4 papers
Bayesian Smoothed Quantile Regression
Bingqi Liu, Kangqiang Li, Tianxiao Pang
The standard asymmetric Laplace framework for Bayesian quantile regression (BQR) suffers from a fundamental decision-theoretic misalignment, yielding biased finite-sample estimates…
A Log-domain Interior Point Method for Convex Quadratic Games
Bingqi Liu, Dominic Liao-McPherson
In this paper, we propose an equilibrium-seeking algorithm for finding generalized Nash equilibria of non-cooperative monotone convex quadratic games. Specifically, we recast the N…
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Haoyi Niu, Tianying Ji, Bingqi Liu +7
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online R…
Robust low-rank tensor regression via clipping and Huber loss
Kangqiang Li, Bingqi Liu, Yang Yang +1
In this paper, we construct a parameter estimation framework for robust low-rank tensor regression based on a truncation method and Huber loss, specifically focusing on models with…