Papers
4,122 papers found
Learning to Rank under Multinomial Logit Choice
James A. Grant, David S. Leslie
Least Squares Model Averaging for Distributed Data
Haili Zhang, Zhaobo Liu, Guohua Zou
LibMTL: A Python Library for Deep Multi-Task Learning
Baijiong Lin, Yu Zhang
Lifted Bregman Training of Neural Networks
Xiaoyu Wang, Martin Benning
Limitations on approximation by deep and shallow neural networks
Guergana Petrova, Przemyslaw Wojtaszczyk
Limits of Dense Simplicial Complexes
T. Mitchell Roddenberry, Santiago Segarra
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications
Johannes Kirschner, Tor Lattimore, Andreas Krause
Lower Bounds and Accelerated Algorithms for Bilevel Optimization
Kaiyi ji, Yingbin Liang
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence
Yuetian Luo, Anru R. Zhang
Low Tree-Rank Bayesian Vector Autoregression Models
Leo L Duan, Zeyu Yuwen, George Michailidis et al.
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang et al.
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao et al.
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation
Qian Li, Binyan Jiang, Defeng Sun
MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun et al.
Maximum likelihood estimation in Gaussian process regression is ill-posed
Toni Karvonen, Chris J. Oates
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
Bokun Wang, Zhuoning Yuan, Yiming Ying et al.
Merlion: End-to-End Machine Learning for Time Series
Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu et al.
Metrizing Weak Convergence with Maximum Mean Discrepancies
Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf et al.
Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, G. Bruno De Luca, Eva Silverstein et al.
Mini-batching error and adaptive Langevin dynamics
Inass Sekkat, Gabriel Stoltz
Minimal Width for Universal Property of Deep RNN
Chang hoon Song, Geonho Hwang, Jun ho Lee et al.
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
Shuxiao Chen, Qinqing Zheng, Qi Long et al.
Minimax Risk Classifiers with 0-1 Loss
Santiago Mazuelas, Mauricio Romero, Peter Grunwald
Mixed Regression via Approximate Message Passing
Nelvin Tan, Ramji Venkataramanan
MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert et al.