Journal of Machine Learning Research
The Journal of Machine Learning Research (JMLR), established in 2000, 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
- On Universal Approximation and Error Bounds for Fourier Neural Operators
- Nikola Kovachki, Samuel Lanthaler, Siddhartha Mishra, 2021.
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- VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
- Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson, 2021.
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- A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines
- Marco C. Campi, Simone Garatti, 2021.
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- V-statistics and Variance Estimation
- Zhengze Zhou, Lucas Mentch, Giles Hooker, 2021.
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- An Online Sequential Test for Qualitative Treatment Effects
- Chengchun Shi, Shikai Luo, Hongtu Zhu, Rui Song, 2021.
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- Double Generative Adversarial Networks for Conditional Independence Testing
- Chengchun Shi, Tianlin Xu, Wicher Bergsma, Lexin Li, 2021.
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- Linear Bandits on Uniformly Convex Sets
- Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont, Sebastian Pokutta, 2021.
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- Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
- Oskar Allerbo, Rebecka Jörnsten, 2021.
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- LDLE: Low Distortion Local Eigenmaps
- Dhruv Kohli, Alexander Cloninger, Gal Mishne, 2021.
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- Contrastive Estimation Reveals Topic Posterior Information to Linear Models
- Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, 2021.
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- Graph Matching with Partially-Correct Seeds
- Liren Yu, Jiaming Xu, Xiaojun Lin, 2021.
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- Fast Learning for Renewal Optimization in Online Task Scheduling
- Michael J. Neely, 2021.
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- Gaussian Approximation for Bias Reduction in Q-Learning
- Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli, 2021.
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- Estimating the Lasso's Effective Noise
- Johannes Lederer, Michael Vogt, 2021.
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- Partial Policy Iteration for L1-Robust Markov Decision Processes
- Chin Pang Ho, Marek Petrik, Wolfram Wiesemann, 2021.
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- Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models
- Bin Liu, Xinsheng Zhang, Yufeng Liu, 2021.
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- On the Hardness of Robust Classification
- Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell, 2021.
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- Transferability of Spectral Graph Convolutional Neural Networks
- Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, Gitta Kutyniok, 2021.
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- Nonparametric Continuous Sensor Registration
- William Clark, Maani Ghaffari, Anthony Bloch, 2021.
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- Further results on latent discourse models and word embeddings
- Sammy Khalife, Douglas Gonçalves, Youssef Allouah, Leo Liberti, 2021.
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- CAT: Compression-Aware Training for bandwidth reduction
- Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson, 2021.
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- Stable-Baselines3: Reliable Reinforcement Learning Implementations
- Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann, 2021. (Machine Learning Open Source Software Paper)
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- Reproducing kernel Hilbert C*-module and kernel mean embeddings
- Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara, 2021.
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- Learning Bayesian Networks from Ordinal Data
- Xiang Ge Luo, Giusi Moffa, Jack Kuipers, 2021.
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- Exact Asymptotics for Linear Quadratic Adaptive Control
- Feicheng Wang, Lucas Janson, 2021.
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- Regularized spectral methods for clustering signed networks
- Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi, 2021.
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- On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes
- Constantin Christof, 2021.
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- Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
- Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang, 2021.
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- Domain adaptation under structural causal models
- Yuansi Chen, Peter Bühlmann, 2021.
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- Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
- Xiaowu Dai, Michael I. Jordan, 2021.
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- ROOTS: Object-Centric Representation and Rendering of 3D Scenes
- Chang Chen, Fei Deng, Sungjin Ahn, 2021.
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- Optimized Score Transformation for Consistent Fair Classification
- Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon, 2021.
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- Estimating Uncertainty Intervals from Collaborating Networks
- Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson, 2021.
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- Model Linkage Selection for Cooperative Learning
- Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh, 2021.
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- Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
- Alex Luedtke, Incheoul Chung, Oleg Sofrygin, 2021.
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- Inference for the Case Probability in High-dimensional Logistic Regression
- Zijian Guo, Prabrisha Rakshit, Daniel S. Herman, Jinbo Chen, 2021.
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- Bifurcation Spiking Neural Network
- Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou, 2021.
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- Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design
- Jayanth Jagalur-Mohan, Youssef Marzouk, 2021.
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- Tractable Approximate Gaussian Inference for Bayesian Neural Networks
- James-A. Goulet, Luong Ha Nguyen, Saeid Amiri, 2021.
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- Bayesian time-aligned factor analysis of paired multivariate time series
- Arkaprava Roy, Jana Schaich Borg, David B Dunson, 2021.
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- Statistically and Computationally Efficient Change Point Localization in Regression Settings
- Daren Wang, Zifeng Zhao, Kevin Z. Lin, Rebecca Willett, 2021.
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- Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
- Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani, 2021.
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- Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals
- Emilie Kaufmann, Wouter M. Koolen, 2021.
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- On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization
- Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe, 2021.
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- Consistency of Gaussian Process Regression in Metric Spaces
- Peter Koepernik, Florian Pfaff, 2021.
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- Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
- Jesus Maria Sanz-Serna, Konstantinos C. Zygalakis, 2021.
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- Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
- Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste, 2021.
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- DIG: A Turnkey Library for Diving into Graph Deep Learning Research
- Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji, 2021. (Machine Learning Open Source Software Paper)
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- Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
- Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu, 2021.
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- DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
- Haishan Ye, Tong Zhang, 2021.
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- Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
- Massimo Fornasier, Lorenzo Pareschi, Hui Huang, Philippe Sünnen, 2021.
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- Expanding Boundaries of Gap Safe Screening
- Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte, 2021.
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- GIBBON: General-purpose Information-Based Bayesian Optimisation
- Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson, 2021.
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- A general linear-time inference method for Gaussian Processes on one dimension
- Jackson Loper, David Blei, John P. Cunningham, Liam Paninski, 2021.
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- A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families
- Robert Sicks, Ralf Korn, Stefanie Schwaar, 2021.
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- Probabilistic Iterative Methods for Linear Systems
- Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid, 2021.
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- sklvq: Scikit Learning Vector Quantization
- Rick van Veen, Michael Biehl, Gert-Jan de Vries, 2021. (Machine Learning Open Source Software Paper)
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- Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature
- Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué, Lucie Neirac, 2021.
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- Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be
- Valerio Biscione, Jeffrey S. Bowers, 2021.
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- Tighter Risk Certificates for Neural Networks
- María Pérez-Ortiz, Omar Risvaplata, John Shawe-Taylor, Csaba Szepesvári, 2021.
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- FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection
- Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang, 2021. (Machine Learning Open Source Software Paper)
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- Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems
- Rui Wang, Yuesheng Xu, 2021.
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- Stochastic Online Optimization using Kalman Recursion
- Joseph de Vilmarest, Olivier Wintenberger, 2021.
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- Classification vs regression in overparameterized regimes: Does the loss function matter?
- Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai, 2021.
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- A Bayes-Optimal View on Adversarial Examples
- Eitan Richardson, Yair Weiss, 2021.
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- Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions
- Xi Chen, Victor Chernozhukov, Ivan Fernandez-Val, Scott Kostyshak, Ye Luo, 2021.
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- Thompson Sampling Algorithms for Cascading Bandits
- Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan, 2021.
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- A Unified Framework for Spectral Clustering in Sparse Graphs
- Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay, 2021.
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- Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions
- Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark, 2021.
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- TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
- Paweł Rościszewski, Michał Martyniak, Filip Schodowski, 2021. (Machine Learning Open Source Software Paper)
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- dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
- Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek, 2021. (Machine Learning Open Source Software Paper)
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- Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms
- Jianyu Wang, Gauri Joshi, 2021.
