3 citations · 5 across the 6 of their papers we have counts for
6 papers
Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach
Fnu Hairi, Jiao Yang, Tianchen Zhou +6
In many multi-objective reinforcement learning (MORL) applications, being able to systematically explore the Pareto-stationary solutions under multiple non-convex reward objectives…
STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning
Zhuqing Liu, Chaosheng Dong, Michinari Momma +5
Recently, multi-objective optimization (MOO) has gained attention for its broad applications in ML, operations research, and engineering. However, MOO algorithm design remains in i…
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
Mingdai Yang, Fan Yang, Yanhui Guo +6
User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus o…
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning
Yanhui Guo, Shaoyuan Xu, Jinmiao Fu +3
This paper introduces \textbf{Q-tuning}, a novel approach for continual prompt tuning that enables the lifelong learning of a pre-trained language model. When learning a new task,…
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments
Tianchen Zhou, Jia Liu, Yang Jiao +4
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years. Among the existing approaches for ONL2R, a…
Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu +2
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms i…