Proceedings of Machine Learning Research

Proceedings of Machine Learning Research
The Geometry of Random Features
Krzysztof Choromanski, Mark Rowland, Tamas Sarlos, Vikas Sindhwani, Richard Turner, Adrian Weller ; PMLR 84:1-9
Gauged Mini-Bucket Elimination for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller ; PMLR 84:10-19
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt ; PMLR 84:20-28
An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland, Marc Bellemare, Will Dabney, Remi Munos, Yee Whye Teh ; PMLR 84:29-37
Combinatorial Preconditioners for Proximal Algorithms on Graphs
Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers ; PMLR 84:38-47
Growth-Optimal Portfolio Selection under CVaR Constraints
Guy Uziel, Ran El-Yaniv ; PMLR 84:48-57
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Satoshi Hara, Kohei Hayashi ; PMLR 84:77-85
Mixed Membership Word Embeddings for Computational Social Science
James Foulds ; PMLR 84:86-95
Fast Threshold Tests for Detecting Discrimination
Emma Pierson, Sam Corbett-Davies, Sharad Goel ; PMLR 84:96-105
Iterative Spectral Method for Alternative Clustering
Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli, Jennifer Dy ; PMLR 84:115-123
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means
Dennis Forster, Jörg Lücke ; PMLR 84:124-132
Parallelised Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos ; PMLR 84:133-142
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul Krishnan, Dawen Liang, Matthew Hoffman ; PMLR 84:143-151
Post Selection Inference with Kernels
Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi ; PMLR 84:152-160
On how complexity affects the stability of a predictor
Joel Ratsaby ; PMLR 84:161-167
On Truly Block Eigensolvers via Riemannian Optimization
Zhiqiang Xu, Xin Gao ; PMLR 84:168-177
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo, Kaan Kara, Ce Zhang ; PMLR 84:178-187
IHT dies hard: Provable accelerated Iterative Hard Thresholding
Rajiv Khanna, Anastasios Kyrillidis ; PMLR 84:188-198
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
Jinshan ZENG, Ke Ma, Yuan Yao ; PMLR 84:199-207
Outlier Detection and Robust Estimation in Nonparametric Regression
Dehan Kong, Howard Bondell, Weining Shen ; PMLR 84:208-216
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis
Luca Ambrogioni, Eric Maris ; PMLR 84:217-225
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling
Gabriele Abbati, Alessandra Tosi, Michael Osborne, Seth Flaxman ; PMLR 84:226-234
Online Learning with Non-Convex Losses and Non-Stationary Regret
Xiand Gao, Xiaobo Li, Shuzhong Zhang ; PMLR 84:235-243
Learning Determinantal Point Processes in Sublinear Time
Christophe Dupuy, Francis Bach ; PMLR 84:244-257
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding
Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang ; PMLR 84:258-268
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra ; PMLR 84:269-278
Online Boosting Algorithms for Multi-label Ranking
Young Hun Jung, Ambuj Tewari ; PMLR 84:279-287
Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred Hero ; PMLR 84:288-297
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher ; PMLR 84:298-307
Stochastic Multi-armed Bandits in Constant Space
David Liau, Zhao Song, Eric Price, Ger Yang ; PMLR 84:386-394
Matrix completability analysis via graph k-connectivity
Dehua Cheng, Natali Ruchansky, Yan Liu ; PMLR 84:395-403
FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Xiang Cheng, Fred Roosta, Stefan Palombo, Peter Bartlett, Michael Mahoney ; PMLR 84:404-414
Multi-view Metric Learning in Vector-valued Kernel Spaces
Riikka Huusari, Hachem Kadri, Cécile Capponi ; PMLR 84:415-424
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
William Herlands, Edward McFowland, Andrew Wilson, Daniel Neill ; PMLR 84:425-434
Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino, Rene Vidal ; PMLR 84:435-444
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
Tianbao Yang, Zhe Li, Lijun Zhang ; PMLR 84:445-453
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa ; PMLR 84:454-463
Boosting Variational Inference: an Optimization Perspective
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Ratsch ; PMLR 84:464-472
Personalized and Private Peer-to-Peer Machine Learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi ; PMLR 84:473-481
Tensor Regression Meets Gaussian Processes
Rose Yu, Guangyu Li, Yan Liu ; PMLR 84:482-490
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization
Emanuel Laude, Tao Wu, Daniel Cremers ; PMLR 84:491-499
Medoids in Almost-Linear Time via Multi-Armed Bandits
Vivek Bagaria, Govinda Kamath, Vasilis Ntranos, Martin Zhang, David Tse ; PMLR 84:500-509
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization
Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh ; PMLR 84:538-547
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods
Stephan Clémençon, François Portier ; PMLR 84:548-556
Group invariance principles for causal generative models
Michel Besserve, naji Shajarisales, Bernhard Schoelkopf, Dominik Janzing ; PMLR 84:557-565
A Provable Algorithm for Learning Interpretable Scoring Systems
Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker ; PMLR 84:566-574
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Hyunjik Kim, Yee Whye Teh ; PMLR 84:575-584
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams
Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato ; PMLR 84:585-594
Transfer Learning on fMRI Datasets
Hejia Zhang, Po-Hsuan Chen, Peter Ramadge ; PMLR 84:595-603
An Optimization Approach to Learning Falling Rule Lists
Chaofan Chen, Cynthia Rudin ; PMLR 84:604-612
Catalyst for Gradient-based Nonconvex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui ; PMLR 84:613-622
Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin ; PMLR 84:623-631
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman ; PMLR 84:689-697
Variational inference for the multi-armed contextual bandit
Iñigo Urteaga, Chris Wiggins ; PMLR 84:698-706
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Robert Gower, Nicolas Le Roux, Francis Bach ; PMLR 84:707-715
Subsampling for Ridge Regression via Regularized Volume Sampling
Michal Derezinski, Manfred Warmuth ; PMLR 84:716-725
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov ; PMLR 84:726-735
Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer ; PMLR 84:736-744
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka ; PMLR 84:745-754
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
Feras Saad, Vikash Mansinghka ; PMLR 84:755-764
Stochastic Three-Composite Convex Minimization with a Linear Operator
Renbo Zhao, Volkan Cevher ; PMLR 84:765-774
Direct Learning to Rank And Rerank
Cynthia Rudin, Yining Wang ; PMLR 84:775-783
One-shot Coresets: The Case of k-Clustering
Olivier Bachem, Mario Lucic, Silvio Lattanzi ; PMLR 84:784-792
Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock ; PMLR 84:793-802
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar ; PMLR 84:803-812
Variational Inference based on Robust Divergences
Futoshi Futami, Issei Sato, Masashi Sugiyama ; PMLR 84:813-822
[ abs ] [ Download PDF ][ Supplementary PDF ]
Best arm identification in multi-armed bandits with delayed feedback
Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon ; PMLR 84:833-842
A fully adaptive algorithm for pure exploration in linear bandits
Liyuan Xu, Junya Honda, Masashi Sugiyama ; PMLR 84:843-851
Contextual Bandits with Stochastic Experts
Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai ; PMLR 84:852-861
Human Interaction with Recommendation Systems
Sven Schmit, Carlos Riquelme ; PMLR 84:862-870
Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms
I Chien, Chung-Yi Lin, I-Hsiang Wang ; PMLR 84:871-879
Smooth and Sparse Optimal Transport
Mathieu Blondel, Vivien Seguy, Antoine Rolet ; PMLR 84:880-889
Robust Maximization of Non-Submodular Objectives
Ilija Bogunovic, Junyao Zhao, Volkan Cevher ; PMLR 84:890-899
Cause-Effect Inference by Comparing Regression Errors
Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schoelkopf ; PMLR 84:900-909
Tree-based Bayesian Mixture Model for Competing Risks
Alexis Bellot, Mihaela Schaar ; PMLR 84:910-918
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games
Julien Perolat, Bilal Piot, Olivier Pietquin ; PMLR 84:919-928
Random Subspace with Trees for Feature Selection Under Memory Constraints
Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts ; PMLR 84:929-937
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Jakob Runge ; PMLR 84:938-947
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos ; PMLR 84:948-957
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach
Achintya Kundu, Francis Bach, Chiranjib Bhattacharya ; PMLR 84:958-967
Statistically Efficient Estimation for Non-Smooth Probability Densities
Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida ; PMLR 84:978-987
SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning
Xu Hu, Guillaume Obozinski ; PMLR 84:988-997
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon ; PMLR 84:998-1007
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
Atsushi Nitanda, Taiji Suzuki ; PMLR 84:1008-1016
Statistical Sparse Online Regression: A Diffusion Approximation Perspective
Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun ; PMLR 84:1017-1026
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida ; PMLR 84:1027-1036
