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ORCIDLeonardo Cella
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2020 – today
- 2024
[j5]Leonardo Cella:
Distribution-free Inferential Models: Achieving finite-sample valid probabilistic inference, with emphasis on quantile regression. Int. J. Approx. Reason. 170: 109211 (2024)- [c20]Leonardo Cella, Ryan Martin:
Variational Approximations of Possibilistic Inferential Models. BELIEF 2024: 121-130 - [c19]Leonardo Cella:
Fusing Independent Inferential Models in a Black-Box Manner. BELIEF 2024: 189-196 - 2023
- [j4]Leonardo Cella, Ryan Martin:
Possibility-theoretic statistical inference offers performance and probativeness assurances. Int. J. Approx. Reason. 163: 109060 (2023) - [c18]Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil:
Multi-task Representation Learning with Stochastic Linear Bandits. AISTATS 2023: 4822-4847 - [c17]Leonardo Cella:
Finite sample valid probabilistic inference on quantile regression. ISIPTA 2023: 109-118 - 2022
- [j3]Leonardo Cella, Ryan Martin:
Validity, consonant plausibility measures, and conformal prediction. Int. J. Approx. Reason. 141: 110-130 (2022) - [j2]Leonardo Cella, Ryan Martin:
Valid inferential models for prediction in supervised learning problems. Int. J. Approx. Reason. 150: 1-18 (2022) - [j1]Leonardo Cella, Ryan Martin:
Direct and approximately valid probabilistic inference on a class of statistical functionals. Int. J. Approx. Reason. 151: 205-224 (2022) - [c16]Leonardo Cella, Ryan Martin:
Valid Inferential Models Offer Performance and Probativeness Assurances. BELIEF 2022: 219-228 - [c15]Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil:
Group Meritocratic Fairness in Linear Contextual Bandits. NeurIPS 2022 - [i7]Leonardo Cella, Karim Lounici, Massimiliano Pontil:
Multi-task Representation Learning with Stochastic Linear Bandits. CoRR abs/2202.10066 (2022) - [i6]Leonardo Cella, Karim Lounici, Massimiliano Pontil:
Meta Representation Learning with Contextual Linear Bandits. CoRR abs/2205.15100 (2022) - [i5]Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk
, Leonardo Cella, Massimiliano Pontil:
Group Meritocratic Fairness in Linear Contextual Bandits. CoRR abs/2206.03150 (2022) - 2021
- [c14]Leonardo Cella, Ryan Martin:
Approximately Valid and Model-Free Possibilistic Inference. BELIEF 2021: 127-136 - [c13]Leonardo Cella, Massimiliano Pontil, Claudio Gentile:
Best Model Identification: A Rested Bandit Formulation. ICML 2021: 1362-1372 - [c12]Leonardo Cella, Ryan Martin:
Valid Inferential Models for Prediction in Supervised Learning Problems. ISIPTA 2021: 72-82 - [c11]Leonardo Cella, Massimiliano Pontil:
Multi-task and meta-learning with sparse linear bandits. UAI 2021: 1692-1702 - 2020
- [c10]Leonardo Cella, Nicolò Cesa-Bianchi:
Stochastic Bandits with Delay-Dependent Payoffs. AISTATS 2020: 1168-1177 - [c9]Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil:
Meta-learning with Stochastic Linear Bandits. ICML 2020: 1360-1370 - [i4]Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil:
Meta-learning with Stochastic Linear Bandits. CoRR abs/2005.08531 (2020) - [i3]Leonardo Cella, Claudio Gentile, Massimiliano Pontil:
Online Model Selection: a Rested Bandit Formulation. CoRR abs/2012.03522 (2020)
2010 – 2019
- 2019
- [c8]Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi:
Efficient Linear Bandits through Matrix Sketching. AISTATS 2019: 177-185 - [c7]Leonardo Cella, Ryan Martin:
Incorporating Expert Opinion in an Inferential Model while Maintaining Validity. ISIPTA 2019: 68-77 - [i2]Leonardo Cella, Nicolò Cesa-Bianchi:
Stochastic Bandits with Delay-Dependent Payoffs. CoRR abs/1910.02757 (2019) - 2018
- [c6]Leonardo Cella:
Efficient Context-Aware Sequential Recommender System. WWW (Companion Volume) 2018: 1391-1394 - [i1]Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi:
Efficient Linear Bandits through Matrix Sketching. CoRR abs/1809.11033 (2018) - 2017
- [c5]Stefano Cereda, Leonardo Cella, Paolo Cremonesi:
Estimate Features Relevance for Groups of Users. IIR 2017: 80-83 - [c4]Leonardo Cella, Romaric Gaudel, Paolo Cremonesi:
Kernalized Collaborative Contextual Bandits. RecSys Posters 2017 - [c3]Mehdi Elahi, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Leonardo Cella, Stefano Cereda, Paolo Cremonesi:
Exploring the Semantic Gap for Movie Recommendations. RecSys 2017: 326-330 - [c2]Leonardo Cella:
Modelling User Behaviors with Evolving Users and Catalogs of Evolving Items. UMAP (Adjunct Publication) 2017: 115-116 - [c1]Leonardo Cella, Stefano Cereda, Massimo Quadrana, Paolo Cremonesi:
Deriving Item Features Relevance from Past User Interactions. UMAP 2017: 275-279
Coauthor Index
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last updated on 2024-08-29 21:54 CEST by the dblp team
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