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- Convex Geometry and Duality of Over-parameterized Neural Networks
- Tolga Ergen, Mert Pilanci, 2021.
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- Bandit Learning in Decentralized Matching Markets
- Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan, 2021.
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- Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
- Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla, 2021.
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- Explaining by Removing: A Unified Framework for Model Explanation
- Ian Covert, Scott Lundberg, Su-In Lee, 2021.
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- Oblivious Data for Fairness with Kernels
- Steffen Grünewälder, Azadeh Khaleghi, 2021.
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- A Unified Convergence Analysis for Shuffling-Type Gradient Methods
- Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, 2021.
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- Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
- Jeongho Kim, Jaeuk Shin, Insoon Yang, 2021.
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- Langevin Monte Carlo: random coordinate descent and variance reduction
- Zhiyan Ding, Qin Li, 2021.
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- Failures of Model-dependent Generalization Bounds for Least-norm Interpolation
- Peter L. Bartlett, Philip M. Long, 2021.
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- Learning partial correlation graphs and graphical models by covariance queries
- Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik, 2021.
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- Interpretable Deep Generative Recommendation Models
- Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jiaqi Wang, Jian Yu, Michael K. Ng, 2021.
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- Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization
- Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik, 2021.
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- Refined approachability algorithms and application to regret minimization with global costs
- Joon Kwon, 2021.
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- On ADMM in Deep Learning: Convergence and Saturation-Avoidance
- Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou, 2021.
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- Integrated Principal Components Analysis
- Tiffany M. Tang, Genevera I. Allen, 2021.
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- Particle-Gibbs Sampling for Bayesian Feature Allocation Models
- Alexandre Bouchard-Côté, Andrew Roth, 2021.
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- COKE: Communication-Censored Decentralized Kernel Learning
- Ping Xu, Yue Wang, Xiang Chen, Zhi Tian, 2021.
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- Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
- Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis, 2021.
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- Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
- Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe, 2021.
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- Sparse Popularity Adjusted Stochastic Block Model
- Majid Noroozi, Marianna Pensky, Ramchandra Rimal, 2021.
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- Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
- Fei Wang, Ling Zhou, Lu Tang, Peter X.K. Song, 2021.
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- Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks
- Changyue Song, Kaibo Liu, Xi Zhang, 2021.
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- One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
- Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani, 2021.
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- Differentially Private Regression and Classification with Sparse Gaussian Processes
- Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence, 2021.
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- Matrix Product States for Inference in Discrete Probabilistic Models
- Rasmus Bonnevie, Mikkel N. Schmidt, 2021.
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- As You Like It: Localization via Paired Comparisons
- Andrew K. Massimino, Mark A. Davenport, 2021.
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- Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
- HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell, 2021.
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- mlr3pipelines - Flexible Machine Learning Pipelines in R
- Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl, 2021. (Machine Learning Open Source Software Paper)
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- Benchmarking Unsupervised Object Representations for Video Sequences
- Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker, 2021.
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- A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
- Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen, 2021.
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- Alibi Explain: Algorithms for Explaining Machine Learning Models
- Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca, 2021. (Machine Learning Open Source Software Paper)
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- Improved Shrinkage Prediction under a Spiked Covariance Structure
- Trambak Banerjee, Gourab Mukherjee, Debashis Paul, 2021.
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- A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
- Yuetian Luo, Garvesh Raskutti, Ming Yuan, Anru R. Zhang, 2021.
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- Conditional independences and causal relations implied by sets of equations
- Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij, 2021.
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- Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond
- Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, 2021.
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- Doubly infinite residual neural networks: a diffusion process approach
- Stefano Peluchetti, Stefano Favaro, 2021.
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- Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
- Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari, 2021.
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- Unlinked Monotone Regression
- Fadoua Balabdaoui, Charles R. Doss, Cécile Durot, 2021.