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Lawrence Murray, Daniel Lundën, Jan Kudlicka, David Broman, Thomas Schön ; PMLR 84:1037-1046
Learning to Round for Discrete Labeling Problems
Pritish Mohapatra, Jawahar C.V., M Pawan Kumar ; PMLR 84:1047-1056
Approximate ranking from pairwise comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin Wainwright ; PMLR 84:1057-1066
Learning Structural Weight Uncertainty for Sequential Decision-Making
Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin ; PMLR 84:1137-1146
Towards Memory-Friendly Deterministic Incremental Gradient Method
Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang ; PMLR 84:1147-1156
Alpha-expansion is Exact on Stable Instances
Hunter Lang, David Sontag, Aravindan Vijayaraghavan ; PMLR 84:1157-1166
Bayesian Approaches to Distribution Regression
Ho Chung Leon Law, Dougal Sutherland, Dino Sejdinovic, Seth Flaxman ; PMLR 84:1167-1176
Submodularity on Hypergraphs: From Sets to Sequences
Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi ; PMLR 84:1177-1184
Provable Estimation of the Number of Blocks in Block Models
Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng ; PMLR 84:1185-1194
Differentially Private Regression with Gaussian Processes
Michael Smith, Mauricio Álvarez, Max Zwiessele, Neil Lawrence ; PMLR 84:1195-1203
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
Sai Praneeth Reddy Karimireddy, Sebastian Stich, Martin Jaggi ; PMLR 84:1204-1213
Robustness of classifiers to uniform $\ell_p$ and Gaussian noise
Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi ; PMLR 84:1280-1288
Nested CRP with Hawkes-Gaussian Processes
Xi Tan, Vinayak Rao, Jennifer Neville ; PMLR 84:1289-1298
Sketching for Kronecker Product Regression and P-splines
Huaian Diao, Zhao Song, Wen Sun, David Woodruff ; PMLR 84:1299-1308
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Ardavan Saeedi, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson, Ryan Adams ; PMLR 84:1309-1317
Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams
Chris Hickey, Graham Cormode ; PMLR 84:1318-1326
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning
Shashank Singh, Barnabas Poczos, Jian Ma ; PMLR 84:1327-1336
Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?
Chandrashekar Lakshminarayanan, Csaba Szepesvari ; PMLR 84:1347-1355
Stochastic Zeroth-order Optimization in High Dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh ; PMLR 84:1356-1365
Teacher Improves Learning by Selecting a Training Subset
Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu ; PMLR 84:1366-1375
[ abs ] [ Download PDF ][ Supplementary PDF ]
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick, Sang-Yun Oh ; PMLR 84:1376-1386
Robust Vertex Enumeration for Convex Hulls in High Dimensions
Pranjal Awasthi, Bahman Kalantari, Yikai Zhang ; PMLR 84:1387-1396
Fast generalization error bound of deep learning from a kernel perspective
Taiji Suzuki ; PMLR 84:1397-1406
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob Gardner, Geoff Pleiss, Ruihan Wu, Kilian Weinberger, Andrew Wilson ; PMLR 84:1407-1416
Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation
Mohammadreza Soltani, Chinmay Hegde ; PMLR 84:1417-1426
Scalable Generalized Dynamic Topic Models
Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt ; PMLR 84:1427-1435
Bayesian Structure Learning for Dynamic Brain Connectivity
Michael Andersen, Ole Winther, Lars Kai Hansen, Russell Poldrack, Oluwasanmi Koyejo ; PMLR 84:1436-1446
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro ; PMLR 84:1447-1455
Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien ; PMLR 84:1456-1465
Learning linear structural equation models in polynomial time and sample complexity
Asish Ghoshal, Jean Honorio ; PMLR 84:1466-1475
Convergence diagnostics for stochastic gradient descent with constant learning rate
Jerry Chee, Panos Toulis ; PMLR 84:1476-1485
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio ; PMLR 84:1486-1494
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization
Seung-Jean Kim, Johan Lim, Joong-Ho Won ; PMLR 84:1495-1504
Stochastic algorithms for entropy-regularized optimal transport problems
Brahim Khalil Abid, Robert Gower ; PMLR 84:1505-1512
Plug-in Estimators for Conditional Expectations and Probabilities
Steffen Grunewalder ; PMLR 84:1513-1521
Factorized Recurrent Neural Architectures for Longer Range Dependence
Francois Belletti, Alex Beutel, Sagar Jain, Ed Chi ; PMLR 84:1522-1530
On the Statistical Efficiency of Compositional Nonparametric Prediction
Yixi Xu, Jean Honorio, Xiao Wang ; PMLR 84:1531-1539
Metrics for Deep Generative Models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick Smagt ; PMLR 84:1540-1550
Combinatorial Penalties: Which structures are preserved by convex relaxations?
