JMLR Volume 24
- Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
- Benjamin Moseley, Joshua R. Wang; (1):1−36, 2023.
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- The Brier Score under Administrative Censoring: Problems and a Solution
- Håvard Kvamme, Ørnulf Borgan; (2):1−26, 2023.
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- Bayesian Spiked Laplacian Graphs
- Leo L Duan, George Michailidis, Mingzhou Ding; (3):1−35, 2023.
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- Efficient Structure-preserving Support Tensor Train Machine
- Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner; (4):1−22, 2023.
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- Cluster-Specific Predictions with Multi-Task Gaussian Processes
- Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey; (5):1−49, 2023.
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- AutoKeras: An AutoML Library for Deep Learning
- Haifeng Jin, François Chollet, Qingquan Song, Xia Hu; (6):1−6, 2023. (Machine Learning Open Source Software Paper)
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- On Distance and Kernel Measures of Conditional Dependence
- Tianhong Sheng, Bharath K. Sriperumbudur; (7):1−16, 2023.
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- A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs
- Radu I. Bot, Michael Sedlmayer, Phan Tu Vuong; (8):1−37, 2023.
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- Sampling random graph homomorphisms and applications to network data analysis
- Hanbaek Lyu, Facundo Memoli, David Sivakoff; (9):1−79, 2023.
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- A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
- Michael J. O'Neill, Stephen J. Wright; (10):1−34, 2023.
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- Optimal Strategies for Reject Option Classifiers
- Vojtech Franc, Daniel Prusa, Vaclav Voracek; (11):1−49, 2023.
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- Learning-augmented count-min sketches via Bayesian nonparametrics
- Emanuele Dolera, Stefano Favaro, Stefano Peluchetti; (12):1−60, 2023.
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- Adaptation to the Range in K-Armed Bandits
- Hédi Hadiji, Gilles Stoltz; (13):1−33, 2023.
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- Python package for causal discovery based on LiNGAM
- Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; (14):1−8, 2023. (Machine Learning Open Source Software Paper)
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- Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
- Jon Vadillo, Roberto Santana, Jose A. Lozano; (15):1−42, 2023.
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- Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
- Cynthia Rudin, Yaron Shaposhnik; (16):1−44, 2023.
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- Learning Mean-Field Games with Discounted and Average Costs
- Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi; (17):1−59, 2023.
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- An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
- Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis; (18):1−41, 2023.
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- Regularized Joint Mixture Models
- Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee; (19):1−47, 2023.
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- Interpolating Classifiers Make Few Mistakes
- Tengyuan Liang, Benjamin Recht; (20):1−27, 2023.
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- Graph-Aided Online Multi-Kernel Learning
- Pouya M. Ghari, Yanning Shen; (21):1−44, 2023.
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- Lower Bounds and Accelerated Algorithms for Bilevel Optimization
- Kaiyi ji, Yingbin Liang; (22):1−56, 2023.
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- Bayesian Data Selection
- Eli N. Weinstein, Jeffrey W. Miller; (23):1−72, 2023.
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- Calibrated Multiple-Output Quantile Regression with Representation Learning
- Shai Feldman, Stephen Bates, Yaniv Romano; (24):1−48, 2023.
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- Discrete Variational Calculus for Accelerated Optimization
- Cédric M. Campos, Alejandro Mahillo, David Martín de Diego; (25):1−33, 2023.
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- Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
- Hao Wang, Rui Gao, Flavio P. Calmon; (26):1−43, 2023.
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- The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
- Raj Agrawal, Tamara Broderick; (27):1−60, 2023.
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- Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
- XuranMeng, JeffYao; (28):1−40, 2023. (Machine Learning Open Source Software Paper)
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- HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn
- Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard; (29):1−17, 2023. (Machine Learning Open Source Software Paper)
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- Attacks against Federated Learning Defense Systems and their Mitigation
- Cody Lewis, Vijay Varadharajan, Nasimul Noman; (30):1−50, 2023.
