publications

2025

1. ICML

   

   BILBO: BILevel Bayesian Optimization

   Ruth Wan Theng Chew, Quoc Phong Nguyen, and Bryan Kian Hsiang Low

   In International Conference on Machine Learning, 2025

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   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 prevalent in real-world problems. The constraint by optimal lower-level solutions poses significant challenges, especially in noisy, constrained, and derivative-free settings, as repeating lower-level optimizations is sample inefficient and predicted lower-level solutions may be suboptimal. We present BILevel Bayesian Optimization (BILBO), a novel Bayesian optimization algorithm for general bilevel problems with blackbox functions, which optimizes both upper- and lower-level problems simultaneously, without the repeated lower-level optimization required by existing methods. BILBO samples from confidence-bounds based trusted sets, which bounds the suboptimality on the lower level. Moreover, BILBO selects only one function query per iteration, where the function query selection strategy incorporates the uncertainty of estimated lower-level solutions and includes a conditional reassignment of the query to encourage exploration of the lower-level objective. The performance of BILBO is theoretically guaranteed with a sublinear regret bound for commonly used kernels and is empirically evaluated on several synthetic and real-world problems.

2. README: Rapid Equation Discovery with Multimodal Encoders

   Gregory Kang Ruey Lau, Yue Ran Kang, Zi-Yu Khoo, and 3 more authors

   In 2nd AI for Math Workshop@ ICML, 2025

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   Discovering scientific laws or interpretable symbolic equations from data rapidly is important in many setting, such as decision-making in time-sensitive high-stake scenarios or applications involving interactive or iterative experimentation such as in scientific or machine learning workflows. However, existing methods, generally known as symbolic regression (SR), typically require long computational time to achieve good performance and have to run from scratch for each dataset. Recent methods that use pre-training SR foundation models for faster inference also suffer from performance limitations and require large training datasets. In this work, we propose README, a framework for rapid equation discovery that can generate performant, interpretable equations from limited, noisy data in just a few seconds, and requires significantly less training data compared to past SR foundation model approaches. We achieve this by being the first to (1) work with image representations of datasets to efficiently capture their key properties, (2) combine the capabilities of open-sourced pre-trained text and image encoders to produce an informative SR embedding space, and (3) develop a novel Grey Wolf Optimizer with Bayesian Optimization (GWOBO) algorithm to rapidly optimize for the best symbolic expression within seconds. We empirically show that README outperforms benchmarks on a wide range of realistic datasets, including real experimental data from various domains and noisy video-extracted dynamics.

2024

1. ICLR

   

   Optimistic Bayesian optimization with unknown constraints

   Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, and 2 more authors

   In The Twelfth International Conference on Learning Representations, 2024

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   Though some research efforts have been dedicated to constrained Bayesian optimization (BO), there remains a notable absence of a principled approach with a theoretical performance guarantee in the decoupled setting. Such a setting involves independent evaluations of the objective function and constraints at different inputs, and is hence a relaxation of the commonly-studied coupled setting where functions must be evaluated together. As a result, the decoupled setting requires an adaptive selection between evaluating either the objective function or a constraint, in addition to selecting an input (in the coupled setting). This paper presents a novel constrained BO algorithm with a provable performance guarantee that can address the above relaxed setting. Specifically, it considers the fundamental trade-off between exploration and exploitation in constrained BO, and, interestingly, affords a noteworthy connection to active learning. The performance of our proposed algorithms is also empirically evaluated using several synthetic and real-world optimization problems.

2021

1. 

   Method and system for object localization, and controlling movement of an autonomous underwater vehicle for object intervention

   Eng Wei Goh, Wan Theng Ruth Chew, and Yaadhav Raaj

   WO Patent WO2021045679A1, 2021

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   An underwater object localisation method performed by an autonomous underwater vehicle (AUV) (100) having a sensing device coupled to its body is disclosed. In a described embodiment, the method includes generating sensor data (312) corresponding to an underwater object (314) detected by the sensing device, generating a distribution of N particles (203) based on the generated sensor data (312) to represent a posterior distribution of the object’s location, with each particle (203) associated with an initial position (326) relative to a reference of the AUV body (100), and transforming the initial positions (326) of the N particles (203) into initial object global coordinates (208) using the AUV’s global coordinates (334) to define the object’s approximate 3D location, the object global coordinates (208) being independent of the AUV body reference. A localisation system and a control system are also disclosed.