Stefano Favaro

Papers:

• Function-space MCMC for Bayesian wide neural networks, with L. Pezzetti and S. Peluchetti
• Artificial Intelligence and Statistics, 2025
• [AISTATS]
• A smoothed-Bayesian approach to frequency recovery from sketched data, with M. Beraha and M. Sesia
• Journal of the American Statistical Association, to appear
• [JASA] [ArXiv]
• On the power of private likelihood-ratio tests for goodness-of-fit in frequency tables, with M. Beraha and E. Dolera
• Bernoulli, to appear
• [BEJ] [ArXiv]
• Quantitative central limit theorems in deep neural networks, with B. Hanin, D. Marinucci, I. Nourdin and G. Peccati
• Probability Theory and Related Fields, 2025
• [PTRF] [ArXiv]
• Random measure priors in Bayesian recovery from sketches, with M. Beraha and M. Sesia
• Journal of Machine Learning Research, 2024
• [JMLR] [ArXiv]
• MCMC for Bayesian nonparametric mixture modeling under differential privacy, with M. Beraha and V. Rao
• Journal of Computational and Graphical Statistics, to appear
• [JCGS] [ArXiv]
• Preconditioned Crank-Nicolson algorithms for Bayesian wide neural networks, with S. Peluchetti and L. Pezzetti
• Neural Information Processing Systems, 2024
• [NeurIPS]
• A martingale approach to Gaussian fluctuations and laws of iterated logarithm for Ewens-Pitman model, with B. Bercu
• Stochastic Processes and their Applications, 2024
• [SPA] [ArXiv]
• Bayesian nonparametric inference for "species-sampling" problems, with C. Balocchi and Z. Naulet
• Statistical Science, to appear
• [STS] [ArXiv]
• On second-order Poincaré inequalities in non-asymptotic approximation of Gaussian neural networks, with A. Bordino and S. Fortini
• Advances in Approximate Bayesian Inference, 2024
• [AABI]
• Large-width asymptotics and training dynamics for alpha-Stable ReLU neural networks, with S. Fortini and S. Peluchetti
• Journal of Machine Learning Research, 2024
• [JMLR] [ArXiv]
• Wasserstein posterior contraction rates in non-dominated Bayesian nonparametric models, with E. Dolera and E. Mainini
• Annales de l'Institut Henri Poincaré - Probabilités et Statistiques, to appear
• [AIHP] [ArXiv]
• Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation, with T. Broderick, F. Camerlenghi and L. Masoero
• Journal of the American Statistical Association, 2024
• [JASA] [ArXiv]
• Strong posterior contraction rates via Wasserstein dynamics, with E. Dolera and E. Mainini
• Probability Theory and Related Fields, 2024
• [PTRF] [ArXiv]
• A Bayesian nonparametric approach to species sampling problems with ordering, with C. Balocchi and F. Camerlenghi
• Bayesian Analysis, to appear
• [BA] [ArXiv]
• Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes, with M. De Iorio, A. Guglielmi and Y. Lifeng
• Annals of Applied Statistics, 2023
• [AOAS] [ArXiv]
• Near-optimal estimation of the unseen under regularly varying tail populations, with Z. Naulet
• Bernoulli, 2023
• [BEJ] [ArXiv]
• Conformal frequency estimation using discrete sketched data with coverage for distinct queries, with E. Dobriban and M. Sesia
• Journal of Machine Learning Research, 2023
• [JMLR] [ArXiv]
• Deep Stable neural networks: large-width asymptotics and convergence rates, with S. Fortini and S. Peluchetti
• Bernoulli, 2023
• [BEJ] [ArXiv]
• Learning-augmented count-min sketches via Bayesian nonparametrics, with E. Dolera and S. Peluchetti
• Journal of Machine Learning Research, 2023
• [JMLR] [ArXiv]
• Conformalized frequency estimation from sketched data, with M. Sesia
• Neural Information Processing Systems, 2022
• [NeurIPS]
• Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions, with A. Bordino and S. Fortini
• Transactions on Machine Learning Research, 2022
• [TMLR] [ArXiv]
• More for less: predicting and maximizing genetic variant discovery via Bayesian nonparametrics, with T. Broderick, F. Camerlenghi and L. Masoero
• Biometrika, 2022
• [Biometrika] [ArXiv]
• Infinite-channel deep Stable convolutional neural networks, with D. Bracale, S. Fortini and S. Peluchetti
• Neural Information Processing Systems, 2021
• [NeurIPS]
• Consistent and rate optimal estimation of the missing mass, with F. Ayed, M. Battiston and F. Camerlenghi
• Annales de l'Institut Henri Poincaré - Probabilités et Statistiques, 2021
• [AIHP] [ArXiv]
• Bayesian nonparametric disclosure risk assessment, with F. Panero and T. Rigon
• Electronic Journal of Statistics, 2021
• [EJS]
• Doubly infinite neural networks: a diffusion process approach, with S. Peluchetti
• Journal of Machine Learning Research, 2021
• [JMLR] [ArXiv]
• Large-width functional asymptotics for deep Gaussian neural networks, with D. Bracale, S. Fortini and S. Peluchetti
• International Conference on Learning Representations, 2021
• [ICLR]
• A Bayesian nonparametric approach to count-min sketch under power-law data streams, with E. Dolera and S. Peluchetti
• Artificial Intelligence and Statistics, 2021
• [AISTATS]
• Optimal disclosure risk assessment, with F. Camerlenghi, Z. Naulet and F. Panero
• Annals of Statistics, 2021
• [AOS] [ArXiv]
• Upscaling human activity data: an ecological perspective, with M. Formentin, A. Maritan, S. Stivanello, S. Suweis and A. Tovo
• Proceedings of the National Academy of Sciences, 2021
• [PNAS] [ArXiv]
• Perfect sampling for posterior hierarchical Pitman-Yor processes, with S. Bacallado and L. Trippa
• Bayesian Analysis, 2021
• [BA]
• Consistent estimation of small masses in feature sampling, with F. Ayed, M. Battiston and F. Camerlenghi
• Journal of Machine Learning Research, 2021
• [JMLR] [ArXiv]
• Stable behaviour of infinitely wide deep neural networks, with S. Fortini and S. Peluchetti
  
