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Search · four archives
14 papers · ranked by Valyu relevance
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros + 5 more
'Nadathur Satish' 'Narayanan Sundaram' 'Mostofa Patwary' 'Prabhat' 'Ryan P. Adams'] Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model…
Stefan Falkner, Aaron Klein, Frank Hutter
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other hand, bandit-based configuration evaluation approaches based on random search lack…
Shashank Shekhar, Adesh Bansode, Asif Salim
—Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right…
Julien-Charles Lévesque, Christian Gagné, Robert Sabourin
In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of an ensemble with regards to its hyperparameters. Our method consists in building a fixed-size ensemble, optimizing the configuration of one classifier of the ensemble at each iteration of…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Kevin Swersky, Jasper Snoek, Ryan P. Adams
In machine learning, the term "training" is used to describe the procedure of fitting a model to data. In many popular models, this fitting procedure is framed as an optimization problem, in which a loss is minimized as a function of the parameters. In all but the simplest machine learning models, this minimization…
Tinu Theckel Joy, Santu Rana, Sunil Gupta, Svetha Venkatesh
In this paper, we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC learning theory; the generalization bound will be higher for a small subset of data compared to the whole, and the highest accuracy for a…
Abhilash Nandy, Chandan Kumar‐Sinha, Deepak Mewada, Soumya Sharma
In this report we survey Bayesian Optimization methods focussed on Multi-Armed Bandit Problem. We take the help of the paper "Portfolio Allocation for Bayesian Optimization" [1]. We report a small literature survey on the acquisition functions and the types of portfolio strategies used in the papers [1] [2]. We also…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Doniyor Ulmasov, Caroline Baroukh, Benoît Chachuat, Marc Peter Deisenroth + 1 more
'Marc Peter Deisenroth' 'Ruth Misener'] Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of BO is cheap, but experiments are time consuming or costly. In practice, this allows us to optimize…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…