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Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier

Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration—a key determinant of …

BO4Mob - Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem

We introduce BO4Mob, a new benchmark framework for high-dimensional Bayesian Optimization (BO), driven by the challenge of origin-destination (OD) travel demand estimation in large urban road networks. Estimating OD travel demand from limited traffic …

Online Optimization for Offline Safe Reinforcement Learning

We study the problem of Offline Safe Reinforcement Learning (OSRL), where the goal is to learn a reward-maximizing policy from fixed data under a cumulative cost constraint. We propose a novel OSRL approach that frames the problem as a mini-max …

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning

Offline safe reinforcement learning (OSRL) involves learning a decision-making policy to maximize rewards from a fixed batch of training data to satisfy pre-defined safety constraints. However, adapting to varying safety constraints during deployment …

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces

Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box functions. However, in settings with very few function evaluations, a successful application of BO may require transferring information from historical …

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching

Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessitates the use of in silico surrogate functions to predict and maximize …

Streamflow Prediction with Uncertainty Quantification for Water Management

Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on …

Offline Model-based Black-Box Optimization via Policy-guided Gradient Search

Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize …

Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings

We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We use Bayesian Optimization (BO) and propose a novel surrogate modeling …

Gauche - A Library for Gaussian Processes in Chemistry

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending …