:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Dong, Chaosheng, Wang, Yijia
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2402.07246
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

DeepMTL2R: A Library for Deep Multi-task Learning to Rank
por: Dong, Chaosheng, et al.
Publicado: (2026)

LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
por: Xiao, Peiyao, et al.
Publicado: (2025)

$P^2$GNN: Two Prototype Sets to boost GNN Performance
por: Jain, Arihant, et al.
Publicado: (2026)

Toward Information Theoretic Active Inverse Reinforcement Learning
por: Bajgar, Ondrej, et al.
Publicado: (2024)

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach
por: Hairi, Fnu, et al.
Publicado: (2025)

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
por: Xie, Sean, et al.
Publicado: (2022)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
por: Dong, Zixuan, et al.
Publicado: (2025)

Faster Reinforcement Learning by Freezing Slow States
por: Wang, Yijia, et al.
Publicado: (2023)

Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
por: Hassani, Hossein, et al.
Publicado: (2024)

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
por: Li, Zhe, et al.
Publicado: (2024)

Federated Multi-Objective Learning
por: Yang, Haibo, et al.
Publicado: (2023)

Inverse Reinforcement Learning without Reinforcement Learning
por: Swamy, Gokul, et al.
Publicado: (2023)

Distributional Inverse Reinforcement Learning
por: Wu, Feiyang, et al.
Publicado: (2025)

Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
por: Mehrdad, Sarmad, et al.
Publicado: (2026)

Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
por: Kim, Woo Kyung, et al.
Publicado: (2024)

Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
por: Schlaginhaufen, Andreas, et al.
Publicado: (2024)

Implicit Generative Prior for Bayesian Neural Networks
por: Liu, Yijia, et al.
Publicado: (2024)

Inverse Delayed Reinforcement Learning
por: Zhan, Simon Sinong, et al.
Publicado: (2024)

The Virtues of Pessimism in Inverse Reinforcement Learning
por: Wu, David, et al.
Publicado: (2024)

Hybrid Inverse Reinforcement Learning
por: Ren, Juntao, et al.
Publicado: (2024)

Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
por: Li, Zhe, et al.
Publicado: (2025)

Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
por: Skalse, Joar, et al.
Publicado: (2024)

Inverse Reinforcement Learning with Multiple Planning Horizons
por: Yao, Jiayu, et al.
Publicado: (2024)

Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
por: Wu, David, et al.
Publicado: (2024)

Walking the Values in Bayesian Inverse Reinforcement Learning
por: Bajgar, Ondrej, et al.
Publicado: (2024)

Confidence Aware Inverse Constrained Reinforcement Learning
por: Subramanian, Sriram Ganapathi, et al.
Publicado: (2024)

Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
por: Fan, Jialiang, et al.
Publicado: (2026)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
por: Zhao, Lei, et al.
Publicado: (2023)

Fast Rates for Inverse Reinforcement Learning
por: Schlaginhaufen, Andreas, et al.
Publicado: (2026)

On the Effective Horizon of Inverse Reinforcement Learning
por: Xu, Yiqing, et al.
Publicado: (2023)

Environment Design for Inverse Reinforcement Learning
por: Buening, Thomas Kleine, et al.
Publicado: (2022)

Recursive Deep Inverse Reinforcement Learning
por: Ghanem, Paul, et al.
Publicado: (2025)

Inverse Reinforcement Learning With Constraint Recovery
por: Das, Nirjhar, et al.
Publicado: (2023)

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning
por: Liu, Zhuqing, et al.
Publicado: (2025)

PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
por: Bajgar, Ondrej, et al.
Publicado: (2025)

Pushing the Limits of Inverse Lithography with Generative Reinforcement Learning
por: Yang, Haoyu, et al.
Publicado: (2026)

On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
por: Freihaut, Till, et al.
Publicado: (2024)

Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
por: Topper, Noah, et al.
Publicado: (2024)

A Bayesian Approach to Robust Inverse Reinforcement Learning
por: Wei, Ran, et al.
Publicado: (2023)

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
por: Audiffren, Julien, et al.
Publicado: (2026)