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Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.


Latest papers

Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri S. Chatterji, Philip M. Long, 2021.
[abs][pdf][bib]

Optimal Bounds between f-Divergences and Integral Probability Metrics
Rohit Agrawal, Thibaut Horel, 2021.
[abs][pdf][bib]

LassoNet: A Neural Network with Feature Sparsity
Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani, 2021.
[abs][pdf][bib]      [code]

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai, 2021.
[abs][pdf][bib]

Bandit Convex Optimization in Non-stationary Environments
Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou, 2021.
[abs][pdf][bib]

A flexible model-free prediction-based framework for feature ranking
Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong, 2021.
[abs][pdf][bib]      [code]

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne, François-Xavier Briol, Mark Girolami, 2021.
[abs][pdf][bib]

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis, Maxim Sviridenko, 2021.
[abs][pdf][bib]

Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Vikram Krishnamurthy, George Yin, 2021.
[abs][pdf][bib]

Empirical Bayes Matrix Factorization
Wei Wang, Matthew Stephens, 2021.
[abs][pdf][bib]      [code]

Some Theoretical Insights into Wasserstein GANs
Gérard Biau, Maxime Sangnier, Ugo Tanielian, 2021.
[abs][pdf][bib]

A General Framework for Adversarial Label Learning
Chidubem Arachie, Bert Huang, 2021.
[abs][pdf][bib]      [code]

Strong Consistency, Graph Laplacians, and the Stochastic Block Model
Shaofeng Deng, Shuyang Ling, Thomas Strohmer, 2021.
[abs][pdf][bib]

An Importance Weighted Feature Selection Stability Measure
Victor Hamer, Pierre Dupont, 2021.
[abs][pdf][bib]

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
Michael R. Metel, Akiko Takeda, 2021.
[abs][pdf][bib]

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy, 2021.
[abs][pdf][bib]

A Lyapunov Analysis of Accelerated Methods in Optimization
Ashia C. Wilson, Ben Recht, Michael I. Jordan, 2021.
[abs][pdf][bib]

L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik, 2021.
[abs][pdf][bib]

Non-parametric Quantile Regression via the K-NN Fused Lasso
Steven Siwei Ye, Oscar Hernan Madrid Padilla, 2021.
[abs][pdf][bib]      [code]

River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

mvlearn: Multiview Machine Learning in Python
Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Towards a Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic, 2021.
[abs][pdf][bib]

Beyond English-Centric Multilingual Machine Translation
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, Armand Joulin, 2021.
[abs][pdf][bib]      [code]

Online stochastic gradient descent on non-convex losses from high-dimensional inference
Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath, 2021.
[abs][pdf][bib]

Pathwise Conditioning of Gaussian Processes
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, 2021.
[abs][pdf][bib]      [code]

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee, 2021.
[abs][pdf][bib]      [code]

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse, 2021.
[abs][pdf][bib]      [code]

Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression
Gunwoong Park, Sang Jun Moon, Sion Park, Jong-June Jeon, 2021.
[abs][pdf][bib]

LocalGAN: Modeling Local Distributions for Adversarial Response Generation
Baoxun Wang, Zhen Xu, Huan Zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun, 2021.
[abs][pdf][bib]      [code]

OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Adaptive estimation of nonparametric functionals
Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen, 2021.
[abs][pdf][bib]

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan, 2021.
[abs][pdf][bib]

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli, 2021.
[abs][pdf][bib]

Guided Visual Exploration of Relations in Data Sets
Kai Puolamäki, Emilia Oikarinen, Andreas Henelius, 2021.
[abs][pdf][bib]      [code]

Histogram Transform Ensembles for Large-scale Regression
Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen, 2021.
[abs][pdf][bib]

Consistent Semi-Supervised Graph Regularization for High Dimensional Data
Xiaoyi Mai, Romain Couillet, 2021.
[abs][pdf][bib]

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage
Zhengrong Xing, Peter Carbonetto, Matthew Stephens, 2021.
[abs][pdf][bib]      [code]

NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
Anastasis Kratsios, Cody Hyndman, 2021.
[abs][pdf][bib]      [code]

Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
Tingting Zhao, Alexandre Bouchard-Côté, 2021.
[abs][pdf][bib]      [code]

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
Tomer Galanti, Sagie Benaim, Lior Wolf, 2021.
[abs][pdf][bib]      [code]

Bayesian Text Classification and Summarization via A Class-Specified Topic Model
Feifei Wang, Junni L. Zhang, Yichao Li, Ke Deng, Jun S. Liu, 2021.
[abs][pdf][bib]

Edge Sampling Using Local Network Information
Can M. Le, 2021.
[abs][pdf][bib]

On Solving Probabilistic Linear Diophantine Equations
Patrick Kreitzberg, Oliver Serang, 2021.
[abs][pdf][bib]      [code]

