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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.
[abs][pdf][bib]

The Brier Score under Administrative Censoring: Problems and a Solution
Håvard Kvamme, Ørnulf Borgan; (2):1−26, 2023.
[abs][pdf][bib]

Bayesian Spiked Laplacian Graphs
Leo L Duan, George Michailidis, Mingzhou Ding; (3):1−35, 2023.
[abs][pdf][bib]      [code]

Efficient Structure-preserving Support Tensor Train Machine
Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner; (4):1−22, 2023.
[abs][pdf][bib]      [code]

Cluster-Specific Predictions with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey; (5):1−49, 2023.
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

On Distance and Kernel Measures of Conditional Dependence
Tianhong Sheng, Bharath K. Sriperumbudur; (7):1−16, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]

Sampling random graph homomorphisms and applications to network data analysis
Hanbaek Lyu, Facundo Memoli, David Sivakoff; (9):1−79, 2023.
[abs][pdf][bib]      [code]

A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
Michael J. O'Neill, Stephen J. Wright; (10):1−34, 2023.
[abs][pdf][bib]

Optimal Strategies for Reject Option Classifiers
Vojtech Franc, Daniel Prusa, Vaclav Voracek; (11):1−49, 2023.
[abs][pdf][bib]

Learning-augmented count-min sketches via Bayesian nonparametrics
Emanuele Dolera, Stefano Favaro, Stefano Peluchetti; (12):1−60, 2023.
[abs][pdf][bib]

Adaptation to the Range in K-Armed Bandits
Hédi Hadiji, Gilles Stoltz; (13):1−33, 2023.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano; (15):1−42, 2023.
[abs][pdf][bib]      [code]

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
Cynthia Rudin, Yaron Shaposhnik; (16):1−44, 2023.
[abs][pdf][bib]      [code]

Learning Mean-Field Games with Discounted and Average Costs
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi; (17):1−59, 2023.
[abs][pdf][bib]

An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis; (18):1−41, 2023.
[abs][pdf][bib]      [code]

Regularized Joint Mixture Models
Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee; (19):1−47, 2023.
[abs][pdf][bib]      [code]

Interpolating Classifiers Make Few Mistakes
Tengyuan Liang, Benjamin Recht; (20):1−27, 2023.
[abs][pdf][bib]

Graph-Aided Online Multi-Kernel Learning
Pouya M. Ghari, Yanning Shen; (21):1−44, 2023.
[abs][pdf][bib]      [code]

Lower Bounds and Accelerated Algorithms for Bilevel Optimization
Kaiyi ji, Yingbin Liang; (22):1−56, 2023.
[abs][pdf][bib]

Bayesian Data Selection
Eli N. Weinstein, Jeffrey W. Miller; (23):1−72, 2023.
[abs][pdf][bib]      [code]

Calibrated Multiple-Output Quantile Regression with Representation Learning
Shai Feldman, Stephen Bates, Yaniv Romano; (24):1−48, 2023.
[abs][pdf][bib]      [code]

Discrete Variational Calculus for Accelerated Optimization
Cédric M. Campos, Alejandro Mahillo, David Martín de Diego; (25):1−33, 2023.
[abs][pdf][bib]      [code]

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, Flavio P. Calmon; (26):1−43, 2023.
[abs][pdf][bib]

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Raj Agrawal, Tamara Broderick; (27):1−60, 2023.
[abs][pdf][bib]

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)
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

Attacks against Federated Learning Defense Systems and their Mitigation
Cody Lewis, Vijay Varadharajan, Nasimul Noman; (30):1−50, 2023.
[abs][pdf][bib]      [code]

Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe; (31):1−35, 2023.
[abs][pdf][bib]

Sparse PCA: a Geometric Approach
Dimitris Bertsimas, Driss Lahlou Kitane; (32):1−33, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

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)
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]

Label Distribution Changing Learning with Sample Space Expanding
Chao Xu, Hong Tao, Jing Zhang, Dewen Hu, Chenping Hou; (36):1−48, 2023.
[abs][pdf][bib]

Ridges, Neural Networks, and the Radon Transform
Michael Unser; (37):1−33, 2023.
[abs][pdf][bib]

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis; (38):1−46, 2023.
[abs][pdf][bib]

Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies; (39):1−45, 2023.
[abs][pdf][bib]

On Batch Teaching Without Collusion
Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles; (40):1−33, 2023.
[abs][pdf][bib]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson; (43):1−48, 2023.
[abs][pdf][bib]      [code]

Robust Load Balancing with Machine Learned Advice
Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng; (44):1−46, 2023.
[abs][pdf][bib]

The multimarginal optimal transport formulation of adversarial multiclass classification
Nicolás García Trillos, Matt Jacobs, Jakwang Kim; (45):1−56, 2023.
[abs][pdf][bib]

The d-Separation Criterion in Categorical Probability
Tobias Fritz, Andreas Klingler; (46):1−49, 2023.
[abs][pdf][bib]

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