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Ruth Misener

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14 papers

2 author rows

ICML Conference 2025 Conference Paper

BARK: A Fully Bayesian Tree Kernel for Black-box Optimization

Toby Boyne
Jose Pablo Folch
Robert M. Lee
Behrang Shafei
Ruth Misener

We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior over piecewise-constant functions, and we explore the space of tree kernels using a Markov chain Monte Carlo approach. Where BART only samples functions, the resulting BARK model obtains samples of Gaussian processes defining distributions over functions, which allow us to build acquisition functions for Bayesian optimization. Our tree-based approach enables global optimization over the surrogate, even for mixed-feature spaces. Moreover, where many previous tree-based kernels provide uncertainty quantification over function values, our sampling scheme captures uncertainty over the tree structure itself. Our experiments show the strong performance of BARK on both synthetic and applied benchmarks, due to the combination of our fully Bayesian surrogate and the optimization procedure.

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JMLR Journal 2025 Journal Article

BoFire: Bayesian Optimization Framework Intended for Real Experiments

Johannes P. Dürholt
Thomas S. Asche
Johanna Kleinekorte
Gabriel Mancino-Ball
Benjamin Schiller
Simon Sung
Julian Keupp
Aaron Osburg

Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature or software, require substantial adaptation for effective real-world deployment in chemical industry. BoFire provides a rich feature-set with extensive configurability and realizes our vision of fast-tracking research contributions into industrial use via maintainable open-source software. Owing to quality-of-life features like JSON-serializability of problem formulations, BoFire enables seamless integration of BO into RESTful APIs, a common architecture component for both self-driving laboratories and human-in-the-loop setups. This paper discusses the differences between BoFire and other BO implementations and outlines ways that BO research needs to be adapted for real-world use in a chemistry setting. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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TMLR Journal 2025 Journal Article

System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization

Jixiang Qing
Rebecca D. Langdon
Robert Matthew Lee
Behrang Shafei
Mark van der Wilk
Calvin Tsay
Ruth Misener

We consider the problem of optimizing initial conditions and termination time in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating different initial conditions is costly and the state's value can not be measured in real-time but only with a delay while the measuring device processes the sample. To identify the optimal conditions in limited trials, we introduce a few-shot Bayesian Optimization (BO) framework based on the system's prior information. At the core of our approach is the System-Aware Neural ODE Processes (SANODEP), an extension of Neural ODE Processes (NODEP) designed to meta-learn ODE systems from multiple trajectories using a novel context embedding block. We further develop a two-stage BO framework to effectively incorporate search space constraints, enabling efficient optimization of both initial conditions and observation timings. We conduct extensive experiments showcasing SANODEP's potential for few-shot BO within dynamical systems. We also explore SANODEP's adaptability to varying levels of prior information, highlighting the trade-off between prior flexibility and model fitting accuracy.

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NeurIPS Conference 2025 Conference Paper

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning

Toby Boyne
Juan Campos
Rebecca Langdon
Jixiang Qing
Yilin Xie
Shiqiang Zhang
Calvin Tsay
Ruth Misener

Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to the machine learning community as they tend to require cleaning, thorough understanding of the chemistry, or are simply not available. In this paper, we introduce a novel dataset for yield prediction, providing the first-ever transient flow dataset for machine learning benchmarking, covering over 1200 process conditions. While previous datasets focus on discrete parameters, our experimental set-up allow us to sample a large number of continuous process conditions, generating new challenges for machine learning models. We focus on solvent selection, a task that is particularly difficult to model theoretically and therefore ripe for machine learning applications. We showcase benchmarking for regression algorithms, transfer-learning approaches, feature engineering, and active learning, with important applications towards solvent replacement and sustainable manufacturing.

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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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ICML Conference 2024 Conference Paper

Verifying message-passing neural networks via topology-based bounds tightening

Christopher Hojny
Shiqiang Zhang
Juan S. Campos
Ruth Misener

Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural networks (MPNNs) using a Rectified Linear Unit (ReLU) activation function. Because our work builds on mixed-integer optimization, it encodes a wide variety of subproblems, for example it admits (i) both adding and removing edges, (ii) both global and local budgets, and (iii) both topological perturbations and feature modifications. Our key technology, topology-based bounds tightening, uses graph structure to tighten bounds. We also experiment with aggressive bounds tightening to dynamically change the optimization constraints by tightening variable bounds. To demonstrate the effectiveness of these strategies, we implement an extension to the open-source branch-and-cut solver SCIP. We test on both node and graph classification problems and consider topological attacks that both add and remove edges.

