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20242026
most citedCausality-Aware Transformer Networks for Robotic Navigation

1 citations · 3 across the 15 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery

Xiaoxuan Li, Yao Liu, Ruoyu Wang +1

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from obser…

cs.LG2024

OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models

Junda Wu, Xintong Li, Ruoyu Wang +9

Offline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline eva…

cs.CL2024

Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs

Ruoyu Wang, Xiaoxuan Li, Lina Yao

Large Language Models (LLMs) have demonstrated remarkable efficiency in tackling various tasks based on human instructions, but studies reveal that they often struggle with tasks r…

cs.LG2024

Independence Constrained Disentangled Representation Learning from Epistemological Perspective

Ruoyu Wang, Lina Yao

Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables…

cs.RO2024★ 1 cited

Causality-Aware Transformer Networks for Robotic Navigation

Ruoyu Wang, Yao Liu, Yuanjiang Cao +1

Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Em…

cs.LG2024

ASFT: Aligned Supervised Fine-Tuning through Absolute Likelihood

Ruoyu Wang, Jiachen Sun, Shaowei Hua +1

Direct Preference Optimization (DPO) is a method for enhancing model performance by directly optimizing for the preferences or rankings of outcomes, instead of traditional loss fun…

cs.RO2024★ 1 cited

Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Yao Liu, Ruoyu Wang, Yuanjiang Cao +2

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering…