Valerio Perrone | DeepAI

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∙ 11/12/2021

A Simple and Fast Baseline for Tuning Large XGBoost Models

XGBoost, a scalable tree boosting algorithm, has proven effective for ma...

0 Sanyam Kapoor, et al. ∙

∙ 06/10/2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Bayesian optimization (BO) is a popular method for optimizing expensive-...

0 Eric Hans Lee, et al. ∙

∙ 06/10/2021

A multi-objective perspective on jointly tuning hardware and hyperparameters

In addition to the best model architecture and hyperparameters, a full A...

0 David Salinas, et al. ∙

∙ 04/16/2021

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution

Bayesian Optimization (BO) is a successful methodology to tune the hyper...

0 Anastasia Makarova, et al. ∙

∙ 01/22/2021

Lexical semantic change for Ancient Greek and Latin

Change and its precondition, variation, are inherent in languages. Over ...

0 Valerio Perrone, et al. ∙

∙ 12/15/2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization

Tuning complex machine learning systems is challenging. Machine learning...

0 Valerio Perrone, et al. ∙

∙ 12/15/2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale

AutoML systems provide a black-box solution to machine learning problems...

0 Piali Das, et al. ∙

∙ 11/23/2020

Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization

Bayesian optimization (BO) is a popular method to optimize expensive bla...

1 Gauthier Guinet, et al. ∙

∙ 06/09/2020

Fair Bayesian Optimization

Given the increasing importance of machine learning (ML) in our lives, a...

31 Valerio Perrone, et al. ∙

∙ 03/22/2020

Cost-aware Bayesian Optimization

Bayesian optimization (BO) is a class of global optimization algorithms,...

8 Eric Hans Lee, et al. ∙

∙ 10/15/2019

Constrained Bayesian Optimization with Max-Value Entropy Search

Bayesian optimization (BO) is a model-based approach to sequentially opt...

0 Valerio Perrone, et al. ∙

∙ 09/30/2019

A Copula approach for hyperparameter transfer learning

Bayesian optimization (BO) is a popular methodology to tune the hyperpar...

0 David Salinas, et al. ∙

∙ 09/27/2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

Bayesian optimization (BO) is a successful methodology to optimize black...

0 Valerio Perrone, et al. ∙

∙ 03/13/2019

GASC: Genre-Aware Semantic Change for Ancient Greek

Word meaning changes over time, depending on linguistic and extra-lingui...

0 Valerio Perrone, et al. ∙

∙ 02/16/2018

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

Inference for population genetics models is hindered by computationally ...

0 Jeffrey Chan, et al. ∙

∙ 12/08/2017

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start

Bayesian optimization (BO) is a model-based approach for gradient-free b...

0 Valerio Perrone, et al. ∙

∙ 09/14/2016

Relativistic Monte Carlo

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCM...

0 Xiaoyu Lu, et al. ∙

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