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.
News
- 2023.01.20: Volume 23 completed; Volume 24 began.
- 2022.07.20: New special issue on climate change.
- 2022.02.18: New blog post: Retrospectives from 20 Years of JMLR .
- 2022.01.25: Volume 22 completed; Volume 23 began.
- 2021.12.02: Message from outgoing co-EiC Bernhard Schölkopf.
- 2021.02.10: Volume 21 completed; Volume 22 began.
- More news ...
Latest papers
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Fairlearn: Assessing and Improving Fairness of AI Systems
Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio, 2023
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Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White, 2023
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Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum, 2023
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skrl: Modular and Flexible Library for Reinforcement Learning
Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba, 2023
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Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat, 2023
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Adaptive False Discovery Rate Control with Privacy Guarantee
Xintao Xia, Zhanrui Cai, 2023
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Atlas: Few-shot Learning with Retrieval Augmented Language Models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave, 2023
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Convex Reinforcement Learning in Finite Trials
Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli, 2023
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Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC
Tianze Wang, Guanyang Wang, 2023
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Improving multiple-try Metropolis with local balancing
Philippe Gagnon, Florian Maire, Giacomo Zanella, 2023
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Importance Sparsification for Sinkhorn Algorithm
Mengyu Li, Jun Yu, Tao Li, Cheng Meng, 2023
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Graph Attention Retrospective
Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath, 2023
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Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing
Yibo Yan, Xiaozhou Wang, Riquan Zhang, 2023
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Selection by Prediction with Conformal p-values
Ying Jin, Emmanuel J. Candes, 2023
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Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet, 2023
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Sparse Graph Learning from Spatiotemporal Time Series
Andrea Cini, Daniele Zambon, Cesare Alippi, 2023
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Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning
Zhuang Yang, 2023
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel, 2023
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Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification
Oh-Ran Kwon, Hui Zou, 2023
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Efficient Computation of Rankings from Pairwise Comparisons
M. E. J. Newman, 2023
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Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar, 2023
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Neural Q-learning for solving PDEs
Samuel N. Cohen, Deqing Jiang, Justin Sirignano, 2023
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Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
Ziyue Wang, Zhiqiang Tan, 2023
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MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov, 2023
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Strategic Knowledge Transfer
Max Olan Smith, Thomas Anthony, Michael P. Wellman, 2023
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Lifted Bregman Training of Neural Networks
Xiaoyu Wang, Martin Benning, 2023
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Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin, 2023
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Sample Complexity for Distributionally Robust Learning under chi-square divergence
Zhengyu Zhou, Weiwei Liu, 2023
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Interpretable and Fair Boolean Rule Sets via Column Generation
Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei, 2023
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On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon, 2023
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Autoregressive Networks
Binyan Jiang, Jialiang Li, Qiwei Yao, 2023
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Merlion: End-to-End Machine Learning for Time Series
Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang, 2023
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Limits of Dense Simplicial Complexes
T. Mitchell Roddenberry, Santiago Segarra, 2023
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RankSEG: A Consistent Ranking-based Framework for Segmentation
Ben Dai, Chunlin Li, 2023
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Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data
Bingqing Hu, Bin Nan, 2023
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Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees
Mo Zhou, Jianfeng Lu, 2023
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Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
Doudou Zhou, Tianxi Cai, Junwei Lu, 2023
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A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee, 2023
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Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data
Hua Liu, Jinhong You, Jiguo Cao, 2023
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Adaptation Augmented Model-based Policy Optimization
Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang, 2023
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GANs as Gradient Flows that Converge
Yu-Jui Huang, Yuchong Zhang, 2023
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Random Forests for Change Point Detection
Malte Londschien, Peter Bühlmann, Solt Kovács, 2023
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Least Squares Model Averaging for Distributed Data
Haili Zhang, Zhaobo Liu, Guohua Zou, 2023
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An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell, 2023
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Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz, 2023
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An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity
Wei Liu, Xin Liu, Xiaojun Chen, 2023
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Entropic Fictitious Play for Mean Field Optimization Problem
Fan Chen, Zhenjie Ren, Songbo Wang, 2023
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GFlowNet Foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio, 2023
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LibMTL: A Python Library for Deep Multi-Task Learning
Baijiong Lin, Yu Zhang, 2023
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Minimax Risk Classifiers with 0-1 Loss
Santiago Mazuelas, Mauricio Romero, Peter Grunwald, 2023
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Augmented Sparsifiers for Generalized Hypergraph Cuts
Nate Veldt, Austin R. Benson, Jon Kleinberg, 2023
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Non-stationary Online Learning with Memory and Non-stochastic Control
Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou, 2023
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L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
Hussein Hazimeh, Rahul Mazumder, Tim Nonet, 2023
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Buffered Asynchronous SGD for Byzantine Learning
Yi-Rui Yang, Wu-Jun Li, 2023
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A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points
Lili Su, Jiaming Xu, Pengkun Yang, 2023
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Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff, 2023
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Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations
Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng, 2023
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Model-based Causal Discovery for Zero-Inflated Count Data
Junsouk Choi, Yang Ni, 2023
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Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Ali Kara, Naci Saldi, Serdar Yüksel, 2023
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CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges
Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu, 2023
