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ORCIDJournal of Machine Learning Research, Volume 24
Volume 24, 2023
- Benjamin Moseley, Joshua R. Wang:
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. 1:1-1:36 - Håvard Kvamme, Ørnulf Borgan:
The Brier Score under Administrative Censoring: Problems and a Solution. 2:1-2:26 - Leo L. Duan, George Michailidis, Mingzhou Ding:
Bayesian Spiked Laplacian Graphs. 3:1-3:35 - Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner:
Efficient Structure-preserving Support Tensor Train Machine. 4:1-4:22 - Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey:
Cluster-Specific Predictions with Multi-Task Gaussian Processes. 5:1-5:49 - Haifeng Jin, François Chollet, Qingquan Song, Xia Hu:
AutoKeras: An AutoML Library for Deep Learning. 6:1-6:6 - Tianhong Sheng, Bharath K. Sriperumbudur:
On Distance and Kernel Measures of Conditional Dependence. 7:1-7:16 - Radu Ioan Bot, Michael Sedlmayer, Phan Tu Vuong:
A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs. 8:1-8:37 - Hanbaek Lyu, Facundo Mémoli, David Sivakoff:
Sampling random graph homomorphisms and applications to network data analysis. 9:1-9:79 - Michael O'Neill, Stephen J. Wright:
A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees. 10:1-10:34 - Vojtech Franc, Daniel Prusa, Václav Vorácek:
Optimal Strategies for Reject Option Classifiers. 11:1-11:49 - Emanuele Dolera, Stefano Favaro, Stefano Peluchetti:
Learning-augmented count-min sketches via Bayesian nonparametrics. 12:1-12:60 - Hédi Hadiji, Gilles Stoltz:
Adaptation to the Range in K-Armed Bandits. 13:1-13:33 - Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu:
Python package for causal discovery based on LiNGAM. 14:1-14:8 - Jon Vadillo, Roberto Santana, José Antonio Lozano:
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions. 15:1-15:42 - Cynthia Rudin, Yaron Shaposhnik:
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. 16:1-16:44 - Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi:
Learning Mean-Field Games with Discounted and Average Costs. 17:1-17:59 - Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis:
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization. 18:1-18:41 - Konstantinos Perrakis, Thomas Lartigue
, Frank Dondelinger, Sach Mukherjee:
Regularized Joint Mixture Models. 19:1-19:47 - Tengyuan Liang, Benjamin Recht:
Interpolating Classifiers Make Few Mistakes. 20:1-20:27 - Pouya M. Ghari, Yanning Shen:
Graph-Aided Online Multi-Kernel Learning. 21:1-21:44 - Kaiyi Ji, Yingbin Liang:
Lower Bounds and Accelerated Algorithms for Bilevel Optimization. 22:1-22:56 - Eli N. Weinstein, Jeffrey W. Miller:
Bayesian Data Selection. 23:1-23:72 - Shai Feldman, Stephen Bates, Yaniv Romano:
Calibrated Multiple-Output Quantile Regression with Representation Learning. 24:1-24:48 - Cédric M. Campos, Alejandro Mahillo, David Martín de Diego:
Discrete Variational Calculus for Accelerated Optimization. 25:1-25:33 - Hao Wang, Rui Gao, Flávio P. Calmon:
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels. 26:1-26:43 - Raj Agrawal, Tamara Broderick:
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. 27:1-27:60 - Xuran Meng, Jeff Yao:
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping. 28:1-28:40 - Fábio Malcher Miranda, Niklas Köhnecke, Bernhard Y. Renard:
HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn. 29:1-29:17 - Cody Lewis, Vijay Varadharajan, Nasimul Noman:
Attacks against Federated Learning Defense Systems and their Mitigation. 30:1-30:50 - Shiyu Duan, Spencer Chang, José C. Príncipe:
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels. 31:1-31:35 - Dimitris Bertsimas, Driss Lahlou Kitane:
Sparse PCA: a Geometric Approach. 32:1-32:33 - Boyu Wang, Jorge A. Mendez, Changjian Shui, Fan Zhou, Di Wu, Gezheng Xu, Christian Gagné, Eric Eaton:
Gap Minimization for Knowledge Sharing and Transfer. 33:1-33:57 - Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. 34:1-34:11 - Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? 35:1-35:52 - Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou:
Label Distribution Changing Learning with Sample Space Expanding. 36:1-36:48 - Michael Unser:
Ridges, Neural Networks, and the Radon Transform. 37:1-37:33 - Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. 38:1-38:46 - Julián Tachella, Dongdong Chen, Mike E. Davies:
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems. 39:1-39:45 - Shaun M. Fallat, David G. Kirkpatrick, Hans Ulrich Simon, Abolghasem Soltani, Sandra Zilles:
On Batch Teaching Without Collusion. 40:1-40:33 - Shaowu Pan, Steven L. Brunton, J. Nathan Kutz:
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. 41:1-41:60 - Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. 42:1-42:63 - Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson:
