Search · four archives
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…
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…
Théophile Sanchez, Jean Cury, Guillaume Charpiat, Flora Jay
For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve.…
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…
Md Ochiuddin Miah, Umme Habiba, Md Faisal Kabir
Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students’ cognitive states. Real-time classification of students’ confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since…
Devashish Tripathi, Analabha Basu
Environmental factors play a pivotal role in shaping the genetic and phenotypic diversity among organisms. Understanding the influence of the environment on a biological phenomenon is essential for deciphering the mechanisms resulting in trait differences among organisms. In this study, we present a novel approach…
Ekaterina Noskova, Viacheslav Borovitskiy
Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware…
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…
Jaime Carrasco, Fulgencio Lisón, Andrés Weintraub
Traditional Species Distribution Models (SDMs) may not be appropriate when examples of one class (e.g. absence or pseudo-absences) greatly outnumber examples of the other class (e.g. presences or observations), because they tend to favor the learning of observations more frequently. We present an ensemble method called…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…