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- Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
- Shiyu Duan, Spencer Chang, Jose C. Principe; (31):1−35, 2023.
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- Sparse PCA: a Geometric Approach
- Dimitris Bertsimas, Driss Lahlou Kitane; (32):1−33, 2023.
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- Gap Minimization for Knowledge Sharing and Transfer
- Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton; (33):1−57, 2023.
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- Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
- Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne; (34):1−11, 2023. (Machine Learning Open Source Software Paper)
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- Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?
- Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan; (35):1−52, 2023.
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- Label Distribution Changing Learning with Sample Space Expanding
- Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou; (36):1−48, 2023.
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- First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
- Michael I. Jordan, Tianyi Lin, Manolis Zampetakis; (38):1−46, 2023.
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- Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
- Julián Tachella, Dongdong Chen, Mike Davies; (39):1−45, 2023.
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- On Batch Teaching Without Collusion
- Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles; (40):1−33, 2023.
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- Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
- Shaowu Pan, Steven L. Brunton, J. Nathan Kutz; (41):1−60, 2023.
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- A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
- Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan; (42):1−63, 2023.
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- Benchmarking Graph Neural Networks
- Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson; (43):1−48, 2023.
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- Robust Load Balancing with Machine Learned Advice
- Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng; (44):1−46, 2023.
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- The multimarginal optimal transport formulation of adversarial multiclass classification
- Nicolás García Trillos, Matt Jacobs, Jakwang Kim; (45):1−56, 2023.
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- The d-Separation Criterion in Categorical Probability
- Tobias Fritz, Andreas Klingler; (46):1−49, 2023.
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- A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering
- Haizi Yu, Igor Mineyev, Lav R. Varshney; (47):1−61, 2023.
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- On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size
- Xiaoyu Wang, Ya-xiang Yuan; (48):1−49, 2023.
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- Reinforcement Learning for Joint Optimization of Multiple Rewards
- Mridul Agarwal, Vaneet Aggarwal; (49):1−41, 2023.
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- Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning
- Linxi Liu, Dangna Li, Wing Hung Wong; (50):1−64, 2023.
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- Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks
- Lingjun Li, Jun Li; (51):1−44, 2023.
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- Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
- Kunal Pattanayak, Vikram Krishnamurthy; (52):1−64, 2023.
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- VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
- Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica; (53):1−45, 2023.
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- Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
- Chen Lu, Subhabrata Sen; (54):1−34, 2023.
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- Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
- Shaogao Lv, Xin He, Junhui Wang; (55):1−38, 2023.
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- On the geometry of Stein variational gradient descent
- Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch; (56):1−39, 2023.
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- Tree-AMP: Compositional Inference with Tree Approximate Message Passing
- Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová; (57):1−89, 2023.
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- Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval
- Yan Shuo Tan, Roman Vershynin; (58):1−47, 2023.
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- Topological Convolutional Layers for Deep Learning
- Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar Carlsson; (59):1−35, 2023.
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- Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints
- Qinbo Bai, Vaneet Aggarwal, Ather Gattami; (60):1−25, 2023.
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- Density estimation on low-dimensional manifolds: an inflation-deflation approach
- Christian Horvat, Jean-Pascal Pfister; (61):1−37, 2023.
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- Monotonic Alpha-divergence Minimisation for Variational Inference
- Kamélia Daudel, Randal Douc, François Roueff; (62):1−76, 2023.
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- On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
- Marcelo Arenas, Pablo Barcelo, Leopoldo Bertossi, Mikael Monet; (63):1−58, 2023.
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- Fundamental limits and algorithms for sparse linear regression with sublinear sparsity
- Lan V. Truong; (64):1−49, 2023.
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- Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
- Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu; (65):1−37, 2023.
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- Posterior Contraction for Deep Gaussian Process Priors
- Gianluca Finocchio, Johannes Schmidt-Hieber; (66):1−49, 2023.
