Estimate Features Relevance for Groups of Users. - Stefano Cereda; Leonardo Cella; Paolo Cremonesi (IIR, 2017)

Kernalized Collaborative Contextual Bandits. - Leonardo Cella; Romaric Gaudel; Paolo Cremonesi (RecSys Posters, 2017)

Exploring the Semantic Gap for Movie Recommendations. - Mehdi Elahi; Yashar Deldjoo; Farshad Bakhshandegan Moghaddam; Leonardo Cella; Stefano Cereda; Paolo Cremonesi (RecSys, 2017)

Modelling User Behaviors with Evolving Users and Catalogs of Evolving Items. - Leonardo Cella (UMAP (Adjunct Publication), 2017)

Deriving Item Features Relevance from Past User Interactions. - Leonardo Cella; Stefano Cereda; Massimo Quadrana; Paolo Cremonesi (UMAP, 2017)

Efficient Context-Aware Sequential Recommender System. - Leonardo Cella (WWW (Companion Volume), 2018)

Efficient Linear Bandits through Matrix Sketching. - Ilja Kuzborskij; Leonardo Cella; Nicolò Cesa-Bianchi (AISTATS, 2019)

Incorporating Expert Opinion in an Inferential Model while Maintaining Validity. - Leonardo Cella; Ryan Martin (ISIPTA, 2019)

Stochastic Bandits with Delay-Dependent Payoffs. - Leonardo Cella; Nicolò Cesa-Bianchi (AISTATS, 2020)

Meta-learning with Stochastic Linear Bandits. - Leonardo Cella; Alessandro Lazaric; Massimiliano Pontil (ICML, 2020)

Online Model Selection: a Rested Bandit Formulation. - Leonardo Cella; Claudio Gentile; Massimiliano Pontil (CoRR, 2020)

Approximately Valid and Model-Free Possibilistic Inference. - Leonardo Cella; Ryan Martin (BELIEF, 2021)

Best Model Identification: A Rested Bandit Formulation. - Leonardo Cella; Massimiliano Pontil; Claudio Gentile (ICML, 2021)

Multi-task and meta-learning with sparse linear bandits. - Leonardo Cella; Massimiliano Pontil (UAI, 2021)

Valid Inferential Models Offer Performance and Probativeness Assurances. - Leonardo Cella; Ryan Martin (BELIEF, 2022)

Group Meritocratic Fairness in Linear Contextual Bandits. - Riccardo Grazzi; Arya Akhavan; John Isak Texas Falk; Leonardo Cella; Massimiliano Pontil (NeurIPS, 2022)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

Meta Representation Learning with Contextual Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

Validity, consonant plausibility measures, and conformal prediction. - Leonardo Cella; Ryan Martin (Int. J. Approx. Reason., 2022)

Valid inferential models for prediction in supervised learning problems. - Leonardo Cella; Ryan Martin (Int. J. Approx. Reason., 2022)

Direct and approximately valid probabilistic inference on a class of statistical functionals. - Leonardo Cella; Ryan Martin (Int. J. Approx. Reason., 2022)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Grégoire Pacreau; Massimiliano Pontil (AISTATS, 2023)

Finite sample valid probabilistic inference on quantile regression. - Leonardo Cella (ISIPTA, 2023)

Possibility-theoretic statistical inference offers performance and probativeness assurances. - Leonardo Cella; Ryan Martin (Int. J. Approx. Reason., 2023)

Fusing Independent Inferential Models in a Black-Box Manner. - Leonardo Cella (BELIEF, 2024)

Variational Approximations of Possibilistic Inferential Models. - Leonardo Cella; Ryan Martin (BELIEF, 2024)

Distribution-free Inferential Models: Achieving finite-sample valid probabilistic inference, with emphasis on quantile regression. - Leonardo Cella (Int. J. Approx. Reason., 2024)

Distribution-free possibilistic inference on conditional quantities. - Leonardo Cella (ISIPTA, 2025)

Computationally efficient variational-like approximations of possibilistic inferential models. - Leonardo Cella; Ryan Martin (Int. J. Approx. Reason., 2025)