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NeurIPS Conference 2023 Conference Paper

Optimizing over trained GNNs via symmetry breaking

Shiqiang Zhang
Juan Campos
Christian Feldmann
David Walz
Frederik Sandfort
Miriam Mathea
Calvin Tsay
Ruth Misener

Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larger decision-making problem. This paper formulates and solves optimization problems constrained by trained graph neural networks (GNNs). To circumvent the symmetry issue caused by graph isomorphism, we propose two types of symmetry-breaking constraints: one indexing a node 0 and one indexing the remaining nodes by lexicographically ordering their neighbor sets. To guarantee that adding these constraints will not remove all symmetric solutions, we construct a graph indexing algorithm and prove that the resulting graph indexing satisfies the proposed symmetry-breaking constraints. For the classical GNN architectures considered in this paper, optimizing over a GNN with a fixed graph is equivalent to optimizing over a dense neural network. Thus, we study the case where the input graph is not fixed, implying that each edge is a decision variable, and develop two mixed-integer optimization formulations. To test our symmetry-breaking strategies and optimization formulations, we consider an application in molecular design.

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JMLR Journal 2022 Journal Article

OMLT: Optimization & Machine Learning Toolkit

Francesco Ceccon
Jordan Jalving
Joshua Haddad
Alexander Thebelt
Calvin Tsay
Carl D Laird
Ruth Misener

The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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NeurIPS Conference 2022 Conference Paper

SnAKe: Bayesian Optimization with Pathwise Exploration

Jose Pablo Folch
Shiqiang Zhang
Robert Lee
Behrang Shafei
David Walz
Calvin Tsay
Mark van der Wilk
Ruth Misener

"Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to decide on new queries before we finish evaluating previous experiments. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering large batches of queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both the synchronous and asynchronous settings, while reducing the input costs significantly. We show the method is robust to the choice of its single hyper-parameter and provide a parameter-free alternative. "

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NeurIPS Conference 2022 Conference Paper

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Alexander Thebelt
Calvin Tsay
Robert Lee
Nathan Sudermann-Merx
David Walz
Behrang Shafei
Ruth Misener

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e. g. , hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.

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NeurIPS Conference 2021 Conference Paper

Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks

Calvin Tsay
Jan Kronqvist
Alexander Thebelt
Ruth Misener

This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. The approach balances model size and tightness by partitioning node inputs into a number of groups and forming the convex hull over the partitions via disjunctive programming. At one extreme, one partition per input recovers the convex hull of a node, i. e. , the tightest possible formulation for each node. For fewer partitions, we develop smaller relaxations that approximate the convex hull, and show that they outperform existing formulations. Specifically, we propose strategies for partitioning variables based on theoretical motivations and validate these strategies using extensive computational experiments. Furthermore, the proposed scheme complements known algorithmic approaches, e. g. , optimization-based bound tightening captures dependencies within a partition.

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AAAI Conference 2020 Conference Paper

Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

Elena Botoeva
Panagiotis Kouvaros
Jan Kronqvist
Alessio Lomuscio
Ruth Misener

We introduce an efﬁcient method for the veriﬁcation of ReLU-based feed-forward neural networks. We derive an automated procedure that exploits dependency relations between the ReLU nodes, thereby pruning the search tree that needs to be considered by MILP-based formulations of the veriﬁcation problem. We augment the resulting algorithm with methods for input domain splitting and symbolic interval propagation. We present Venus, the resulting veriﬁcation toolkit, and evaluate it on the ACAS collision avoidance networks and models trained on the MNIST and CIFAR-10 datasets. The experimental results obtained indicate considerable gains over the present state-of-the-art tools.

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AAAI Conference 2019 Conference Paper

Argumentation for Explainable Scheduling

Kristijonas Čyras
Dimitrios Letsios
Ruth Misener
Francesca Toni

Mathematical optimization offers highly-effective tools for finding solutions for problems with well-defined goals, notably scheduling. However, optimization solvers are often unexplainable black boxes whose solutions are inaccessible to users and which users cannot interact with. We define a novel paradigm using argumentation to empower the interaction between optimization solvers and users, supported by tractable explanations which certify or refute solutions. A solution can be from a solver or of interest to a user (in the context of ’what-if’ scenarios). Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs). We define three types of AFs, whose stable extensions are in one-to-one correspondence with schedules that are feasible, efficient and satisfying fixed decisions, respectively. We extract the argumentative explanations from these AFs and the natural language explanations from the argumentative ones.

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ICML Conference 2018 Conference Paper

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Simon Olofsson
Marc Peter Deisenroth
Ruth Misener

Healthcare companies must submit pharmaceutical drugs or medical device to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, where the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models, research has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology for introducing Gaussian process surrogates in lieu of the original mechanistic models. This allows us to extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models.

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