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
- 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.
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- Hyperparameter Optimization via Sequential Uniform Designs
- Zebin Yang, Aijun Zhang, 2021.
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- 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.
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- A Contextual Bandit Bake-off
- Alberto Bietti, Alekh Agarwal, John Langford, 2021.
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- 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.
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- Optimal Bounds between f-Divergences and Integral Probability Metrics
- Rohit Agrawal, Thibaut Horel, 2021.
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- LassoNet: A Neural Network with Feature Sparsity
- Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani, 2021.
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- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
- Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai, 2021.
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- Bandit Convex Optimization in Non-stationary Environments
- Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou, 2021.
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- A flexible model-free prediction-based framework for feature ranking
- Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong, 2021.
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- Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
- George Wynne, François-Xavier Briol, Mark Girolami, 2021.
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- Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
- Kyriakos Axiotis, Maxim Sviridenko, 2021.
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- Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
- Vikram Krishnamurthy, George Yin, 2021.
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- Some Theoretical Insights into Wasserstein GANs
- Gérard Biau, Maxime Sangnier, Ugo Tanielian, 2021.
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- A General Framework for Adversarial Label Learning
- Chidubem Arachie, Bert Huang, 2021.
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- Strong Consistency, Graph Laplacians, and the Stochastic Block Model
- Shaofeng Deng, Shuyang Ling, Thomas Strohmer, 2021.
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- An Importance Weighted Feature Selection Stability Measure
- Victor Hamer, Pierre Dupont, 2021.
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- Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
- Michael R. Metel, Akiko Takeda, 2021.
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- 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.
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- A Lyapunov Analysis of Accelerated Methods in Optimization
- Ashia C. Wilson, Ben Recht, Michael I. Jordan, 2021.
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- L-SVRG and L-Katyusha with Arbitrary Sampling
- Xun Qian, Zheng Qu, Peter Richtárik, 2021.
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- Non-parametric Quantile Regression via the K-NN Fused Lasso
- Steven Siwei Ye, Oscar Hernan Madrid Padilla, 2021.
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- 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)
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- 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)
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- Towards a Unified Analysis of Random Fourier Features
- Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic, 2021.
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- 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.
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- Online stochastic gradient descent on non-convex losses from high-dimensional inference
- Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath, 2021.
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- Pathwise Conditioning of Gaussian Processes
- James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, 2021.
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- 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.
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- 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)
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- Adaptive estimation of nonparametric functionals
- Lin Liu, Rajarshi Mukherjee, James M. Robins, Eric Tchetgen Tchetgen, 2021.
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- On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
- Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan, 2021.
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- Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
- Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli, 2021.
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- Guided Visual Exploration of Relations in Data Sets
- Kai Puolamäki, Emilia Oikarinen, Andreas Henelius, 2021.
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- Histogram Transform Ensembles for Large-scale Regression
- Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen, 2021.
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- Consistent Semi-Supervised Graph Regularization for High Dimensional Data
- Xiaoyi Mai, Romain Couillet, 2021.
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- Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage
- Zhengrong Xing, Peter Carbonetto, Matthew Stephens, 2021.
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- 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.
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- On Solving Probabilistic Linear Diophantine Equations
- Patrick Kreitzberg, Oliver Serang, 2021.
[abs][pdf][bib] [code]
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