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- Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
- Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami; (67):1−51, 2023.
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- Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data
- Ruoyu Wang, Miaomiao Su, Qihua Wang; (68):1−52, 2023.
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- Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection
- Jonathan Hillman, Toby Dylan Hocking; (70):1−24, 2023.
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- Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
- Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau; (71):1−57, 2023.
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- How Do You Want Your Greedy: Simultaneous or Repeated?
- Moran Feldman, Christopher Harshaw, Amin Karbasi; (72):1−87, 2023.
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- Inference for a Large Directed Acyclic Graph with Unspecified Interventions
- Chunlin Li, Xiaotong Shen, Wei Pan; (73):1−48, 2023.
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- Intrinsic Persistent Homology via Density-based Metric Learning
- Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman; (75):1−42, 2023.
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- A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection
- Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath; (76):1−31, 2023.
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- A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
- Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin; (77):1−42, 2023.
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- Towards Learning to Imitate from a Single Video Demonstration
- Glen Berseth, Florian Golemo, Christopher Pal; (78):1−26, 2023.
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- Approximate Post-Selective Inference for Regression with the Group LASSO
- Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler; (79):1−49, 2023.
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- Temporal Abstraction in Reinforcement Learning with the Successor Representation
- Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling; (80):1−69, 2023.
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- Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
- Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill; (81):1−36, 2023.
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- Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality
- Ning Ning, Edward L. Ionides; (82):1−76, 2023.
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- Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
- William J. Wilkinson, Simo Särkkä, Arno Solin; (83):1−50, 2023.
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- Online Optimization over Riemannian Manifolds
- Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi; (84):1−67, 2023.
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- Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
- Henry Lam, Haofeng Zhang; (85):1−58, 2023.
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- Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
- George Stepaniants; (86):1−72, 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; (87):1−53, 2023.
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- Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
- Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson; (88):1−49, 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; (89):1−97, 2023.
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- Outlier-Robust Subsampling Techniques for Persistent Homology
- Bernadette J. Stolz; (90):1−35, 2023.
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- Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds
- Likai Chen, Georg Keilbar, Wei Biao Wu; (91):1−25, 2023.
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- Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
- Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang; (92):1−46, 2023.
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- Decentralized Learning: Theoretical Optimality and Practical Improvements
- Yucheng Lu, Christopher De Sa; (93):1−62, 2023.
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- Faith-Shap: The Faithful Shapley Interaction Index
- Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar; (94):1−42, 2023.
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- Statistical Inference for Noisy Incomplete Binary Matrix
- Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu; (95):1−66, 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; (96):1−84, 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; (97):1−30, 2023.
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- An Analysis of Robustness of Non-Lipschitz Networks
- Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang; (98):1−43, 2023.
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- Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
- Artem Vysogorets, Julia Kempe; (99):1−23, 2023.
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- FedLab: A Flexible Federated Learning Framework
- Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu; (100):1−7, 2023. (Machine Learning Open Source Software Paper)
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- Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
- Didong Li, Wenpin Tang, Sudipto Banerjee; (101):1−26, 2023.
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- Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
- Haixu Ma, Donglin Zeng, Yufeng Liu; (102):1−48, 2023.
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- Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint
- Michael R. Metel; (103):1−44, 2023.
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- Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
- Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang; (104):1−42, 2023.
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- Knowledge Hypergraph Embedding Meets Relational Algebra
- Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole; (105):1−34, 2023.
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- Concentration analysis of multivariate elliptic diffusions
- Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch; (106):1−38, 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; (107):1−31, 2023.
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- Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
- Michail Spitieris, Ingelin Steinsland; (108):1−39, 2023.
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- Dimensionless machine learning: Imposing exact units equivariance
- Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu; (109):1−32, 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; (110):1−43, 2023.
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- FLIP: A Utility Preserving Privacy Mechanism for Time Series
- Tucker McElroy, Anindya Roy, Gaurab Hore; (111):1−29, 2023.