• Artificial Intelligence and Statistics, 2020
• [AISTATS]
• Infinitely deep neural networks as diffusion processes, with S. Peluchetti
  
• Artificial Intelligence and Statistics, 2020
• [AISTATS]
• Nonparametric Bayesian multi-armed bandits for single cell experiment design, with F. Camerlenghi, B. Dimitrascu, B. Engelhardt and F. Ferrari
• Annals of Applied Statistics, 2020
• [AOAS] [ArXiv]
• Rates of convergence in de Finetti's representation theorem, and Hausdorff moment problem, with E. Dolera
• Bernoulli, 2020
  
• [BEJ] [ArXiv]
• A Berry-Esseen theorem for Pitman's alpha-diversity, with E. Dolera
• Annals of Applied Probability, 2020
• [AOAP] [ArXiv]
• Approximating predictive probabilities of Gibbs-type priors, with J. Arbel
  
• Sankhya Series A, 2020
  
• [Sankhya] [ArXiv]
• Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis, with S. Bacallado, C. Huttenhower, B. Ren and L. Trippa
• Annals of Applied Statistics, 2020
  
• [AOAS] [ArXiv]
• Neural SDE: information propagation through the lens of diffusion processes, with S. Peluchetti
• Neural Information Processing Systems, 2019
• [NeurIPS]
• A Good-Turing estimator for feature allocation models, with F. Ayed, M. Battiston and F. Camerlenghi
• Electronic Journal of Statistics, 2019
• [EJS] [ArXiv]
• Bayesian nonparametric analysis of Kingman's coalescent, with S. Feng and P. Jenkins
  
• Annales de l'Institut Henri Poincaré - Probabilités et Statistiques, 2019
• [AIHP] [ArXiv]
• Modeling population structure under hierarchical Dirichlet processes, with K. Adhikari, M. De Iorio, L. Elliott and Y.W. Teh
  
• Bayesian Analysis, 2019
  
• [BA] [ArXiv]
• Genomic variety prediction via Bayesian nonparametrics, with T. Broderick, F. Camerlenghi and L. Masoero
• Advances in Approximate Bayesian Inference, 2019
• [AABI]
• Multi-armed bandits for species discovery: a Bayesian nonparametric approach, with M. Battiston and Y.W. Teh
• Journal of the American Statistical Association, 2018
  
• [JASA]
• Dependent generalized Dirichlet priors for the analysis of acute lymphoblastic leukaemia, with W. Barcella, M. De Iorio and G. Rosner
• Biostatistics, 2018
• [Biostatistics]
• Moderate deviations for Ewens-Pitman sampling models, with S. Feng and F. Gao
  
• Sankhya Series A, 2018
• [Sankhya] [ArXiv]
• A characterization of product-form exchangeable feature probability functions, with M. Battiston, D.M. Roy and Y.W. Teh
• Annals of Applied Probability, 2018
• [AOAP] [ArXiv]
• Posterior representations of hierarchical completely random measures in trait allocation models, with T. Broderick, F. Camerlenghi and L. Masoero
  