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Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum, 2023
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Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann, 2023
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Clustering and Structural Robustness in Causal Diagrams
Santtu Tikka, Jouni Helske, Juha Karvanen, 2023
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MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton, 2023
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Divide-and-Conquer Fusion
Ryan S.Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts, 2023
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PAC-learning for Strategic Classification
Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao, 2023
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Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg, 2023
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Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees
Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg, 2023
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Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality
Lukas Gonon, 2023
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The Proximal ID Algorithm
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen, 2023
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Quantifying Network Similarity using Graph Cumulants
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Pierre-André Maugis, Carey E. Priebe, 2023
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Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks
Jun Shu, Deyu Meng, Zongben Xu, 2023
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On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity
Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin, 2023
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Metrizing Weak Convergence with Maximum Mean Discrepancies
Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey, 2023
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Quasi-Equivalence between Width and Depth of Neural Networks
Fenglei Fan, Rongjie Lai, Ge Wang, 2023
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Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding
Justin Grimmer, Dean Knox, Brandon Stewart, 2023
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Factor Graph Neural Networks
Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee, 2023
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Dropout Training is Distributionally Robust Optimal
José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang, 2023
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Variational Inference for Deblending Crowded Starfields
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, The LSST Dark Energy Science Collaboration, 2023
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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha, 2023
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Comprehensive Algorithm Portfolio Evaluation using Item Response Theory
Sevvandi Kandanaarachchi, Kate Smith-Miles, 2023
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Evaluating Instrument Validity using the Principle of Independent Mechanisms
Patrick F. Burauel, 2023
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Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang, 2023
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Posterior Consistency for Bayesian Relevance Vector Machines
Xiao Fang, Malay Ghosh, 2023
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From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
Johannes Resin, 2023
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Beyond the Golden Ratio for Variational Inequality Algorithms
Ahmet Alacaoglu, Axel Böhm, Yura Malitsky, 2023
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Incremental Learning in Diagonal Linear Networks
Raphaël Berthier, 2023
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Small Transformers Compute Universal Metric Embeddings
Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić, 2023
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DART: Distance Assisted Recursive Testing
Xuechan Li, Anthony D. Sung, Jichun Xie, 2023
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Inference on the Change Point under a High Dimensional Covariance Shift
Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis, 2023
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Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, 2023
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A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition
Masaru Ito, Zhaosong Lu, Chuan He, 2023
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Robust Methods for High-Dimensional Linear Learning
Ibrahim Merad, Stéphane Gaïffas, 2023
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A Framework and Benchmark for Deep Batch Active Learning for Regression
David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart, 2023
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Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
Gavin Zhang, Salar Fattahi, Richard Y. Zhang, 2023
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Flexible Model Aggregation for Quantile Regression
Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani, 2023
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q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou, 2023
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Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling
Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron, 2023
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Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
Arnab Ganguly, Riten Mitra, Jinpu Zhou, 2023
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Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Hamid Reza Feyzmahdavian, Mikael Johansson, 2023
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Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity
Huan Li, Zhouchen Lin, 2023
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Integrating Random Effects in Deep Neural Networks
Giora Simchoni, Saharon Rosset, 2023
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Adaptive Data Depth via Multi-Armed Bandits
Tavor Baharav, Tze Leung Lai, 2023
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Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd, 2023
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Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process
Cheng Zeng, Jeffrey W Miller, Leo L Duan, 2023
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Selective inference for k-means clustering
Yiqun T. Chen, Daniela M. Witten, 2023
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Generalization error bounds for multiclass sparse linear classifiers
Tomer Levy, Felix Abramovich, 2023
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MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang, 2023
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Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Titouan Vayer, Rémi Gribonval, 2023
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Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang, 2023
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Stochastic Optimization under Distributional Drift
Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui, 2023
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Off-Policy Actor-Critic with Emphatic Weightings
Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White, 2023
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Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang, 2023
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Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering
Noirrit Kiran Chandra, Antonio Canale, David B. Dunson, 2023
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Large sample spectral analysis of graph-based multi-manifold clustering
Nicolas Garcia Trillos, Pengfei He, Chenghui Li, 2023
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On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith, 2023
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Optimal Convergence Rates for Distributed Nystroem Approximation
Jian Li, Yong Liu, Weiping Wang, 2023
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Jump Interval-Learning for Individualized Decision Making with Continuous Treatments
Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu, 2023
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Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
Ben Hambly, Renyuan Xu, Huining Yang, 2023