Benchmarking Graph Neural Networks. 43:1-43:48 - Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. 44:1-44:46 - Nicolás García Trillos, Matt Jacobs, Jakwang Kim:
The multimarginal optimal transport formulation of adversarial multiclass classification. 45:1-45:56 - Tobias Fritz, Andreas Klingler:
The d-Separation Criterion in Categorical Probability. 46:1-46:49 - Haizi Yu, Igor Mineyev, Lav R. Varshney:
A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering. 47:1-47:61 - Xiaoyu Wang, Yaxiang Yuan:
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size. 48:1-48:49 - Mridul Agarwal, Vaneet Aggarwal:
Reinforcement Learning for Joint Optimization of Multiple Rewards. 49:1-49:41 - Linxi Liu, Dangna Li, Wing Hung Wong:
Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning. 50:1-50:64 - Lingjun Li, Jun Li:
Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks. 51:1-51:44 - Kunal Pattanayak, Vikram Krishnamurthy:
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems. 52:1-52:64 - Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. 53:1-53:45 - Chen Lu, Subhabrata Sen:
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity. 54:1-54:34 - Shaogao Lv, Xin He, Junhui Wang:
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches. 55:1-55:38 - Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch:
On the geometry of Stein variational gradient descent. 56:1-56:39 - Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová:
Tree-AMP: Compositional Inference with Tree Approximate Message Passing. 57:1-57:89 - Yan Shuo Tan, Roman Vershynin:
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval. 58:1-58:47 - Ephy R. Love, Benjamin Filippenko, Vasileios Maroulas, Gunnar E. Carlsson:
Topological Convolutional Layers for Deep Learning. 59:1-59:35 - Qinbo Bai, Vaneet Aggarwal, Ather Gattami:
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints. 60:1-60:25 - Christian Horvat, Jean-Pascal Pfister:
Density estimation on low-dimensional manifolds: an inflation-deflation approach. 61:1-61:37 - Kamélia Daudel, Randal Douc, François Roueff:
Monotonic Alpha-divergence Minimisation for Variational Inference. 62:1-62:76 - Marcelo Arenas, Pablo Barceló, Leopoldo E. Bertossi, Mikaël Monet:
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results. 63:1-63:58 - Lan V. Truong:
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity. 64:1-64:49 - Nikhil Iyer, V. Thejas, Nipun Kwatra, Ramachandran Ramjee, Muthian Sivathanu:
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule. 65:1-65:37 - Gianluca Finocchio, Johannes Schmidt-Hieber:
Posterior Contraction for Deep Gaussian Process Priors. 66:1-66:49 - Eliezer de Souza da Silva, Tomasz Kusmierczyk, Marcelo Hartmann, Arto Klami:
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching. 67:1-67:51 - Ruoyu Wang, Miaomiao Su, Qihua Wang:
Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data. 68:1-68:52 - Hau-Tieng Wu, Nan Wu:
When Locally Linear Embedding Hits Boundary. 69:1-69:80 - Jonathan Hillman, Toby Dylan Hocking:
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection. 70:1-70:24 - Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau:
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. 71:1-71:57 - Moran Feldman, Christopher Harshaw, Amin Karbasi:
How Do You Want Your Greedy: Simultaneous or Repeated? 72:1-72:87 - Chunlin Li, Xiaotong Shen, Wei Pan:
Inference for a Large Directed Acyclic Graph with Unspecified Interventions. 73:1-73:48 - Jordan Awan, Vinayak Rao:
Privacy-Aware Rejection Sampling. 74:1-74:32 - Ximena Fernández, Eugenio Borghini, Gabriel B. Mindlin, Pablo Groisman:
Intrinsic Persistent Homology via Density-based Metric Learning. 75:1-75:42 - Di Bo, Hoon Hwangbo, Vinit Sharma, Corey Arndt, Stephanie TerMaath:
A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection. 76:1-76:31 - Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. 77:1-77:42 - Glen Berseth, Florian Golemo, Christopher Pal:
Towards Learning to Imitate from a Single Video Demonstration. 78:1-78:26 - Snigdha Panigrahi, Peter W. MacDonald, Daniel Kessler:
Approximate Post-Selective Inference for Regression with the Group LASSO. 79:1-79:49 - Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. 80:1-80:69 - Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill:
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics. 81:1-81:36 - Ning Ning, Edward L. Ionides:
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality. 82:1-82:76 - William J. Wilkinson, Simo Särkkä, Arno Solin:
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees. 83:1-83:50 - Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi:
Online Optimization over Riemannian Manifolds. 84:1-84:67 - Henry Lam, Haofeng Zhang:
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence. 85:1-85:58 - George Stepaniants:
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces. 86:1-86:72 - Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll:
