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Max Liu

2 accepted papers

2025

Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search

ICLR 2025poster

Programmatic reinforcement learning (PRL) has been explored for representing policies through programs as a means to achieve interpretability and generalization. Despite promising outcomes, current state-of-the-art PRL methods are hindered by sample inefficiency, necessitating tens of millions of pr…

Cited by 6SourcePDFScholar
2024

AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models

ICASSP 2024accepted

Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of learned representations are unclear. To this end, w…

Cited by 0SourceScholar