Valerio Perrone

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 29

Top Topics

Transfer Learning

Top Venues

Publications

Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR (2024)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Automatic Termination for Hyperparameter Optimization. AutoML (2022)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD (2021)
Théo Galy-Fajou, Valerio Perrone, Manfred Opper
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy 23 (8) (2021)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR (2021)
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR (2021)
Valerio Perrone, Simon Hengchen, Marco Palma, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
Lexical semantic change for Ancient Greek and Latin. CoRR (2021)
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. AIES (2021)
Sanyam Kapoor, Valerio Perrone
A Simple and Fast Baseline for Tuning Large XGBoost Models. CoRR (2021)
David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR (2021)
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI (2021)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR (2020)
Gauthier Guinet, Valerio Perrone, Cédric Archambeau
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. CoRR (2020)
David Salinas, Huibin Shen, Valerio Perrone
A Quantile-based Approach for Hyperparameter Transfer Learning. ICML (2020)
Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger
Cost-aware Bayesian Optimization. CoRR (2020)
Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. CoRR (2020)
Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel
Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR (2020)
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR (2019)
Valerio Perrone, Marco Palma, Simon Hengchen, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
GASC: Genre-Aware Semantic Change for Ancient Greek. LChange@ACL (2019)
Valerio Perrone, Huibin Shen
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. NeurIPS (2019)
Valerio Perrone, Marco Palma, Simon Hengchen, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
GASC: Genre-Aware Semantic Change for Ancient Greek. CoRR (2019)
David Salinas, Huibin Shen, Valerio Perrone
A Copula approach for hyperparameter transfer learning. CoRR (2019)
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR (2019)
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks. CoRR (2018)
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
Scalable Hyperparameter Transfer Learning. NeurIPS (2018)
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks. NeurIPS (2018)
Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh
Poisson Random Fields for Dynamic Feature Models. J. Mach. Learn. Res. 18 (2017)
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer
Relativistic Monte Carlo. AISTATS (2017)