Gaussian Processes with Errors in Variables: Theory and Computation. 87:1-87:53 - Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson:
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data. 88:1-88:49 - Nikola B. Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. 89:1-89:97 - Bernadette J. Stolz:
Outlier-Robust Subsampling Techniques for Persistent Homology. 90:1-90:35 - Likai Chen, Georg Keilbar, Wei Biao Wu:
Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds. 91:1-91:25 - Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang:
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption. 92:1-92:46 - Yucheng Lu, Christopher De Sa:
Decentralized Learning: Theoretical Optimality and Practical Improvements. 93:1-93:62 - Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. 94:1-94:42 - Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu:
Statistical Inference for Noisy Incomplete Binary Matrix. 95:1-95:66 - Jianhao Ma, Salar Fattahi:
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization. 96:1-96:84 - Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu:
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. 97:1-97:30 - Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
An Analysis of Robustness of Non-Lipschitz Networks. 98:1-98:43 - Artem Vysogorets, Julia Kempe:
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity. 99:1-99:23 - Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu:
FedLab: A Flexible Federated Learning Framework. 100:1-100:7 - Didong Li, Wenpin Tang, Sudipto Banerjee:
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds. 101:1-101:26 - Haixu Ma, Donglin Zeng, Yufeng Liu:
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments. 102:1-102:48 - Michael R. Metel:
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint. 103:1-103:44 - Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang:
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics. 104:1-104:42 - Bahare Fatemi, Perouz Taslakian, David Vázquez, David Poole:
Knowledge Hypergraph Embedding Meets Relational Algebra. 105:1-105:34 - Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch:
Concentration analysis of multivariate elliptic diffusions. 106:1-106:38 - Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile:
Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption. 107:1-107:31 - Michail Spitieris, Ingelin Steinsland:
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors. 108:1-108:39 - Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu:
Dimensionless machine learning: Imposing exact units equivariance. 109:1-109:32 - Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. 110:1-110:43 - Tucker McElroy, Anindya Roy, Gaurab Hore:
FLIP: A Utility Preserving Privacy Mechanism for Time Series. 111:1-111:29 - Martijn Gösgens, Remco van der Hofstad, Nelly Litvak:
The Hyperspherical Geometry of Community Detection: Modularity as a Distance. 112:1-112:36 - Ohad Shamir:
The Implicit Bias of Benign Overfitting. 113:1-113:40 - Xin Zou, Weiwei Liu:
Generalization Bounds for Adversarial Contrastive Learning. 114:1-114:54 - Chengzhuo Ni, Yaqi Duan, Munther A. Dahleh, Mengdi Wang, Anru R. Zhang:
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. 115:1-115:53 - Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, Chaochao Chen:
SQLFlow: An Extensible Toolkit Integrating DB and AI. 116:1-116:9 - Niladri S. Chatterji, Philip M. Long:
Deep linear networks can benignly overfit when shallow ones do. 117:1-117:39 - Manoj Kumar, Anurag Sharma, Sandeep Kumar:
A Unified Framework for Optimization-Based Graph Coarsening. 118:1-118:50 - Stefan Stein, Chenlei Leng:
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks. 119:1-119:69 - Toni Karvonen, Chris J. Oates:
Maximum likelihood estimation in Gaussian process regression is ill-posed. 120:1-120:47 - Changhoon Song, Geonho Hwang, Junho Lee, Myungjoo Kang:
Minimal Width for Universal Property of Deep RNN. 121:1-121:41 - Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock:
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. 122:1-122:77 - Alexander Tsigler, Peter L. Bartlett:
Benign overfitting in ridge regression. 123:1-123:76 - Weijie J. Su, Yuancheng Zhu:
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation. 124:1-124:53 - Shaoyan Guo, Huifu Xu, Liwei Zhang:
Statistical Robustness of Empirical Risks in Machine Learning. 125:1-125:38 - Siddarth Asokan, Chandra Sekhar Seelamantula:
Euler-Lagrange Analysis of Generative Adversarial Networks. 126:1-126:100 - Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. 127:1-127:21 - Joshua Daniel Loyal, Yuguo Chen:
An Eigenmodel for Dynamic Multilayer Networks. 128:1-128:69 - Xinchi Qiu, Titouan Parcollet, Javier Fernández-Marqués, Pedro P. B. de Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane:
A First Look into the Carbon Footprint of Federated Learning. 129:1-129:23