:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Dong, Chaosheng, Wang, Yijia
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2402.07246
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

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

Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)

Inverse Reinforcement Learning without Reinforcement Learning
by: Swamy, Gokul, et al.
Published: (2023)

Distributional Inverse Reinforcement Learning
by: Wu, Feiyang, et al.
Published: (2025)

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

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

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

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

Inverse Delayed Reinforcement Learning
by: Zhan, Simon Sinong, et al.
Published: (2024)

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

Hybrid Inverse Reinforcement Learning
by: Ren, Juntao, et al.
Published: (2024)

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

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

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

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

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

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

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

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

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

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

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

Recursive Deep Inverse Reinforcement Learning
by: Ghanem, Paul, et al.
Published: (2025)

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

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

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

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

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

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

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

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