18 citations · 20 across the 11 of their papers we have counts for
7 papers · 1 filter
AutoMAS: A Generic Multi-Agent System for Algorithm Self-Adaptation in Wireless Networks
Dingli Yuan, Jingchen Peng, Jie Fan +3
The wireless communication environment has the characteristic of strong dynamics. Conventional wireless networks operate based on the static rules with predefined algorithms, lacki…
Robust distortion risk metrics with Wasserstein and Unimodal Constraints
Peng Liu, Steven Vanduffel, Yi Xia +1
We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlyin…
Lambda Value-at-Risk under ambiguity and risk sharing
Peng Liu, Alexander Schied
In this paper, we investigate the Lambda Value-at-Risk (VaR) under ambiguity, where the ambiguity is represented by a family of probability measures. We establish that for incre…
BPO: Revisiting Preference Modeling in Direct Preference Optimization
Lin Sun, Chuang Liu, Peng Liu +3
Direct Preference Optimization (DPO) have emerged as a popular method for aligning Large Language Models (LLMs) with human preferences. While DPO effectively preserves the relative…
FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness
Yuanjian Xu, Jianing Hao, Kunsheng Tang +4
Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. Previous event studies, constrained by static single-company analyses and…
An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems
Peng Liu, Liang Chen, Minru Bai
As an extension of the alternating direction method of multipliers (ADMM), the semi-proximal ADMM (sPADMM) has been widely used in various fields due to its flexibility and robustn…
Explainable Neural Networks with Guarantees: A Sparse Estimation Approach
Antoine Ledent, Peng Liu
Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obst…