Leonardo Cella

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Probabilistic Inference

Top Venues

Int. J. Approx. Reason.

Publications

Leonardo Cella
Fusing Independent Inferential Models in a Black-Box Manner. BELIEF (2024)
Leonardo Cella, Ryan Martin
Variational Approximations of Possibilistic Inferential Models. BELIEF (2024)
Leonardo Cella
Distribution-free Inferential Models: Achieving finite-sample valid probabilistic inference, with emphasis on quantile regression. Int. J. Approx. Reason. 170 (2024)
Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil
Multi-task Representation Learning with Stochastic Linear Bandits. AISTATS (2023)
Leonardo Cella
Finite sample valid probabilistic inference on quantile regression. ISIPTA (2023)
Leonardo Cella, Ryan Martin
Possibility-theoretic statistical inference offers performance and probativeness assurances. Int. J. Approx. Reason. 163 (2023)
Leonardo Cella, Ryan Martin
Valid Inferential Models Offer Performance and Probativeness Assurances. BELIEF (2022)
Leonardo Cella, Ryan Martin
Validity, consonant plausibility measures, and conformal prediction. Int. J. Approx. Reason. 141 (2022)
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
Group Meritocratic Fairness in Linear Contextual Bandits. NeurIPS (2022)
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Meta Representation Learning with Contextual Linear Bandits. CoRR (2022)
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
Group Meritocratic Fairness in Linear Contextual Bandits. CoRR (2022)
Leonardo Cella, Ryan Martin
Valid inferential models for prediction in supervised learning problems. Int. J. Approx. Reason. 150 (2022)
Leonardo Cella, Ryan Martin
Direct and approximately valid probabilistic inference on a class of statistical functionals. Int. J. Approx. Reason. 151 (2022)
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Multi-task Representation Learning with Stochastic Linear Bandits. CoRR (2022)
Leonardo Cella, Massimiliano Pontil, Claudio Gentile
Best Model Identification: A Rested Bandit Formulation. ICML (2021)
Leonardo Cella, Ryan Martin
Approximately Valid and Model-Free Possibilistic Inference. BELIEF (2021)
Leonardo Cella, Massimiliano Pontil
Multi-task and meta-learning with sparse linear bandits. UAI (2021)
Leonardo Cella, Ryan Martin
Valid Inferential Models for Prediction in Supervised Learning Problems. ISIPTA (2021)
Leonardo Cella, Nicolò Cesa-Bianchi
Stochastic Bandits with Delay-Dependent Payoffs. AISTATS (2020)
Leonardo Cella, Claudio Gentile, Massimiliano Pontil
Online Model Selection: a Rested Bandit Formulation. CoRR (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. ICML (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. CoRR (2020)
Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi
Efficient Linear Bandits through Matrix Sketching. AISTATS (2019)
Leonardo Cella, Ryan Martin
Incorporating Expert Opinion in an Inferential Model while Maintaining Validity. ISIPTA (2019)
Leonardo Cella, Nicolò Cesa-Bianchi
Stochastic Bandits with Delay-Dependent Payoffs. CoRR (2019)
Leonardo Cella
Efficient Context-Aware Sequential Recommender System. WWW (Companion Volume) (2018)
Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi
Efficient Linear Bandits through Matrix Sketching. CoRR (2018)
Stefano Cereda, Leonardo Cella, Paolo Cremonesi
Estimate Features Relevance for Groups of Users. IIR (2017)
Leonardo Cella, Romaric Gaudel, Paolo Cremonesi
Kernalized Collaborative Contextual Bandits. RecSys Posters (2017)
Leonardo Cella
Modelling User Behaviors with Evolving Users and Catalogs of Evolving Items. UMAP (Adjunct Publication) (2017)
Leonardo Cella, Stefano Cereda, Massimo Quadrana, Paolo Cremonesi
Deriving Item Features Relevance from Past User Interactions. UMAP (2017)
Mehdi Elahi, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Leonardo Cella, Stefano Cereda, Paolo Cremonesi
Exploring the Semantic Gap for Movie Recommendations. RecSys (2017)