:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xie, Zhijie, Song, S. H.
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2204.08125
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
by: Zhu, Lingwei, et al.
Published: (2023)

Adaptive Symmetrization of the KL Divergence
by: Ben-Dov, Omri, et al.
Published: (2025)

FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
by: Németh, Gergely D., et al.
Published: (2025)

Client Selection for Federated Policy Optimization with Environment Heterogeneity
by: Xie, Zhijie, et al.
Published: (2023)

The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning
by: Xie, Zhijie, et al.
Published: (2025)

On Flow Matching KL Divergence
by: Su, Maojiang, et al.
Published: (2025)

On the KL-Divergence-based Robust Satisficing Model
by: Yan, Haojie, et al.
Published: (2024)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
by: Yu, Po-Hsien, et al.
Published: (2025)

Training Neural Samplers with Reverse Diffusive KL Divergence
by: He, Jiajun, et al.
Published: (2024)

Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
by: Zeng, Shuang, et al.
Published: (2024)

Bidirectional Soft Actor-Critic: Leveraging Forward and Reverse KL Divergence for Efficient Reinforcement Learning
by: Zhang, Yixian, et al.
Published: (2025)

FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
by: Zhou, Zhekai, et al.
Published: (2025)

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
by: Wang, Yuan, et al.
Published: (2024)

Optimal Stability of KL Divergence under Gaussian Perturbations
by: Pan, Jialu, et al.
Published: (2026)

Logarithmic Regret for Online KL-Regularized Reinforcement Learning
by: Zhao, Heyang, et al.
Published: (2025)

KL-Regularized Reinforcement Learning is Designed to Mode Collapse
by: GX-Chen, Anthony, et al.
Published: (2025)

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning
by: Yuan, Rui, et al.
Published: (2026)

Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation
by: Ma, Xing
Published: (2025)

Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence
by: Datar, Adwait, et al.
Published: (2025)

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
by: Aminian, Gholamali, et al.
Published: (2025)

FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
by: Wang, Kaile, et al.
Published: (2026)

FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
by: Chan, Yun-Hin, et al.
Published: (2023)

KL for a KL: On-Policy Distillation with Control Variate Baseline
by: Oh, Minjae, et al.
Published: (2026)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)

Tackling Data Heterogeneity in Federated Time Series Forecasting
by: Yuan, Wei, et al.
Published: (2024)

How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metrics
by: Smelser, Kiran, et al.
Published: (2025)

The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
by: Schaeffer, Bernardo P., et al.
Published: (2025)

Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
by: Zhang, Shiyuan, et al.
Published: (2026)

Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models
by: Pal, Samyajoy, et al.
Published: (2024)

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)

FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
by: Yang, Zhiqin, et al.
Published: (2025)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)

KL Divergence Between Gaussians: A Step-by-Step Derivation for the Variational Autoencoder Objective
by: Muñoz, Andrés, et al.
Published: (2026)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)

Shifted Composition III: Local Error Framework for KL Divergence
by: Altschuler, Jason M., et al.
Published: (2024)

FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning
by: Sen, Mrinmay, et al.
Published: (2025)

Well-Posed KL-Regularized Control via Wasserstein and Kalman-Wasserstein KL Divergences
by: Stein, Viktor, et al.
Published: (2026)

Recursive KL Divergence Optimization: A Dynamic Framework for Representation Learning
by: Martin, Anthony D
Published: (2025)