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Valerio Perrone
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2020 – today
- 2024
- [i16]Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau:
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR abs/2405.02267 (2024) - 2022
- [c11]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: 7/1-21 - [c10]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: 16/1-23 - 2021
- [j2]Théo Galy-Fajou
, Valerio Perrone, Manfred Opper
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy 23(8): 990 (2021) - [c9]Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. AIES 2021: 854-863 - [c8]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: 3463-3471 - [c7]Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger:
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI 2021: 568-577 - [i15]Valerio Perrone, Simon Hengchen, Marco Palma
Lexical semantic change for Ancient Greek and Latin. CoRR abs/2101.09069 (2021) - [i14]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 abs/2104.08166 (2021) - [i13]David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau:
A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR abs/2106.05680 (2021) - [i12]Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger:
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR abs/2106.06079 (2021) - [i11]Sanyam Kapoor, Valerio Perrone:
A Simple and Fast Baseline for Tuning Large XGBoost Models. CoRR abs/2111.06924 (2021) - 2020
- [c6]David Salinas, Huibin Shen, Valerio Perrone:
A Quantile-based Approach for Hyperparameter Transfer Learning. ICML 2020: 8438-8448 - [i10]Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger:
Cost-aware Bayesian Optimization. CoRR abs/2003.10870 (2020) - [i9]Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. CoRR abs/2006.05109 (2020) - [i8]Gauthier Guinet, Valerio Perrone, Cédric Archambeau:
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. CoRR abs/2011.11456 (2020) - [i7]Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Liénart, 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 abs/2012.08483 (2020) - [i6]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 abs/2012.08489 (2020)
2010 – 2019
- 2019
- [c5]Valerio Perrone, Marco Palma
GASC: Genre-Aware Semantic Change for Ancient Greek. LChange@ACL 2019: 56-66 - [c4]Valerio Perrone, Huibin Shen:
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. NeurIPS 2019: 12751-12761 - [i5]Valerio Perrone, Marco Palma
GASC: Genre-Aware Semantic Change for Ancient Greek. CoRR abs/1903.05587 (2019) - [i4]Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton:
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR abs/1909.12552 (2019) - [i3]David Salinas, Huibin Shen, Valerio Perrone:
A Copula approach for hyperparameter transfer learning. CoRR abs/1909.13595 (2019) - [i2]Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger:
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR abs/1910.07003 (2019) - 2018
- [c3]Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau:
Scalable Hyperparameter Transfer Learning. NeurIPS 2018: 6846-6856 - [c2]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: 8603-8614 - [i1]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 abs/1802.06153 (2018) - 2017
- [j1]Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh:
Poisson Random Fields for Dynamic Feature Models. J. Mach. Learn. Res. 18: 127:1-127:45 (2017) - [c1]Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer:
Relativistic Monte Carlo. AISTATS 2017: 1236-1245 - 2016
- [d1]Valerio Perrone:
NIPS Conference Papers 1987-2015. UCI Machine Learning Repository, 2016
Coauthor Index
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