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek, 2021.
[abs][pdf][bib]

Gradient Methods Never Overfit On Separable Data
Ohad Shamir, 2021.
[abs][pdf][bib]

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu, Weidong Liu, Xiaojun Mao, Xi Chen, 2021.
[abs][pdf][bib]

Statistical Query Lower Bounds for Tensor PCA
Rishabh Dudeja, Daniel Hsu, 2021.
[abs][pdf][bib]

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann, 2021.
[abs][pdf][bib]

Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
Maxime Cauchois, Suyash Gupta, John C. Duchi, 2021.
[abs][pdf][bib]

Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
Zengfeng Huang, Xuemin Lin, Wenjie Zhang, Ying Zhang, 2021.
[abs][pdf][bib]

Is SGD a Bayesian sampler? Well, almost
Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis, 2021.
[abs][pdf][bib]

POT: Python Optimal Transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

ChainerRL: A Deep Reinforcement Learning Library
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
Alain Celisse, Martin Wahl, 2021.
[abs][pdf][bib]

Attention is Turing-Complete
Jorge Pérez, Pablo Barceló, Javier Marinkovic, 2021.
[abs][pdf][bib]

Kernel Operations on the GPU, with Autodiff, without Memory Overflows
Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
Michael Muehlebach, Michael I. Jordan, 2021.
[abs][pdf][bib]

Prediction against a limited adversary
Erhan Bayraktar, Ibrahim Ekren, Xin Zhang, 2021.
[abs][pdf][bib]

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang, 2021.
[abs][pdf][bib]      [code]

Testing Conditional Independence via Quantile Regression Based Partial Copulas
Lasse Petersen, Niels Richard Hansen, 2021.
[abs][pdf][bib]      [code]

Determining the Number of Communities in Degree-corrected Stochastic Block Models
Shujie Ma, Liangjun Su, Yichong Zhang, 2021.
[abs][pdf][bib]

Path Length Bounds for Gradient Descent and Flow
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas, 2021.
[abs][pdf][bib]      [blog]

A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun, 2021.
[abs][pdf][bib]

Approximate Newton Methods
Haishan Ye, Luo Luo, Zhihua Zhang, 2021.
[abs][pdf][bib]

Dynamic Tensor Recommender Systems
Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu, 2021.
[abs][pdf][bib]

Sparse Tensor Additive Regression
Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun, 2021.
[abs][pdf][bib]

Geometric structure of graph Laplacian embeddings
Nicolás García Trillos, Franca Hoffmann, Bamdad Hosseini, 2021.
[abs][pdf][bib]

How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information
Mariusz Kubkowski, Jan Mielniczuk, Paweł Teisseyre, 2021.
[abs][pdf][bib]

Stochastic Proximal AUC Maximization
Yunwen Lei, Yiming Ying, 2021.
[abs][pdf][bib]

A Distributed Method for Fitting Laplacian Regularized Stratified Models
Jonathan Tuck, Shane Barratt, Stephen Boyd, 2021.
[abs][pdf][bib]      [code]

Predictive Learning on Hidden Tree-Structured Ising Models
Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate, 2021.
[abs][pdf][bib]      [code]

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
Zhe Fei, Yi Li, 2021.
[abs][pdf][bib]      [code]

Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan, 2021.
[abs][pdf][bib]

Incorporating Unlabeled Data into Distributionally Robust Learning
Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon, 2021.
[abs][pdf][bib]

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Minjie Wang, Genevera I. Allen, 2021.
[abs][pdf][bib]

GemBag: Group Estimation of Multiple Bayesian Graphical Models
Xinming Yang, Lingrui Gan, Naveen N. Narisetty, Feng Liang, 2021.
[abs][pdf][bib]

Subspace Clustering through Sub-Clusters
Weiwei Li, Jan Hannig, Sayan Mukherjee, 2021.
[abs][pdf][bib]

Sparse and Smooth Signal Estimation: Convexification of L0-Formulations
Alper Atamturk, Andres Gomez, Shaoning Han, 2021.
[abs][pdf][bib]

Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks
Junlong Zhu, Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Keqin Li, 2021.
[abs][pdf][bib]

Structure Learning of Undirected Graphical Models for Count Data
Nguyen Thi Kim Hue, Monica Chiogna, 2021.
[abs][pdf][bib]

From Low Probability to High Confidence in Stochastic Convex Optimization
Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang, 2021.
[abs][pdf][bib]

Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
Behzad Azmi, Dante Kalise, Karl Kunisch, 2021.
[abs][pdf][bib]

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
Soon Hoe Lim, 2021.
[abs][pdf][bib]

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Tony Cai, Hongzhe Li, Rong Ma, 2021.
[abs][pdf][bib]

RaSE: Random Subspace Ensemble Classification
Ye Tian, Yang Feng, 2021.
[abs][pdf][bib]      [code]

Wasserstein barycenters can be computed in polynomial time in fixed dimension
Jason M Altschuler, Enric Boix-Adsera, 2021.
[abs][pdf][bib]      [code]

Banach Space Representer Theorems for Neural Networks and Ridge Splines
Rahul Parhi, Robert D. Nowak, 2021.
[abs][pdf][bib]

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan, 2021.
[abs][pdf][bib]

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Henning Lange, Steven L. Brunton, J. Nathan Kutz, 2021.
[abs][pdf][bib]      [code]

Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam, 2021.
[abs][pdf][bib]

giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
Umit Köse, Andrzej Ruszczyński, 2021.
[abs][pdf][bib]

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Zhao Tang Luo, Huiyan Sang, Bani Mallick, 2021.
[abs][pdf][bib]

Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldívar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2021.
[abs][pdf][bib]      [code]

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
Melkior Ornik, Ufuk Topcu, 2021.
[abs][pdf][bib]

Neighborhood Structure Assisted Non-negative Matrix Factorization and Its Application in Unsupervised Point-wise Anomaly Detection
Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding, 2021.
[abs][pdf][bib]      [code]

Asynchronous Online Testing of Multiple Hypotheses
Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan, 2021.
[abs][pdf][bib]

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang, 2021.
[abs][pdf][bib]      [code]

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, 2021.
[abs][pdf][bib]      [website]

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris, 2021.
[abs][pdf][bib]

Single and Multiple Change-Point Detection with Differential Privacy
Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings, 2021.
[abs][pdf][bib]

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin, 2021.
[abs][pdf][bib]

Inference In High-dimensional Single-Index Models Under Symmetric Designs
Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov, 2021.
[abs][pdf][bib]      [code]

Finite Time LTI System Identification
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh, 2021.
[abs][pdf][bib]

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
Yunwen Lei, Ting Hu, Ke Tang, 2021.
[abs][pdf][bib]

Entangled Kernels - Beyond Separability
Riikka Huusari, Hachem Kadri, 2021.
[abs][pdf][bib]      [code]

A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone, 2021.
[abs][pdf][bib]

When random initializations help: a study of variational inference for community detection
Purnamrita Sarkar, Y. X. Rachel Wang, Soumendu S. Mukherjee, 2021.
[abs][pdf][bib]

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh, 2021.
[abs][pdf][bib]

Aggregated Hold-Out
Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle, 2021.
[abs][pdf][bib]

Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi, 2021.
[abs][pdf][bib]

A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective
Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang, 2021.
[abs][pdf][bib]

Continuous Time Analysis of Momentum Methods
Nikola B. Kovachki, Andrew M. Stuart, 2021.
[abs][pdf][bib]

Pykg2vec: A Python Library for Knowledge Graph Embedding
Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque, 2021. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]      [code]

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples
Jagdeep Singh Bhatia, 2021.
[abs][pdf][bib]

On Multi-Armed Bandit Designs for Dose-Finding Trials
Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere, 2021.
[abs][pdf][bib]

Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors
Xiao Di, Yuan Ke, Runze Li, 2021.
[abs][pdf][bib]

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Shenglong Zhou, Naihua Xiu, Hou-Duo Qi, 2021.
[abs][pdf][bib]

Unfolding-Model-Based Visualization: Theory, Method and Applications
Yunxiao Chen, Zhiliang Ying, Haoran Zhang, 2021.
[abs][pdf][bib]      [code]

Mixing Time of Metropolis-Hastings for Bayesian Community Detection
Bumeng Zhuo, Chao Gao, 2021.
[abs][pdf][bib]

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, 2021.
[abs][pdf][bib]

A Unified Framework for Random Forest Prediction Error Estimation
Benjamin Lu, Johanna Hardin, 2021.
[abs][pdf][bib]

Preference-based Online Learning with Dueling Bandits: A Survey
Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier, 2021.
[abs][pdf][bib]

Consistent estimation of small masses in feature sampling
Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro, 2021.
[abs][pdf][bib]

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
Carlos A. Gomez-Uribe, Brian Karrer, 2021.
[abs][pdf][bib]

An Empirical Study of Bayesian Optimization: Acquisition Versus Partition
Erich Merrill, Alan Fern, Xiaoli Fern, Nima Dolatnia, 2021.
[abs][pdf][bib]      [code]

Regulating Greed Over Time in Multi-Armed Bandits
Stefano Tracà, Cynthia Rudin, Weiyu Yan, 2021.
[abs][pdf][bib]      [code]

Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott, 2021.
[abs][pdf][bib]      [code]

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Krishnakumar Balasubramanian, Tong Li, Ming Yuan, 2021.
[abs][pdf][bib]

Full list

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