ICML 2024 "sample complexity analysis" Papers
12 papers found
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
Xuran Meng, Difan Zou, Yuan Cao
ICML 2024poster
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi et al.
ICML 2024poster
Federated Representation Learning in the Under-Parameterized Regime
Renpu Liu, Cong Shen, Jing Yang
ICML 2024poster
Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
Fuzhong Zhou, Chenyu Zhang, Xu Chen et al.
ICML 2024poster
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
ICML 2024poster
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Umberto Tomasini, Matthieu Wyart
ICML 2024spotlight
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
Yuyang Zhang, Shahriar Talebi, Na Li
ICML 2024poster
On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
ICML 2024poster
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta et al.
ICML 2024poster
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, XiangCheng Zhang, Siwei Wang et al.
ICML 2024poster
Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
Xinyi Ni, Guanlin Liu, Lifeng Lai
ICML 2024poster
Single-Trajectory Distributionally Robust Reinforcement Learning
Zhipeng Liang, Xiaoteng Ma, Jose Blanchet et al.
ICML 2024poster