Marwa El Halabi, Francis Bach, Volkan Cevher ; PMLR 84:1551-1560
Generalized Binary Search For Split-Neighborly Problems
Stephen Mussmann, Percy Liang ; PMLR 84:1561-1569
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
Jussi Viinikka, Ralf Eggeling, Mikko Koivisto ; PMLR 84:1570-1578
On Statistical Optimality of Variational Bayes
Debdeep Pati, Anirban Bhattacharya, Yun Yang ; PMLR 84:1579-1588
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems
Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu ; PMLR 84:1589-1598
Online Regression with Partial Information: Generalization and Linear Projection
Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi ; PMLR 84:1599-1607
Learning Generative Models with Sinkhorn Divergences
Aude Genevay, Gabriel Peyre, Marco Cuturi ; PMLR 84:1608-1617
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott Linderman, Gonzalo Mena, Hal Cooper, Liam Paninski, John Cunningham ; PMLR 84:1618-1627
Achieving the time of 1-NN, but the accuracy of k-NN
Lirong Xue, Samory Kpotufe ; PMLR 84:1628-1636
Efficient Weight Learning in High-Dimensional Untied MLNs
Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal ; PMLR 84:1637-1645
Learning with Complex Loss Functions and Constraints
Harikrishna Narasimhan ; PMLR 84:1646-1654
Solving lp-norm regularization with tensor kernels
Saverio Salzo, Lorenzo Rosasco, Johan Suykens ; PMLR 84:1655-1663
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Omer Gottesman, Weiwei Pan, Finale Doshi-Velez ; PMLR 84:1664-1672
Multi-objective Contextual Bandit Problem with Similarity Information
Eralp Turgay, Doruk Oner, Cem Tekin ; PMLR 84:1673-1681
Turing: Composable inference for probabilistic programming
Hong Ge, Kai Xu, Zoubin Ghahramani ; PMLR 84:1682-1690
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Beilun Wang, arshdeep Sekhon, Yanjun Qi ; PMLR 84:1691-1700
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Sanket Kamthe, Marc Deisenroth ; PMLR 84:1701-1710
Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy
Bai Jiang ; PMLR 84:1711-1721
Practical Bayesian optimization in the presence of outliers
Ruben Martinez-Cantin, Kevin Tee, Michael McCourt ; PMLR 84:1722-1731
Competing with Automata-based Expert Sequences
Mehryar Mohri, Scott Yang ; PMLR 84:1732-1740
Reducing Crowdsourcing to Graphon Estimation, Statistically
Devavrat Shah, Christina Lee ; PMLR 84:1741-1750
Graphical Models for Non-Negative Data Using Generalized Score Matching
Shiqing Yu, Mathias Drton, Ali Shojaie ; PMLR 84:1781-1790
Asynchronous Doubly Stochastic Group Regularized Learning
Bin Gu, Zhouyuan Huo, Heng Huang ; PMLR 84:1791-1800
Convergence of Value Aggregation for Imitation Learning
Ching-An Cheng, Byron Boots ; PMLR 84:1801-1809
Inference in Sparse Graphs with Pairwise Measurements and Side Information
Dylan Foster, Karthik Sridharan, Daniel Reichman ; PMLR 84:1810-1818
Parallel and Distributed MCMC via Shepherding Distributions
Arkabandhu Chowdhury, Christopher Jermaine ; PMLR 84:1819-1827
The Power Mean Laplacian for Multilayer Graph Clustering
Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein ; PMLR 84:1828-1838
Adaptive Sampling for Coarse Ranking
Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak ; PMLR 84:1839-1848
Comparison Based Learning from Weak Oracles
Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi ; PMLR 84:1849-1858
The Binary Space Partitioning-Tree Process
Xuhui Fan, Bin Li, Scott Sisson ; PMLR 84:1859-1867
On denoising modulo 1 samples of a function
Mihai Cucuringu, Hemant Tyagi ; PMLR 84:1868-1876
Scalable Hash-Based Estimation of Divergence Measures
Morteza Noshad, Alfred Hero ; PMLR 84:1877-1885
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Aryan Mokhtari, Hamed Hassani, Amin Karbasi ; PMLR 84:1886-1895
Efficient Bayesian Methods for Counting Processes in Partially Observable Environments
Ferdian Jovan, Jeremy Wyatt, Nick Hawes ; PMLR 84:1906-1913
Matrix-normal models for fMRI analysis
Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore Willke, Jonathan Cohen ; PMLR 84:1914-1923
The emergence of spectral universality in deep networks
Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli ; PMLR 84:1924-1932
Spectral Algorithms for Computing Fair Support Vector Machines
Mahbod Olfat, Anil Aswani ; PMLR 84:1933-1942
Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences
He Zhao, Piyush Rai, Lan Du, Wray Buntine ; PMLR 84:1943-1951
Nonparametric Bayesian sparse graph linear dynamical systems
Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou ; PMLR 84:1952-1960