-2494 - Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu:
An Optimal Control Approach to Sequential Machine Teaching. 2495-2503 - Gautam Goel, Adam Wierman:
An Online Algorithm for Smoothed Regression and LQR Control. 2504-2513 - Aditya Grover, Stefano Ermon:
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. 2514-2524 - Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Structured Disentangled Representations. 2525-2534 - Benjamin Mark, Garvesh Raskutti, Rebecca Willett:
Estimating Network Structure from Incomplete Event Data. 2535-2544 - Marco Gaboardi, Ryan Rogers, Or Sheffet:
Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals. 2545-2554 - Takeru Matsuda, Aapo Hyvärinen:
Estimation of Non-Normalized Mixture Models. 2555-2563 - Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko:
Rotting bandits are no harder than stochastic ones. 2564-2572 - Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang:
A Topological Regularizer for Classifiers via Persistent Homology. 2573-2582 - Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag:
Overcomplete Independent Component Analysis via SDP. 2583-2592 - Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry P. Vetrov:
Doubly Semi-Implicit Variational Inference. 2593-2602 - Nicole Mücke:
Reducing training time by efficient localized kernel regression. 2603-2610 - Shandian Zhe, Wei W. Xing, Robert M. Kirby:
Scalable High-Order Gaussian Process Regression. 2611-2620 - Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani:
A Higher-Order Kolmogorov-Smirnov Test. 2621-2630 - Kelvin Hsu, Fabio Ramos:
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference. 2631-2640 - Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. 2641-2649 - Théophane Weber, Nicolas Heess, Lars Buesing, David Silver:
Credit Assignment Techniques in Stochastic Computation Graphs. 2650-2660 - Anders Kirk Uhrenholt, Bjørn Sand Jensen:
Efficient Bayesian Optimization for Target Vector Estimation. 2661-2670 - Hsiang Hsu, Salman Salamatian, Flávio P. Calmon:
Correspondence Analysis Using Neural Networks. 2671-2680 - Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré:
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences. 2681-2690 - Rafael M. Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner:
Multi-Observation Regression. 2691-2700 - Kiárash Shaloudegi, András György:
Adaptive MCMC via Combining Local Samplers. 2701-2710 - Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson:
Variance reduction properties of the reparameterization trick. 2711-2720 - Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh, Grigory Yaroslavtsev:
Hierarchical Clustering for Euclidean Data. 2721-2730 - Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. 2731-2740 - Benjamin Rhodes, Michael U. Gutmann:
Variational Noise-Contrastive Estimation. 2741-2750 - Henry R. Chai, Roman Garnett:
Improving Quadrature for Constrained Integrands. 2751-2759 - Ying Zhu, Zhuqing Yu, Guang Cheng:
High Dimensional Inference in Partially Linear Models. 2760-2769 - Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama:
Cost aware Inference for IoT Devices. 2770-2779 - Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman:
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. 2780-2789 - Lai Tian, Feiping Nie, Xuelong Li:
A Unified Weight Learning Paradigm for Multi-view Learning. 2790-2800 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Region-Based Active Learning. 2801-2809 - Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou:
Precision Matrix Estimation with Noisy and Missing Data. 2810-2819 - Wenbo Ren, Jia Liu, Ness B. Shroff:
Exploring k out of Top $ρ$ Fraction of Arms in Stochastic Bandits. 2820-2828 - Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu:
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI. 2829-2838 - Veronika Rocková, Enakshi Saha:
On Theory for BART. 2839-2848 - Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai:
Deep Topic Models for Multi-label Learning. 2849-2857 - Thanh Van Nguyen, Raymond K. W. Wong, Chinmay Hegde:
On the Dynamics of Gradient Descent for Autoencoders. 2858-2867 - Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian:
Complexities in Projection-Free Stochastic Non-convex Minimization. 2868-2876 - Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. 2877-2886 - Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. 2887-2896 - Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian:
Decentralized Gradient Tracking for Continuous DR-Submodular Maximization. 2897-2906 - Craig Kelly, Somdeb Sarkhel, Deepak Venugopal:
Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models. 2907-2915 - Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright:
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. 2916-2925 - Anirudh Vemula, Wen Sun, J. Andrew Bagnell:
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. 2926-2935 - Difan Zou, Pan Xu, Quanquan Gu:
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. 2936-2945 - Mingming Sun, Ping Li:
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation. 2946-2955 - Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy:
Imitation-Regularized Offline Learning. 2956-2965 - Tamara Fernandez, Arthur Gretton:
A maximum-mean-discrepancy goodness-of-fit test for censored data. 2966-2975 - Youssef Mroueh, Tom Sercu, Anant Raj:
Sobolev Descent. 2976-2985 - Daniel Malinsky, Peter Spirtes:
Learning the Structure of a Nonstationary Vector Autoregression. 2986-2994 - Tadashi Kozuno, Eiji Uchibe, Kenji Doya:
Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning. 2995-3003 - Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh:
A Fast Sampling Algorithm for Maximum Inner Product Search. 3004-3012 - Byoungwook Jang, Alfred O. Hero III:
