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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 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 - Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic:
Combinatorial Optimization and Reasoning with Graph Neural Networks. 130:1-130:61 - Patricia Wollstadt, Sebastian Schmitt, Michael Wibral:
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition. 131:1-131:44 - Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu:
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data. 132:1-132:57 - Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill:
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators. 133:1-133:57 - Qian Li, Binyan Jiang, Defeng Sun:
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation. 134:1-134:44 - Sheng Gao, Zongming Ma:
Sparse GCA and Thresholded Gradient Descent. 135:1-135:61 - Wenhao Li, Ningyuan Chen, L. Jeff Hong:
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection. 136:1-136:84 - Hui Jin, Guido Montúfar:
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. 137:1-137:97 - Andrew Davison, Morgane Austern:
Asymptotics of Network Embeddings Learned via Subsampling. 138:1-138:120 - Ben M. Hambly, Renyuan Xu, Huining Yang:
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games. 139:1-139:56 - Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu:
Jump Interval-Learning for Individualized Decision Making with Continuous Treatments. 140:1-140:92 - Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence Rates for Distributed Nystroem Approximation. 141:1-141:39 - Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith:
On Tilted Losses in Machine Learning: Theory and Applications. 142:1-142:79 - Nicolás García Trillos, Pengfei He, Chenghui Li:
Large sample spectral analysis of graph-based multi-manifold clustering. 143:1-143:71 - Noirrit Kiran Chandra, Antonio Canale, David B. Dunson:
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. 144:1-144:42 - Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. 145:1-145:46 - Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White:
Off-Policy Actor-Critic with Emphatic Weightings. 146:1-146:63 - Joshua Cutler, Dmitriy Drusvyatskiy, Zaïd Harchaoui:
Stochastic Optimization under Distributional Drift. 147:1-147:56 - Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang:
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. 148:1-148:63 - Titouan Vayer, Rémi Gribonval:
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning. 149:1-149:51 - Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang:
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning. 150:1-150:12 - Tomer Levy, Felix Abramovich:
Generalization error bounds for multiclass sparse linear classifiers. 151:1-151:35 - Yiqun T. Chen, Daniela M. Witten:
Selective inference for k-means clustering. 152:1-152:41 - Cheng Zeng, Jeffrey W. Miller, Leo L. Duan:
Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process. 153:1-153:32 - Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd:
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. 154:1-154:48 - Tavor Z. Baharav, Tze Leung Lai:
Adaptive Data Depth via Multi-Armed Bandits. 155:1-155:29 - Giora Simchoni, Saharon Rosset:
Integrating Random Effects in Deep Neural Networks. 156:1-156:57 - Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity. 157:1-157:37 - Hamid Reza Feyzmahdavian, Mikael Johansson:
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees. 158:1-158:75 - Arnab Ganguly, Riten Mitra, Jinpu Zhou:
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations. 159:1-159:39 - Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron:
Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling. 160:1-160:65 - Yanwei Jia, Xun Yu Zhou:
q-Learning in Continuous Time. 161:1-161:61 - Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Flexible Model Aggregation for Quantile Regression. 162:1-162:45 - Gavin Zhang, Salar Fattahi, Richard Y. Zhang:
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer-Monteiro Factorization with Global Optimality Certification. 163:1-163:55 - David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart:
A Framework and Benchmark for Deep Batch Active Learning for Regression. 164:1-164:81 - Ibrahim Merad, Stéphane Gaïffas:
Robust Methods for High-Dimensional Linear Learning. 165:1-165:44 - Masaru Ito, Zhaosong Lu, Chuan He:
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition. 166:1-166:34 - Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. 167:1-167:37 - Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis:
Inference on the Change Point under a High Dimensional Covariance Shift. 168:1-168:68 - Xuechan Li, Anthony D. Sung, Jichun Xie:
DART: Distance Assisted Recursive Testing. 169:1-169:41 - Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanic:
Small Transformers Compute Universal Metric Embeddings. 170:1-170:48 - Raphaël Berthier:
Incremental Learning in Diagonal Linear Networks. 171:1-171:26 - Ahmet Alacaoglu, Axel Böhm, Yura Malitsky:
Beyond the Golden Ratio for Variational Inequality Algorithms. 172:1-172:33 - Johannes Resin:
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions. 173:1-173:21 - Xiao Fang, Malay Ghosh:
Posterior Consistency for Bayesian Relevance Vector Machines. 174:1-174:17 - Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang:
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. 175:1-175:53 - Patrick F. Burauel:
Evaluating Instrument Validity using the Principle of Independent Mechanisms. 176:1-176:56 - Sevvandi Kandanaarachchi, Kate Smith-Miles:
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory. 177:1-177:52 - Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan, Hongyuan Zha:
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning. 178:1-178:75 - Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration:
Variational Inference for Deblending Crowded Starfields. 179:1-179:36 - Jose H. Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang:
Dropout Training is Distributionally Robust Optimal. 180:1-180:60 - Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee:
Factor Graph Neural Networks. 181:1-181:54 - Justin Grimmer, Dean Knox, Brandon M. Stewart:
Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding. 182:1-182:70 - Fenglei Fan, Rongjie Lai, Ge Wang:
Quasi-Equivalence between Width and Depth of Neural Networks. 183:1-183:22 - Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. 184:1-184:20 - Rodrigue Siry, Ryan Webster, Loïc Simon, Julien Rabin:
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity. 185:1-185:34 - Jun Shu, Deyu Meng, Zongben Xu:
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks. 186:1-186:74 - Gecia Bravo Hermsdorff, Lee M. Gunderson, Pierre-André G. Maugis, Carey E. Priebe:
Quantifying Network Similarity using Graph Cumulants. 187:1-187:27 - Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen:
The Proximal ID Algorithm. 188:1-188:46 - Lukas Gonon:
Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality. 189:1-189:51 - Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg:
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. 190:1-190:56 - Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg:
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. 191:1-191:83 - Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao:
PAC-learning for Strategic Classification. 192:1-192:38 - Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts:
Divide-and-Conquer Fusion. 193:1-193:82 - Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton:
MMD Aggregated Two-Sample Test. 194:1-194:81 - Santtu Tikka, Jouni Helske, Juha Karvanen:
Clustering and Structural Robustness in Causal Diagrams. 195:1-195:32 - Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann:
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. 196:1-196:72 - Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum:
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency. 197:1-197:65 - Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baró, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu:
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges. 198:1-198:6 - Ali Devran Kara, Naci Saldi, Serdar Yüksel:
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity. 199:1-199:34 - Junsouk Choi, Yang Ni:
Model-based Causal Discovery for Zero-Inflated Count Data. 200:1-200:32 - Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng:
Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations. 201:1-201:60 - Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff:
Multiplayer Performative Prediction: Learning in Decision-Dependent Games. 202:1-202:56 - Lili Su, Jiaming Xu, Pengkun Yang:
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. 203:1-203:48 - Yi-Rui Yang, Wu-Jun Li:
Buffered Asynchronous SGD for Byzantine Learning. 204:1-204:62 - Hussein Hazimeh, Rahul Mazumder, Tim Nonet:
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization. 205:1-205:8 - Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. 206:1-206:70 - Nate Veldt, Austin R. Benson, Jon M. Kleinberg:
Augmented Sparsifiers for Generalized Hypergraph Cuts. 207:1-207:50 - Santiago Mazuelas, Mauricio Romero, Peter Grunwald:
Minimax Risk Classifiers with 0-1 Loss. 208:1-208:48 - Baijiong Lin, Yu Zhang:
LibMTL: A Python Library for Deep Multi-Task Learning. 209:1-209:7 - Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio:
GFlowNet Foundations. 210:1-210:55 - Fan Chen, Zhenjie Ren, Songbo Wang:
Entropic Fictitious Play for Mean Field Optimization Problem. 211:1-211:36 - Wei Liu, Xin Liu, Xiaojun Chen:
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity. 212:1-212:43 - Marcel Wienöbst, Max Bannach, Maciej Liskiewicz:
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications. 213:1-213:45 - Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell:
An Empirical Investigation of the Role of Pre-training in Lifelong Learning. 214:1-214:50 - Haili Zhang, Zhaobo Liu, Guohua Zou:
Least Squares Model Averaging for Distributed Data. 215:1-215:59 - Malte Londschien, Peter Bühlmann, Solt Kovács:
Random Forests for Change Point Detection. 216:1-216:45 - Yu-Jui Huang, Yuchong Zhang:
GANs as Gradient Flows that Converge. 217:1-217:40 - Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang:
Adaptation Augmented Model-based Policy Optimization. 218:1-218:35 - Hua Liu, Jinhong You, Jiguo Cao:
Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data. 219:1-219:41 - Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee:
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning. 220:1-220:32 - Doudou Zhou, Tianxi Cai, Junwei Lu:
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices. 221:1-221:43 - Mo Zhou, Jianfeng Lu:
Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees. 222:1-222:34 - Bingqing Hu, Bin Nan:
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data. 223:1-223:26 - Ben Dai, Chunlin Li:
RankSEG: A Consistent Ranking-based Framework for Segmentation. 224:1-224:50 - T. Mitchell Roddenberry, Santiago Segarra:
Limits of Dense Simplicial Complexes. 225:1-225:42 - 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, Dae-ki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven C. H. Hoi, Huan Wang:
Merlion: End-to-End Machine Learning for Time Series. 226:1-226:6 - Binyan Jiang, Jialiang Li, Qiwei Yao:
Autoregressive Networks. 227:1-227:69 - Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon:
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure. 228:1-228:38 - Connor Lawless, Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Interpretable and Fair Boolean Rule Sets via Column Generation. 229:1-229:50 - Zhengyu Zhou, Wei Liu:
Sample Complexity for Distributionally Robust Learning under chi-square divergence. 230:1-230:27 - Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin:
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. 231:1-231:37 - Xiaoyu Wang, Martin Benning:
Lifted Bregman Training of Neural Networks. 232:1-232:51 - Max Olan Smith, Thomas W. Anthony, Michael P. Wellman:
Strategic Knowledge Transfer. 233:1-233:96 - Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov:
MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning. 234:1-234:7 - Ziyue Wang, Zhiqiang Tan:
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models. 235:1-235:112 - Samuel N. Cohen, Deqing Jiang, Justin A. Sirignano:
Neural Q-learning for solving PDEs. 236:1-236:49 - Madhumitha Shridharan, Garud Iyengar:
Scalable Computation of Causal Bounds. 237:1-237:35 - M. E. J. Newman:
Efficient Computation of Rankings from Pairwise Comparisons. 238:1-238:25 - Oh-Ran Kwon, Hui Zou:
Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification. 239:1-239:40 - 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:
PaLM: Scaling Language Modeling with Pathways. 240:1-240:113 - Zhuang Yang:
Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning. 241:1-241:29 - Andrea Cini, Daniele Zambon, Cesare Alippi:
Sparse Graph Learning from Spatiotemporal Time Series. 242:1-242:36 - Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet:
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics. 243:1-243:83 - Ying Jin, Emmanuel J. Candès:
Selection by Prediction with Conformal p-values. 244:1-244:41 - Yibo Yan, Xiaozhou Wang, Riquan Zhang:
Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing. 245:1-245:49 - Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath:
Graph Attention Retrospective. 246:1-246:52 - Mengyu Li, Jun Yu, Tao Li, Cheng Meng:
Importance Sparsification for Sinkhorn Algorithm. 247:1-247:44 - Philippe Gagnon, Florian Maire, Giacomo Zanella:
Improving multiple-try Metropolis with local balancing. 248:1-248:59 - Tianze Wang, Guanyang Wang:
Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC. 249:1-249:40 - Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Convex Reinforcement Learning in Finite Trials. 250:1-250:42 - Gautier Izacard, Patrick S. H. Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave:
Atlas: Few-shot Learning with Retrieval Augmented Language Models. 251:1-251:43 - Xintao Xia, Zhanrui Cai:
Adaptive False Discovery Rate Control with Privacy Guarantee. 252:1-252:35 - Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat:
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. 253:1-253:15 - Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba:
skrl: Modular and Flexible Library for Reinforcement Learning. 254:1-254:9 - Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander V. Veidenbaum:
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures. 255:1-255:10 - Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White:
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks. 256:1-256:34 - Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio:
Fairlearn: Assessing and Improving Fairness of AI Systems. 257:1-257:8
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