Author name cluster

Calvin Tsay

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13 papers

2 author rows

ICML Conference 2025 Conference Paper

Certification for Differentially Private Prediction in Gradient-Based Training

Matthew Wicker
Philip Sosnin
Igor Shilov
Adrianna Janik
Mark Niklas Müller
Yves-Alexandre de Montjoye
Adrian Weller
Calvin Tsay

We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global sensitivity of the model, often resulting in sub-optimal privacy-utility trade-offs compared to private training. We introduce a novel approach for computing dataset-specific upper bounds on prediction sensitivity by leveraging convex relaxation and bound propagation techniques. By combining these bounds with the smooth sensitivity mechanism, we significantly improve the privacy analysis of private prediction compared to global sensitivity-based approaches. Experimental results across real-world datasets in medical image classification and natural language processing demonstrate that our sensitivity bounds are can be orders of magnitude tighter than global sensitivity. Our approach provides a strong basis for the development of novel privacy preserving technologies.

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TMLR Journal 2025 Journal Article

Certified Robustness to Data Poisoning in Gradient-Based Training

Philip Sosnin
Mark Niklas Mueller
Maximilian Baader
Calvin Tsay
Matthew Robert Wicker

Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. Provably bounding the behavior of learning algorithms under such attacks remains an open problem. In this work, we address this challenge by developing the first framework providing provable guarantees on the behavior of models trained with potentially manipulated data without modifying the model or learning algorithm. In particular, our framework certifies robustness against untargeted and targeted poisoning, as well as backdoor attacks, for bounded and unbounded manipulations of the training inputs and labels. Our method leverages convex relaxations to over-approximate the set of all possible parameter updates for a given poisoning threat model, allowing us to bound the set of all reachable parameters for any gradient-based learning algorithm. Given this set of parameters, we provide bounds on worst-case behavior, including model performance and backdoor success rate. We demonstrate our approach on multiple real-world datasets from applications including energy consumption, medical imaging, and autonomous driving.

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ICML Conference 2025 Conference Paper

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization

Mujin Cheon
Jay H. Lee
Dong-Yeun Koh
Calvin Tsay

To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated promising results in recent years. However, owing to the curse of dimensionality, most of these methods make significant approximations or suffer scalability issues. This paper presents a novel reinforcement learning (RL)-based framework for multi-step lookahead BO in high-dimensional black-box optimization problems. The proposed method enhances the scalability and decision-making quality of multi-step lookahead BO by efficiently solving the sequential dynamic program of the BO process in a near-optimal manner using RL. We first introduce an Attention-DeepSets encoder to represent the state of knowledge to the RL agent and subsequently propose a multi-task, fine-tuning procedure based on end-to-end (encoder-RL) on-policy learning. We evaluate the proposed method, EARL-BO (Encoder Augmented RL for BO), on synthetic benchmark functions and hyperparameter tuning problems, finding significantly improved performance compared to existing multi-step lookahead and high-dimensional BO methods.

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RLC Conference 2025 Conference Paper

Gaussian Process Q-Learning for Finite-Horizon Markov Decision Processes

Maximilian Bloor
Tom Savage
Calvin Tsay
Antonio Del rio chanona
Max Mowbray

Many real-world control and optimization problems require making decisions over a finite time horizon to maximize performance. This paper proposes a reinforcement learning framework that approximately solves the finite-horizon Markov Decision Process (MDP) by combining Gaussian Processes (GPs) with Q-learning. The method addresses two key challenges: the tractability of exact dynamic programming in continuous state-control spaces, and the need for sample-efficient state-action value function approximation in systems where data collection is expensive. Using GPs and backward induction, we construct state-action value function approximations that enable efficient policy learning with limited data. To handle the computational burden of GPs as data accumulate across iterations, we propose a subset selection mechanism that uses M-determinantal point processes to draw diverse, high-performing subsets. The proposed method is evaluated on a linear quadratic regulator problem and online optimization of a non-isothermal semi-batch reactor. Improved learning efficiency is shown relative to the use of Deep Q-networks and exact GPs built with all available data.

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RLJ Journal 2025 Journal Article

Gaussian Process Q-Learning for Finite-Horizon Markov Decision Processes

Maximilian Bloor
Tom Savage
Calvin Tsay
Antonio Del rio chanona
Max Mowbray

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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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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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