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- The Hyperspherical Geometry of Community Detection: Modularity as a Distance
- Martijn Gösgens, Remco van der Hofstad, Nelly Litvak; (112):1−36, 2023.
[abs][pdf][bib] [code]
- Generalization Bounds for Adversarial Contrastive Learning
- Xin Zou, Weiwei Liu; (114):1−54, 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; (115):1−53, 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; (116):1−9, 2023. (Machine Learning Open Source Software Paper)
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- Deep linear networks can benignly overfit when shallow ones do
- Niladri S. Chatterji, Philip M. Long; (117):1−39, 2023.
[abs][pdf][bib] [code]
- A Unified Framework for Optimization-Based Graph Coarsening
- Manoj Kumar, Anurag Sharma, Sandeep Kumar; (118):1−50, 2023.
[abs][pdf][bib] [code]
- An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks
- Stefan Stein, Chenlei Leng; (119):1−69, 2023.
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- Maximum likelihood estimation in Gaussian process regression is ill-posed
- Toni Karvonen, Chris J. Oates; (120):1−47, 2023.
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- Minimal Width for Universal Property of Deep RNN
- Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang; (121):1−41, 2023.
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- Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
- Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock; (122):1−77, 2023.
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- Benign overfitting in ridge regression
- Alexander Tsigler, Peter L. Bartlett; (123):1−76, 2023.
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- HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
- Weijie J. Su, Yuancheng Zhu; (124):1−53, 2023.
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- Statistical Robustness of Empirical Risks in Machine Learning
- Shaoyan Guo, Huifu Xu, Liwei Zhang; (125):1−38, 2023.
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- Euler-Lagrange Analysis of Generative Adversarial Networks
- Siddarth Asokan, Chandra Sekhar Seelamantula; (126):1−100, 2023.
[abs][pdf][bib] [code]
- Graph Clustering with Graph Neural Networks
- Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller; (127):1−21, 2023.
[abs][pdf][bib] [code]
- An Eigenmodel for Dynamic Multilayer Networks
- Joshua Daniel Loyal, Yuguo Chen; (128):1−69, 2023.
[abs][pdf][bib] [code]
- 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; (129):1−23, 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 Velickovic; (130):1−61, 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; (131):1−44, 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; (132):1−57, 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; (133):1−57, 2023.
[abs][pdf][bib] [code]
- MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation
- Qian Li, Binyan Jiang, Defeng Sun; (134):1−44, 2023.
[abs][pdf][bib]
- Sparse GCA and Thresholded Gradient Descent
- Sheng Gao, Zongming Ma; (135):1−61, 2023.
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- Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
- Wenhao Li, Ningyuan Chen, L. Jeff Hong; (136):1−84, 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; (137):1−97, 2023.
[abs][pdf][bib] [code]
- Asymptotics of Network Embeddings Learned via Subsampling
- Andrew Davison, Morgane Austern; (138):1−120, 2023.
[abs][pdf][bib] [code]
- Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
- Ben Hambly, Renyuan Xu, Huining Yang; (139):1−56, 2023.
[abs][pdf][bib]
- Jump Interval-Learning for Individualized Decision Making with Continuous Treatments
- Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu; (140):1−92, 2023.
[abs][pdf][bib] [code]
- Optimal Convergence Rates for Distributed Nystroem Approximation
- Jian Li, Yong Liu, Weiping Wang; (141):1−39, 2023.
[abs][pdf][bib] [code]
- On Tilted Losses in Machine Learning: Theory and Applications
- Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith; (142):1−79, 2023.
[abs][pdf][bib] [code]
- Large sample spectral analysis of graph-based multi-manifold clustering
- Nicolas Garcia Trillos, Pengfei He, Chenghui Li; (143):1−71, 2023.
[abs][pdf][bib] [code]
- Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering
- Noirrit Kiran Chandra, Antonio Canale, David B. Dunson; (144):1−42, 2023.
