47 citations · 111 across the 25 of their papers we have counts for
26 papers
Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search
Zhiliang Chen, Sebastian Ament, David Eriksson +4
Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, enabling practitioners to predic…
Pitfalls and Remedies for Multi-Task Bayesian Optimization
Carl Hvarfner, Sam Daulton, Max Balandat +1
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We r…
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
Zhiyuan Jerry Lin, Benjamin Letham, Samuel Dooley +2
System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedbac…
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Hanxian Huang, Igor Fedorov, Andrey Gromov +14
Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce nea…
Empirical Gaussian Processes
Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao +3
Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This k…
BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
Samuel Daulton, David Eriksson, Maximilian Balandat +1
Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully e…
Informed Initialization for Bayesian Optimization and Active Learning
Carl Hvarfner, David Eriksson, Eytan Bakshy +1
Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the…
LILO: Bayesian Optimization with Natural Language Feedback
Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham +3
Many real-world optimization problems are guided by complex, subjective preferences that are difficult to express as explicit closed-form objectives. In response, we introduce Lang…
Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
Qing Feng, Samuel Daulton, Benjamin Letham +2
Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and lea…
Scalable Gaussian Processes with Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +3
Applying Gaussian processes (GPs) to very large datasets remains a challenge due to limited computational scalability. Matrix structures, such as the Kronecker product, can acceler…
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +1
A key task in AutoML is to model learning curves of machine learning models jointly as a function of model hyper-parameters and training progression. While Gaussian processes (GPs)…
Robust Gaussian Processes via Relevance Pursuit
Sebastian Ament, Elizabeth Santorella, David Eriksson +3
Gaussian processes (GPs) are non-parametric probabilistic regression models that are popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates. H…
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
Shangda Yang, Vitaly Zankin, Maximilian Balandat +4
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.…
Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman +6
This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonli…
Joint Composite Latent Space Bayesian Optimization
Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat +1
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When d…
Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Jiayue Wan, Raul Astudillo +3
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of functio…
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Sebastian Ament, Samuel Daulton, David Eriksson +2
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often e…
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
C. Allaire, R. Ammendola, E. -C. Aschenauer +92
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune t…
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat +3
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 us…
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson +3
Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications.…
Robust Multi-Objective Bayesian Optimization Under Input Noise
Samuel Daulton, Sait Cakmak, Maximilian Balandat +3
Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing proce…
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
Wesley Maddox, Qing Feng, Max Balandat
Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a fu…
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat +2
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimi…
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Samuel Daulton, David Eriksson, Maximilian Balandat +1
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives. When the objectives are expensive-to-evaluate, multi-objective Ba…