2 citations · 2 across the 5 of their papers we have counts for
6 papers
When More Objectives Make Identification Easier: The Role of Preferences and Feedback
Bo Xue, Ji Cheng, Yuanyu Wan +1
Best-arm identification (BAI) aims to identify the optimal arm using as few samples as possible. While most existing work focuses on a single objective, many real-world application…
Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation
Bo Xue, Zhi Hong, Jiayi Li +3
Large language model (LLM) configuration evaluation is challenging due to limited evaluation budgets, varying costs, and multiple competing objectives. In this paper, we formulate…
Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits
Bo Xue, Ji Cheng, Haodong Jing +2
This paper studies generalized low-rank matrix bandits with multiple prioritized objectives. At each round, the learner selects a matrix-valued arm and observes a vector-valued rew…
Parametric Pareto Set Learning for Expensive Multi-Objective Optimization
Ji Cheng, Bo Xue, Qingfu Zhang
Parametric multi-objective optimization (PMO) addresses the challenge of solving an infinite family of multi-objective optimization problems, where optimal solutions must adapt to…
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki +4
Neural Network-based active learning (NAL) is a cost-effective data selection technique that utilizes neural networks to select and train on a small subset of samples. While existi…
Evolve Cost-aware Acquisition Functions Using Large Language Models
Yiming Yao, Fei Liu, Ji Cheng +1
Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in add…