:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yu, Zhongwei, Ruan, Jingqing, Xing, Dengpeng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.12615
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Explainable Reinforcement Learning via a Causal World Model
by: Yu, Zhongwei, et al.
Published: (2023)

Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
by: Ruan, Jingqing, et al.
Published: (2023)

Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy
by: Zhang, Xiaoyun, et al.
Published: (2025)

PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling
by: Jian, Ai, et al.
Published: (2025)

ObjectRL: An Object-Oriented Reinforcement Learning Codebase
by: Baykal, Gulcin, et al.
Published: (2025)

Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
by: Zhang, Xiaoyun, et al.
Published: (2025)

Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments
by: Dcruz, Julian Gerald, et al.
Published: (2024)

Object-Oriented Transition Modeling with Inductive Logic Programming
by: Stella, Gabriel, et al.
Published: (2026)

ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
by: Li, Haoran, et al.
Published: (2025)

SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting
by: Zheng, Binbin, et al.
Published: (2026)

Object-Centric World Models for Causality-Aware Reinforcement Learning
by: Nishimoto, Yosuke, et al.
Published: (2025)

Coarse-to-Fine Learning of Dynamic Causal Structures
by: Yang, Dezhi, et al.
Published: (2026)

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
by: Gao, Erdun, et al.
Published: (2025)

Interpretable Imitation Learning with Dynamic Causal Relations
by: Zhao, Tianxiang, et al.
Published: (2023)

AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models
by: Liu, Qi, et al.
Published: (2025)

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning
by: Zhang, Zheyu, et al.
Published: (2025)

Dynamic Objects Relocalization in Changing Environments with Flow Matching
by: Argenziano, Francesco, et al.
Published: (2025)

ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals
by: Xu, Yuanchao, et al.
Published: (2025)

Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
by: Orujlu, Turan, et al.
Published: (2025)

TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments
by: Yun, Sheng, et al.
Published: (2025)

Causal Representation Learning from General Environments under Nonparametric Mixing
by: Ng, Ignavier, et al.
Published: (2026)

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
by: Bach, Philipp, et al.
Published: (2021)

Hummer: Towards Limited Competitive Preference Dataset
by: Jiang, Li, et al.
Published: (2024)

SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression
by: Wang, Xin, et al.
Published: (2024)

Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
by: Liao, Yufan, et al.
Published: (2022)

Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
by: Han, Chao, et al.
Published: (2024)

CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks
by: Xing, Ke, et al.
Published: (2025)

Marrying Causal Representation Learning with Dynamical Systems for Science
by: Yao, Dingling, et al.
Published: (2024)

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
by: Luo, Kang, et al.
Published: (2024)

Dynamic Structural Causal Models
by: Boeken, Philip, et al.
Published: (2024)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)

Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment
by: Corazza, Jan, et al.
Published: (2025)

Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
by: Mangannavar, Rajesh, et al.
Published: (2024)

Environment Inference for Learning Generalizable Dynamical System
by: Liu, Shixuan, et al.
Published: (2025)

SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models
by: Klu, Emmanuel, et al.
Published: (2023)

OOWM: Structuring Embodied Reasoning and Planning via Object-Oriented Programmatic World Modeling
by: Chen, Hongyu, et al.
Published: (2026)

Odin: Oriented Dual-module Integration for Text-rich Network Representation Learning
by: Hong, Kaifeng, et al.
Published: (2025)

VQPy: An Object-Oriented Approach to Modern Video Analytics
by: Yu, Shan, et al.
Published: (2023)

Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
by: Liu, Yuxuan, et al.
Published: (2026)

Dynamic Causal Structure Discovery and Causal Effect Estimation
by: Wang, Jianian, et al.
Published: (2025)