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Yao-Xiang Ding

8 accepted papers

2026

Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation

ICML 2026poster

In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs \& VLAs) are powerful tools with different benefits: VLMs are better at long-term planning, while VLAs are better at reactive control. However, their…

Cited by 0SourceScholar
2026

Reinforcement Learning from Bagged Reward

ICML 2026poster

In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative rewards to obtain the optimal policy. However, in many real-world scenarios, designing immediate reward signals is difficult; …

Cited by 0SourceScholar
2025

Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment

AAAI 2025technical

The task of one-shot face video re-enactment aims at generating target video of faces with the same identity of one source frame and facial deformation of the driving video. To achieve high quality generation, it is essential to precisely disentangle identity-related and identity-independent charac…

Cited by 1SourcePDFScholar
2024

Learning Only When It Matters: Cost-Aware Long-Tailed Classification

AAAI 2024technical

Most current long-tailed classification approaches assume the cost-agnostic scenario, where the training distribution of classes is long-tailed while the testing distribution of classes is balanced. Meanwhile, the misclassification costs of all instances are the same. On the other hand, in many real…

Cited by 1SourcePDFScholar
2023

Model Spider: Learning to Rank Pre-Trained Models Efficiently

NeurIPS 2023spotlight

Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is essential to take advantage of plentiful model resources. With the availability of numerous heterogeneous PTMs from diverse fields, efficiently selecting the most suitable one is challenging due to the time-consuming…

2023

Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning

ICLR 2023top-25%

In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existing imitation learning approaches, since most of them learn policies under homogeneous observation spaces. On the other ha…

Cited by 10SourcePDFScholar
2022

Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning

NeurIPS 2022accept

We study {\it model reusability evaluation} (MRE) for source pre-trained models: evaluating their transfer learning performance to new target tasks. In special, we focus on the setting under which the target training datasets are small, making it difficult to produce reliable MRE scores using them.…

Cited by 12SourcePDFScholar