JMLR Volume 25
- On Truthing Issues in Supervised Classification
- Jonathan K. Su; (1):1−91, 2024.
[abs][pdf][bib]
- Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
- Yuze Han, Guangzeng Xie, Zhihua Zhang; (2):1−86, 2024.
[abs][pdf][bib]
- Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic
- Zheng Tracy Ke, Jun S. Liu, Yucong Ma; (3):1−67, 2024.
[abs][pdf][bib]
- Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
- Shicong Cen, Yuting Wei, Yuejie Chi; (4):1−48, 2024.
[abs][pdf][bib]
- Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method
- Ernesto Araya, Guillaume Braun, Hemant Tyagi; (5):1−43, 2024.
[abs][pdf][bib]
[code]
- Model-Free Representation Learning and Exploration in Low-Rank MDPs
- Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal; (6):1−76, 2024.
[abs][pdf][bib]
- Decorrelated Variable Importance
- Isabella Verdinelli, Larry Wasserman; (7):1−27, 2024.
[abs][pdf][bib]
- On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
- Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh; (8):1−46, 2024.
[abs][pdf][bib]
[code]
- Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
- Zixian Yang, Xin Liu, Lei Ying; (9):1−55, 2024.
[abs][pdf][bib]
- Modeling Random Networks with Heterogeneous Reciprocity
- Daniel Cirkovic, Tiandong Wang; (10):1−40, 2024.
[abs][pdf][bib]
- Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design
- Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov; (11):1−37, 2024.
[abs][pdf][bib]
- Critically Assessing the State of the Art in Neural Network Verification
- Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn; (12):1−53, 2024.
[abs][pdf][bib]
- A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
- Stefan Ankirchner, Stefan Perko; (13):1−55, 2024.
[abs][pdf][bib]
- Improving physics-informed neural networks with meta-learned optimization
- Alex Bihlo; (14):1−26, 2024.
[abs][pdf][bib]
- On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
- Sebastian Neumayer, Lénaïc Chizat, Michael Unser; (15):1−24, 2024.
[abs][pdf][bib]
- Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
- Nathan Kallus, Xiaojie Mao, Masatoshi Uehara; (16):1−59, 2024.
[abs][pdf][bib]
[code]
- On Sufficient Graphical Models
- Bing Li, Kyongwon Kim; (17):1−64, 2024.
[abs][pdf][bib]
- Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
- Ryan Giordano, Martin Ingram, Tamara Broderick; (18):1−39, 2024.
[abs][pdf][bib]
[code]
- Nonparametric Inference under B-bits Quantization
- Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang; (19):1−68, 2024.
[abs][pdf][bib]
- Iterate Averaging in the Quest for Best Test Error
- Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts; (20):1−55, 2024.
[abs][pdf][bib]
[code]
- Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
- Shotaro Yagishita, Jun-ya Gotoh; (21):1−42, 2024.
[abs][pdf][bib]
- On the Generalization of Stochastic Gradient Descent with Momentum
- Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang; (22):1−56, 2024.
[abs][pdf][bib]
- Post-Regularization Confidence Bands for Ordinary Differential Equations
- Xiaowu Dai, Lexin Li; (23):1−51, 2024.
[abs][pdf][bib]
- Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
- Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao; (24):1−67, 2024.
[abs][pdf][bib]
- On Tail Decay Rate Estimation of Loss Function Distributions
- Etrit Haxholli, Marco Lorenzi; (25):1−47, 2024.
[abs][pdf][bib]
[code]
- Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
- Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge; (26):1−36, 2024.
[abs][pdf][bib]
[code]
- Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality
- Stephan Wojtowytsch; (27):1−49, 2024.
[abs][pdf][bib]
- Additive smoothing error in backward variational inference for general state-space models
- Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff; (28):1−33, 2024.
[abs][pdf][bib]
- Rates of convergence for density estimation with generative adversarial networks
- Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov; (29):1−47, 2024.
[abs][pdf][bib]
- Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
- Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi; (30):1−27, 2024.
[abs][pdf][bib]
- Sample-efficient Adversarial Imitation Learning
- Dahuin Jung, Hyungyu Lee, Sungroh Yoon; (31):1−32, 2024.
[abs][pdf][bib]
- Heterogeneous-Agent Reinforcement Learning
- Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang; (32):1−67, 2024.
[abs][pdf][bib]
[code]
- Pygmtools: A Python Graph Matching Toolkit
- Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan; (33):1−7, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Effect-Invariant Mechanisms for Policy Generalization
- Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters; (34):1−36, 2024.
[abs][pdf][bib]
- Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
- Shijun Zhang, Jianfeng Lu, Hongkai Zhao; (35):1−39, 2024.
[abs][pdf][bib]
- Sparse NMF with Archetypal Regularization: Computational and Robustness Properties
- Kayhan Behdin, Rahul Mazumder; (36):1−62, 2024.
[abs][pdf][bib]
[code]
- Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms
- T. Tony Cai, Hongji Wei; (37):1−63, 2024.
[abs][pdf][bib]
- Convergence for nonconvex ADMM, with applications to CT imaging
- Rina Foygel Barber, Emil Y. Sidky; (38):1−46, 2024.
[abs][pdf][bib]
[code]
- On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
- Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel; (39):1−58, 2024.
[abs][pdf][bib]
- Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
- George H. Chen; (40):1−78, 2024.
[abs][pdf][bib]
[code]
- Personalized PCA: Decoupling Shared and Unique Features
- Naichen Shi, Raed Al Kontar; (41):1−82, 2024.
[abs][pdf][bib]
[code]
- Invariant and Equivariant Reynolds Networks
- Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai; (42):1−36, 2024. (Machine Learning Open Source Software Paper)
[abs][pdf][bib]
[code]
- Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
- Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu; (43):1−44, 2024.
[abs][pdf][bib]
- Multiple Descent in the Multiple Random Feature Model
- Xuran Meng, Jianfeng Yao, Yuan Cao; (44):1−49, 2024.
[abs][pdf][bib]
- Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
- Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta; (45):1−64, 2024.
[abs][pdf][bib]
[code]
- A Multilabel Classification Framework for Approximate Nearest Neighbor Search
- Ville Hyvönen, Elias Jääsaari, Teemu Roos; (46):1−51, 2024.
[abs][pdf][bib]
[code]
- Efficient Modality Selection in Multimodal Learning
- Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao; (47):1−39, 2024.
[abs][pdf][bib]
- Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
- Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh; (48):1−53, 2024.
[abs][pdf][bib]
- Trained Transformers Learn Linear Models In-Context
- Ruiqi Zhang, Spencer Frei, Peter L. Bartlett; (49):1−55, 2024.
[abs][pdf][bib]
Mastodon