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Asymptotics of Network Embeddings Learned via Subsampling
Andrew Davison, Morgane Austern, 2023
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Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks
Hui Jin, Guido Montufar, 2023
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Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Wenhao Li, Ningyuan Chen, L. Jeff Hong, 2023
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Sparse GCA and Thresholded Gradient Descent
Sheng Gao, Zongming Ma, 2023
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MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation
Qian Li, Binyan Jiang, Defeng Sun, 2023
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Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill, 2023
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Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data
Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu, 2023
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A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral, 2023
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Combinatorial Optimization and Reasoning with Graph Neural Networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Veličković, 2023
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A First Look into the Carbon Footprint of Federated Learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane, 2023
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An Eigenmodel for Dynamic Multilayer Networks
Joshua Daniel Loyal, Yuguo Chen, 2023
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Graph Clustering with Graph Neural Networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller, 2023
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Euler-Lagrange Analysis of Generative Adversarial Networks
Siddarth Asokan, Chandra Sekhar Seelamantula, 2023
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Statistical Robustness of Empirical Risks in Machine Learning
Shaoyan Guo, Huifu Xu, Liwei Zhang, 2023
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HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu, 2023
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Benign overfitting in ridge regression
Alexander Tsigler, Peter L. Bartlett, 2023
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Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock, 2023
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Minimal Width for Universal Property of Deep RNN
Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang, 2023
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Maximum likelihood estimation in Gaussian process regression is ill-posed
Toni Karvonen, Chris J. Oates, 2023
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An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks
Stefan Stein, Chenlei Leng, 2023
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A Unified Framework for Optimization-Based Graph Coarsening
Manoj Kumar, Anurag Sharma, Sandeep Kumar, 2023
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Deep linear networks can benignly overfit when shallow ones do
Niladri S. Chatterji, Philip M. Long, 2023
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SQLFlow: An Extensible Toolkit Integrating DB and AI
Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen, 2023
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Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition
Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang, 2023
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Generalization Bounds for Adversarial Contrastive Learning
Xin Zou, Weiwei Liu, 2023
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The Implicit Bias of Benign Overfitting
Ohad Shamir, 2023
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The Hyperspherical Geometry of Community Detection: Modularity as a Distance
Martijn Gösgens, Remco van der Hofstad, Nelly Litvak, 2023
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FLIP: A Utility Preserving Privacy Mechanism for Time Series
Tucker McElroy, Anindya Roy, Gaurab Hore, 2023
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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi, 2023
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Dimensionless machine learning: Imposing exact units equivariance
Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu, 2023
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Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
Michail Spitieris, Ingelin Steinsland, 2023
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Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption
Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile, 2023
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Concentration analysis of multivariate elliptic diffusions
Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch, 2023
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Knowledge Hypergraph Embedding Meets Relational Algebra
Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole, 2023
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Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang, 2023
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Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint
Michael R. Metel, 2023
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Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
Haixu Ma, Donglin Zeng, Yufeng Liu, 2023
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Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
Didong Li, Wenpin Tang, Sudipto Banerjee, 2023
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FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu, 2023
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Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
Artem Vysogorets, Julia Kempe, 2023
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An Analysis of Robustness of Non-Lipschitz Networks
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang, 2023
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Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation
Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu, 2023
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Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
Jianhao Ma, Salar Fattahi, 2023
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Statistical Inference for Noisy Incomplete Binary Matrix
Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu, 2023
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Faith-Shap: The Faithful Shapley Interaction Index
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar, 2023
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Decentralized Learning: Theoretical Optimality and Practical Improvements
Yucheng Lu, Christopher De Sa, 2023
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Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang, 2023
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Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds
Likai Chen, Georg Keilbar, Wei Biao Wu, 2023
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Outlier-Robust Subsampling Techniques for Persistent Homology
Bernadette J. Stolz, 2023
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Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs
Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar, 2023
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Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson, 2023
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Gaussian Processes with Errors in Variables: Theory and Computation
Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll, 2023
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Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
George Stepaniants, 2023
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Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
Henry Lam, Haofeng Zhang, 2023
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Online Optimization over Riemannian Manifolds
Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi, 2023
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Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
William J. Wilkinson, Simo Särkkä, Arno Solin, 2023
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Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality
Ning Ning, Edward L. Ionides, 2023
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Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill, 2023
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Temporal Abstraction in Reinforcement Learning with the Successor Representation
Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling, 2023
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