Minimum Volume Topic Modeling. 3013-3021 - Xuhui Fan, Bin Li, Scott A. Sisson:
Binary Space Partitioning Forest. 3022-3031 - Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi:
Improved Semi-Supervised Learning with Multiple Graphs. 3032-3041 - Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis:
Optimizing over a Restricted Policy Class in MDPs. 3042-3050 - Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry:
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. 3051-3059 - Payam Delgosha, Naveen Goela:
Deep Switch Networks for Generating Discrete Data and Language. 3060-3069 - Anand Ramachandran, Steven S. Lumetta, Eric W. Klee, Deming Chen:
A recurrent Markov state-space generative model for sequences. 3070-3079 - Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson:
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. 3080-3088 - Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao:
Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. 3089-3098 - Daniel LeJeune, Reinhard Heckel, Richard G. Baraniuk:
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. 3099-3107 - Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári:
Model-Free Linear Quadratic Control via Reduction to Expert Prediction. 3108-3117 - Adarsh Subbaswamy, Peter Schulam, Suchi Saria:
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. 3118-3127 - Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico:
Structured Robust Submodular Maximization: Offline and Online Algorithms. 3128-3137 - Lionel Blondé, Alexandros Kalousis:
Sample-Efficient Imitation Learning via Generative Adversarial Nets. 3138-3148 - Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. 3149-3157 - Jialin Song, Yuxin Chen, Yisong Yue:
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes. 3158-3167 - Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Online Algorithm for Unsupervised Sensor Selection. 3168-3176 - Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett:
Best of many worlds: Robust model selection for online supervised learning. 3177-3186 - Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Accelerating Imitation Learning with Predictive Models. 3187-3196 - Sayak Ray Chowdhury, Aditya Gopalan:
Online Learning in Kernelized Markov Decision Processes. 3197-3205 - Christos Thrampoulidis, Ankit Singh Rawat:
Lifting high-dimensional non-linear models with Gaussian regressors. 3206-3215 - Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla:
Domain-Size Aware Markov Logic Networks. 3216-3224 - Osman Emre Dai, Daniel Cullina, Negar Kiyavash:
Database Alignment with Gaussian Features. 3225-3233 - Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler:
Size of Interventional Markov Equivalence Classes in random DAG models. 3234-3243 - Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré:
Reparameterizing Distributions on Lie Groups. 3244-3253 - Xiangru Lian, Ji Liu:
Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization. 3254-3263 - Qimao Yang, Changrong Li, Jun Guo:
Multi-Order Information for Working Set Selection of Sequential Minimal Optimization. 3264-3272 - Zheyang Shen, Markus Heinonen, Samuel Kaski:
Harmonizable mixture kernels with variational Fourier features. 3273-3282 - Shubhanshu Shekhar, Tara Javidi:
Multiscale Gaussian Process Level Set Estimation. 3283-3291 - Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson:
The LORACs Prior for VAEs: Letting the Trees Speak for the Data. 3292-3301 - Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin:
Adversarial Learning of a Sampler Based on an Unnormalized Distribution. 3302-3311 - Aadirupa Saha, Aditya Gopalan:
Active Ranking with Subset-wise Preferences. 3312-3321 - Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang:
Recovery Guarantees For Quadratic Tensors With Sparse Observations. 3322-3332 - Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton:
Sample Efficient Graph-Based Optimization with Noisy Observations. 3333-3341 - Heinrich Jiang, Jennifer Jang, Ofir Nachum:
Robustness Guarantees for Density Clustering. 3342-3351 - Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, Jeff A. Bilmes:
Fixing Mini-batch Sequences with Hierarchical Robust Partitioning. 3352-3361 - Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau:
Multitask Metric Learning: Theory and Algorithm. 3362-3371 - Daniel Andrade, Yuzuru Okajima:
Efficient Bayes Risk Estimation for Cost-Sensitive Classification. 3372-3381 - Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo:
Interpreting Black Box Predictions using Fisher Kernels. 3382-3390 - Sephora Madjiheurem, Laura Toni:
Representation Learning on Graphs: A Reinforcement Learning Application. 3391-3399 - Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler:
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. 3400-3409 - Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue:
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design. 3410-3419 - Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry:
Convergence of Gradient Descent on Separable Data. 3420-3428 - Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent:
Structured Neural Topic Models for Reviews. 3429-3439 - Kohei Miyaguchi, Kenji Yamanishi:
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. 3440-3448 - Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. 3449-3458 - Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön:
Evaluating model calibration in classification. 3459-3467 - Anit Kumar Sahu, Manzil Zaheer, Soummya Kar:
Towards Gradient Free and Projection Free Stochastic Optimization. 3468-3477 - Alexander D'Amour:
On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative. 3478-3486 - Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Data-Driven Approach to Multiple-Source Domain Adaptation. 3487-3496
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