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- Optimal Rates of Distributed Regression with Imperfect Kernels
- Hongwei Sun, Qiang Wu, 2021.
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- Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity
- Bin Gu, Xiyuan Wei, Shangqian Gao, Ziran Xiong, Cheng Deng, Heng Huang, 2021.
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- First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
- Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang, 2021.
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- Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors
- Jeffrey W. Miller, 2021.
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- Estimation and Optimization of Composite Outcomes
- Daniel J. Luckett, Eric B. Laber, Siyeon Kim, Michael R. Kosorok, 2021.
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- The ensmallen library for flexible numerical optimization
- Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson, 2021. (Machine Learning Open Source Software Paper)
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- Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
- Charles H. Martin, Michael W. Mahoney, 2021.
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- Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program)
- Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, Hugo Larochelle, 2021.
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- PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
- Ivan Stelmakh, Nihar Shah, Aarti Singh, 2021.
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- Counterfactual Mean Embeddings
- Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat, 2021.
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- MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
- Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven, 2021.
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- Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
- Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar, 2021.
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- When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?
- Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett, 2021.
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- Information criteria for non-normalized models
- Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen, 2021.
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- The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
- Takuo Matsubara, Chris J. Oates, François-Xavier Briol, 2021.
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- A Greedy Algorithm for Quantizing Neural Networks
- Eric Lybrand, Rayan Saab, 2021.
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- What Causes the Test Error? Going Beyond Bias-Variance via ANOVA
- Licong Lin, Edgar Dobriban, 2021.
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- Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
- Yikun Zhang, Yen-Chi Chen, 2021.
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- Factorization Machines with Regularization for Sparse Feature Interactions
- Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara, 2021.
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- Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models
- Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda, 2021.
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- Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces
- László Györfi, Roi Weiss, 2021.
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- Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
- Tian Tong, Cong Ma, Yuejie Chi, 2021.
[abs][pdf][bib] [code]
- Hyperparameter Optimization via Sequential Uniform Designs
- Zebin Yang, Aijun Zhang, 2021.
[abs][pdf][bib] [code]
- Statistical guarantees for local graph clustering
- Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney, 2021.
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- Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
- Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, Hongtu Zhu, 2021.
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- Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
- Krishnakumar Balasubramanian, 2021.
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- On efficient multilevel Clustering via Wasserstein distances
- Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung, 2021.
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- Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
- Henning Petzka, Cristian Sminchisescu, 2021.
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- Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
- Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E. Priebe, Joshua T. Vogelstein, 2021.
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- Pseudo-Marginal Hamiltonian Monte Carlo
- Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten, 2021.
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- Generalization Properties of hyper-RKHS and its Applications
- Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens, 2021.
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- Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning
- Jianqing Fan, Bai Jiang, Qiang Sun, 2021.
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- An algorithmic view of L2 regularization and some path-following algorithms
- Yunzhang Zhu, Renxiong Liu, 2021.
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- Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
- Tong Wang, Qihang Lin, 2021.
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- Implicit Langevin Algorithms for Sampling From Log-concave Densities
- Liam Hodgkinson, Robert Salomone, Fred Roosta, 2021.
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- Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
- Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder, 2021.
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- An Inertial Newton Algorithm for Deep Learning
- Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels, 2021.
[abs][pdf][bib] [code]
- A Contextual Bandit Bake-off
- Alberto Bietti, Alekh Agarwal, John Langford, 2021.
[abs][pdf][bib] [code]
- Locally Differentially-Private Randomized Response for Discrete Distribution Learning
- Adriano Pastore, Michael Gastpar, 2021.
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- MushroomRL: Simplifying Reinforcement Learning Research
- Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters, 2021. (Machine Learning Open Source Software Paper)
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- Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
- Steve Hanneke, 2021.
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- Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
- Niladri S. Chatterji, Philip M. Long, 2021.
[abs][pdf][bib]
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