18 citations · 20 across the 11 of their papers we have counts for
11 papers
Beyond Procedure: Substantive Fairness in Conformal Prediction
Pengqi Liu, Zijun Yu, Mouloud Belbahri +3
Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains und…
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…
Risk sharing with Lambda value at risk under heterogeneous beliefs
Peng Liu, Andreas Tsanakas, Yunran Wei
In this paper, we study the risk sharing problem among multiple agents using Lambda Value-at-Risk as their preference functional, under heterogeneous beliefs, where beliefs are rep…
Factor risk measures
Hirbod Assa, Peng Liu
This paper introduces and studies factor risk measures. While risk measures only rely on the distribution of a loss random variable, in many cases risk needs to be measured relativ…
Risk Premia and Optimal Liquidation of Credit Derivatives
Tim Leung, Peng Liu
This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential pr…