[abs][pdf][bib]
- Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
- Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang; (145):1−46, 2023.
[abs][pdf][bib] [code]
- Off-Policy Actor-Critic with Emphatic Weightings
- Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White; (146):1−63, 2023.
[abs][pdf][bib] [code]
- Stochastic Optimization under Distributional Drift
- Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui; (147):1−56, 2023.
[abs][pdf][bib]
- Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
- Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang; (148):1−63, 2023.
[abs][pdf][bib]
- Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
- Titouan Vayer, Rémi Gribonval; (149):1−51, 2023.
[abs][pdf][bib]
- 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; (150):1−12, 2023. (Machine Learning Open Source Software Paper)
[abs][pdf][bib] [code]
- Generalization error bounds for multiclass sparse linear classifiers
- Tomer Levy, Felix Abramovich; (151):1−35, 2023.
[abs][pdf][bib]
- Selective inference for k-means clustering
- Yiqun T. Chen, Daniela M. Witten; (152):1−41, 2023.
[abs][pdf][bib] [code]
- Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process
- Cheng Zeng, Jeffrey W Miller, Leo L Duan; (153):1−32, 2023.
[abs][pdf][bib] [code]
- Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
- Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd; (154):1−48, 2023.
[abs][pdf][bib] [code]
- Adaptive Data Depth via Multi-Armed Bandits
- Tavor Baharav, Tze Leung Lai; (155):1−29, 2023.
[abs][pdf][bib]
- Integrating Random Effects in Deep Neural Networks
- Giora Simchoni, Saharon Rosset; (156):1−57, 2023.
[abs][pdf][bib] [code]
- Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity
- Huan Li, Zhouchen Lin; (157):1−37, 2023.
[abs][pdf][bib] [code]
- Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
- Hamid Reza Feyzmahdavian, Mikael Johansson; (158):1−75, 2023.
[abs][pdf][bib]
- Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
- Arnab Ganguly, Riten Mitra, Jinpu Zhou; (159):1−39, 2023.
[abs][pdf][bib]
- Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling
- Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron; (160):1−65, 2023.
[abs][pdf][bib] [code]
- Flexible Model Aggregation for Quantile Regression
- Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani; (162):1−45, 2023.
[abs][pdf][bib] [code]
- Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
- Gavin Zhang, Salar Fattahi, Richard Y. Zhang; (163):1−55, 2023.
[abs][pdf][bib]
- A Framework and Benchmark for Deep Batch Active Learning for Regression
- David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart; (164):1−81, 2023.
[abs][pdf][bib] [code]
- Robust Methods for High-Dimensional Linear Learning
- Ibrahim Merad, Stéphane Gaïffas; (165):1−44, 2023.
[abs][pdf][bib]
- A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition
- Masaru Ito, Zhaosong Lu, Chuan He; (166):1−34, 2023.
[abs][pdf][bib]
- Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
- Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo; (167):1−37, 2023.
[abs][pdf][bib] [code]
- Inference on the Change Point under a High Dimensional Covariance Shift
- Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis; (168):1−68, 2023.
[abs][pdf][bib]
- DART: Distance Assisted Recursive Testing
- Xuechan Li, Anthony D. Sung, Jichun Xie; (169):1−41, 2023.
[abs][pdf][bib]
- Small Transformers Compute Universal Metric Embeddings
- Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić; (170):1−48, 2023.
[abs][pdf][bib] [code]
- Incremental Learning in Diagonal Linear Networks
- Raphaël Berthier; (171):1−26, 2023.
[abs][pdf][bib]
- Beyond the Golden Ratio for Variational Inequality Algorithms
- Ahmet Alacaoglu, Axel Böhm, Yura Malitsky; (172):1−33, 2023.
[abs][pdf][bib] [code]
- From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
- Johannes Resin; (173):1−21, 2023.
[abs][pdf][bib]
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