Publications
2023
A. Klein, J. Golebiowski, X. Ma, V. Perrone, C. Archambeau. Structural Pruning of Large Language Models via Neural Architecture Search. AutoML-Conf Workshop Track. 2023.
2022
D. Salinas, M. Seeger, A. Klein, V. Perrone, M. Wistuba, C. Archambeau. Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML-Conf. 2022.
A. Makarova, H. Shen, V. Perrone, A. Klein, J. B. Faddoul, A. Krause, M. Seeger and C. Archambeau. Automatic Termination for Hyperparameter Optimization. AutoML-Conf. 2022. Best Paper Award.
2021
S. Kapoor, V. Perrone. A Simple and Fast Baseline for Tuning Large XGBoost Models. arXiv preprint arXiv:2111.06924. 2021.
T. Galy-Fajou, V. Perrone, M. Opper. Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation. Entropy. 2021.
[pdf]
V. Perrone, H. Shen, A. Zolic, I. Shcherbatyi, A. Ahmed, T. Bansal, M. Donini, F. Winkelmolen, R. Jenatton, J. B. Faddoul, B. Pogorzelska, M. Miladinovic, K. Kenthapadi, M. Seeger, C. Archambeau. Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD. 2021.
E. Lee, D. Ericksson, V. Perrone, M. Seeger. A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI. 2021.
[pdf]
V. Perrone, M. Donini, B. Zafar, R. Schmucker, K. Kenthapadi, C. Archambeau. Fair Bayesian Optimization. AIES. 2021.
A. Makarova, H. Shen, V. Perrone, A. Klein, J. B. Faddoul, A. Krause, M. Seeger and C. Archambeau. Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. ICLR Workshop on NAS. 2021.
N. Ivkin, Z. Karnin, V. Perrone, Giovanni Zappella (alphabetical order). Cost-Aware Adversarial Best Arm Identification. ICLR Workshop on NAS. 2021.
[pdf]
D. Salinas, V. Perrone, C. Archambeau, O. Cruchant. A multi-objective perspective on tuning hardware and hyperparameters. ICLR Workshop on NAS. 2021.
V. Perrone, S. Hengchen, M. Palma, A. Vatri, J. Q. Smith, B. McGillivray. Lexical semantic change for Ancient Greek and Latin. Computational Approaches to Semantic Change, Language Variation, Chapter 9. 2021.
2020
T. Galy-Fajou, V. Perrone, M. Opper. Gaussian Density Parametrization Flow: Particle and Stochastic Approaches. AABI. 2020.
[pdf]
P. Das, V. Perrone, N. Ivkin, T. Bansal, Z. Karnin, H. Shen, I. Shcherbatyi, Y. Elor, W. Wu, A. Zolic, T. Lienart, A. Tang, A. Ahmed, J. B. Faddoul, R. Jenatton, F. Winkelmolen, P. Gautier, L. Dirac, A. Perunicic, M. Miladinovic, G. Zappella, C. Archambeau, M. Seeger, B. Dutt, L. Rouesnel. Amazon SageMaker Autopilot: a white box AutoML solution at scale. arXiv preprint arXiv:2012.08483. 2020.
G. Guinet*, V. Perrone*, C. Archambeau (*joint first author). Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. NeurIPS Workshop on Meta-Learning. 2020.
R. Schmucker, M. Donini, V. Perrone, M. B. Zafar, C. Archambeau. Multi-Objective Multi-Fidelity Hyperparameter Optimization with Application to Fairness. NeurIPS Workshop on Meta-Learning. 2020.
[pdf] [workshop link]
D. Salinas, H. Shen, V. Perrone. A Quantile-based Approach for Hyperparameter Transfer Learning. ICML. 2020.
V. Perrone, M. Donini, K. Kenthapadi, C. Archambeau. Bayesian Optimization with Fairness Constraints. ICML Workshop on AutoML. 2020. Best Paper Award.
E. H. Lee, V. Perrone, C. Archambeau, M. Seeger. Cost-aware Bayesian Optimization. ICML Workshop on AutoML. 2020.
2019
V. Perrone, H. Shen, M. Seeger, C. Archambeau, R. Jenatton. Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. NeurIPS. 2019.
V. Perrone, I. Shcherbatyi, R. Jenatton, C. Archambeau, M. Seeger. Constrained Bayesian Optimization with Max-Value Entropy Search. NeurIPS Workshop on Meta-Learning. 2019.
V. Perrone, M. Palma, S. Hengchen, A. Vatri, J. Q. Smith, B. McGillivray. GASC: Genre-Aware Semantic Change for Ancient Greek. ACL Workshop on Language Change. 2019.
2018
V. Perrone, R. Jenatton, M. Seeger, C. Archambeau. Scalable Hyperparameter Transfer Learning. NeurIPS. 2018.
J. Chan, V. Perrone, J. Spence, P. A. Jenkins, S. Mathieson and Yun S. Song. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks. NeurIPS. 2018. Spotlight (top 3.5% of submissions).
2017
V. Perrone, R. Jenatton, M. Seeger, C. Archambeau. Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start. NeurIPS Workshop on Meta-Learning. 2017. Contributed Talk (top 5% of submissions).
V. Perrone, P. A. Jenkins, D. Spano, Y. W. Teh. Poisson Random Fields for Dynamic Feature Models. Journal of Machine Learning Research. 2017.
X. Lu*, V. Perrone*, L. Hasenclever, Y. W. Teh, S. J. Vollmer (*joint first author). Relativistic Monte Carlo. AISTATS. 2017.