11 papers
On the Learnability of Offline Model-Based Optimization: A Ranking Perspective
Shen-Huan Lyu, Rong-Xi Tan, Ke Xue +4
Offline model-based optimization (MBO) seeks to discover high-performing designs using only a fixed dataset of past evaluations. Most existing methods rely on learning a surrogate…
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Theresa Eimer, Lennart Schäpermeier, André Biedenkapp +15
Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With…
Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
Ming Chen, Sheng Tang, Rong-Xi Tan +4
Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical predicti…
Sequential Multi-Agent Dynamic Algorithm Configuration
Chen Lu, Ke Xue, Lei Yuan +4
Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning, which can dynamically adjust the algorithm's configuration during the execution process and r…
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
Ren-Jian Wang, Ke Xue, Zeyu Qin +7
Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artificial intelligence.…
Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
Rong-Xi Tan, Ming Chen, Ke Xue +4
The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has drive…