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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