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Peng Yan

7 accepted papers

2026

Multi-Aspect Cross-modal Quantization for Generative Recommendation

AAAI 2026technical

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users’ historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item b

Cited by 0SourcePDFScholar
2025

An Inflatable Deployable Origami Grasper for Adaptive and High-Load Grasping

IROS 2025

Robotic graspers are essential for enhancing the efficiency and versatility of robots in grasping tasks. In this paper, we propose a novel inflatable deployable origami grasper with a rigid-flexible coupling structure. The proposed grasper can achieve multiple deployment configurations under a singl

Cited by 0SourceScholar
2025

From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs

ACL 2025finding

Recent progress in large vision-language models (LVLMs) has shown substantial potential across a broad spectrum of third-person tasks. However, adapting these LVLMs to egocentric scenarios remains challenging due to their third-person training bias. Existing methods that adapt LVLMs for first-person…

2025

Self-Evolutionary Large Language Models Through Uncertainty-Enhanced Preference Optimization

AAAI 2025technical

Iterative preference optimization has recently become one of the de-facto training paradigms for large language models (LLMs), but the performance is still underwhelming due to too much noisy preference data yielded in the loop. To combat this issue, we present an Uncertainty-enhanced Preference Opt…

2024

Calibrating the Confidence of Large Language Models by Eliciting Fidelity

EMNLP 2024main

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper,…

Cited by 4SourcePDFScholar
2023

Dual Personalization on Federated Recommendation

IJCAI 2023poster

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of…