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Mojmír Mutný

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JMLR Journal 2024 Journal Article

Data Summarization via Bilevel Optimization

Zalán Borsos
Mojmír Mutný
Marco Tagliasacchi
Andreas Krause

The increasing availability of massive data sets poses various challenges for machine learning. Prominent among these is learning models under hardware or human resource constraints. In such resource-constrained settings, a simple yet powerful approach is operating on small subsets of the data. Coresets are weighted subsets of the data that provide approximation guarantees for the optimization objective. However, existing coreset constructions are highly model-specific and are limited to simple models such as linear regression, logistic regression, and k-means. In this work, we propose a generic coreset construction framework that formulates the coreset selection as a cardinality-constrained bilevel optimization problem. In contrast to existing approaches, our framework does not require model-specific adaptations and applies to any twice differentiable model, including neural networks. We show the effectiveness of our framework for a wide range of models in various settings, including training non-convex models online and batch active learning. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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NeurIPS Conference 2024 Conference Paper

Transition Constrained Bayesian Optimization via Markov Decision Processes

Jose P. Folch
Calvin Tsay
Robert M. Lee
Behrang Shafei
Weronika Ormaniec
Andreas Krause
Mark van der Wilk
Ruth Misener

Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexibility; in particular, the search space of the next query may depend on previous ones. Example challenges arise in the physical sciences in the form of local movement constraints, required monotonicity in certain variables, and transitions influencing the accuracy of measurements. Altogether, such transition constraints necessitate a form of planning. This work extends classical Bayesian optimization via the framework of Markov Decision Processes. We iteratively solve a tractable linearization of our utility function using reinforcement learning to obtain a policy that plans ahead for the entire horizon. This is a parallel to the optimization of an acquisition function in policy space. The resulting policy is potentially history-dependent and non-Markovian. We showcase applications in chemical reactor optimization, informative path planning, machine calibration, and other synthetic examples.

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