77 citations · 584 across the 84 of their papers we have counts for
7 papers · 2 filters
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents
Arun Verma, Indrajit Saha, Makoto Yokoo +1
This paper considers a contextual bandit problem involving multiple agents, where a learner sequentially observes the contexts and the agent's reported arms, and then selects the a…
ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
Xiaoqiang Lin, Arun Verma, Zhongxiang Dai +3
The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various downstream tasks, such as question answe…
Active Human Feedback Collection via Neural Contextual Dueling Bandits
Arun Verma, Xiaoqiang Lin, Zhongxiang Dai +2
Collecting human preference feedback is often expensive, leading recent works to develop principled algorithms to select them more efficiently. However, these works assume that the…
PIED: Physics-Informed Experimental Design for Inverse Problems
Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng +1
In many science and engineering settings, system dynamics are characterized by governing PDEs, and a major challenge is to solve inverse problems (IPs) where unknown PDE parameters…
DUPRE: Data Utility Prediction for Efficient Data Valuation
Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Quoc Phong Nguyen +2
Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperat…
BILBO: BILevel Bayesian Optimization
Ruth Wan Theng Chew, Quoc Phong Nguyen, Bryan Kian Hsiang Low
Bilevel optimization is characterized by a two-level optimization structure, where the upper-level problem is constrained by optimal lower-level solutions, and such structures are…
DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
Zhiliang Chen, Gregory Kang Ruey Lau, Chuan-Sheng Foo +1
The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the data involved in an unseen evaluation t…