• Neural Information Processing Systems, 2018
• [NeurIPS]
• On a general Maclaurin's inequality, with S.G. Walker
  
• Proceedings of the American Mathematical Society, 2018
• [PAMS]
• Sufficientness postulates for Gibbs-type priors and hierarchial generalizations, with S. Bacallado, M. Battiston and L. Trippa
  
• Statistical Science, 2017
• [STS]
• A marginal sampler for sigma-stable Poisson-Kingman mixture models, with M. Lomeli and Y.W. Teh
• Journal of Computational and Graphical Statistics, 2017
• [JCGS] [ArXiv]
• Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, with J. Arbel, B. Nipoti and Y.W. Teh
  
• Statistica Sinica, 2017
• [SS] [ArXiv]
• Bayesian nonparametric ordination for the analysis of microbial communities, with S. Bacallado, S. Holmes, B. Ren and L. Trippa
  
• Journal of the American Statistical Association, 2017
  
• [JASA] [ArXiv]
• Rediscovery of Good-Turing estimators via Bayesian nonparametrics, with B. Nipoti and Y.W. Teh
  
• Biometrics, 2016
• [Biometrics] [ArXiv]
• Frequency of frequencies distributions and size-dependent exchangeable random partitions, with S.G. Walker and M. Zhou
• Journal of the American Statistical Association, 2016
  
• [JASA] [ArXiv]
• On the stick-breaking representation for homogeneous NRMIs, with A. Lijoi, B. Nipoti, I. Pruenster and Y.W. Teh
• Bayesian Analysis, 2016
• [BA]
• A note on nonparametric inference for species variety with Gibbs-type priors, with L.F. James
  
• Electronic Journal of Statistics, 2016
  
• [EJS]
• Relatives of the Ewens sampling formula in Bayesian nonparametrics, with L.F. James
• Statistical Science, 2016
• [STS]
• Large deviation principles for the Ewens-Pitman sampling model, with S. Feng
  
• Electronic Journal of Probability, 2015
• [EJP] [ArXiv]
• A hybrid sampler for Poisson-Kingman mixture models, with M. Lomeli and Y.W. Teh
  
• Neural Information Processing Systems, 2015
• [NeurIPS]
• Random variate generation for Laguerre-type exponentially tilted alpha-stable distributions, with B. Nipoti and Y.W. Teh
  
• Electronic Journal of Statistics, 2015
  
• [EJS]
• Looking-backward probabilities for Gibbs-type exchangeable random partitions, with S. Bacallado and L. Trippa
• Bernoulli, 2015
• [BEJ] [ArXiv]
• Bayesian regularization of the length of memory in reversible sequences, with S. Bacallado, L. Trippa
• Journal of the Royal Statistical Society Series B, 2015
• [JRSSB]
• On the stick-breaking representation of sigma-stable Poisson-Kingman models, with M. Lomeli, B. Nipoti and Y.W. Teh
• Electronic Journal of Statistics, 2014
• [EJS]
• Posterior analysis of rare variants in Gibbs-type species sampling models, with O. Cesari and B. Nipoti
• Journal of Multivariate Analysis, 2014
• [JMA]
• Asymptotics for the number of blocks in a conditional Ewens-Pitman sampling model, with S. Feng
  
• Electronic Journal of Probability, 2014
• [EJP]
• MCMC for normalized random measure mixture models, with Y.W. Teh
• Statistical Science, 2013
  
• [STS] [ArXiv]
  
• Conditional formulae for Gibbs-type exchangeable random partitions, with A. Lijoi and I. Pruenster
  
• Annals of Applied Probability, 2013
  
• [AOAP] [ArXiv]
  
• Slice sampling sigma-stable Poisson-Kingman mixture models, with S.G. Walker
  
• Journal of Computational and Graphical Statistics, 2013
  
• [JCGS]
  
• Bayesian nonparametric analysis of reversible Markov chains, with S. Bacallado and L. Trippa
  
• Annals of Statistics, 2013
  
• [AOS] [ArXiv]
• Asymptotics for a Bayesian nonparametric estimator of species richness, with A. Lijoi and I. Pruenster
• Bernoulli, 2012
  
• [BEJ] [ArXiv]
  
• A class of normalized random measure with an exact predictive sampling scheme, with L. Trippa
• Scandinavian Journal of Statistics, 2012
  
• [SJS]
  
• On the stick-breaking representation of normalized inverse Gaussian priors, with A. Lijoi and I. Pruenster
  
• Biometrika, 2012
  
• [Biometrika]
  
• A new estimator of the discovery probability, with A. Lijoi and I. Pruenster
• Biometrics, 2012
  
• [Biometrics]
  
• Alpha-diversity processes and normalized inverse Gaussian diffusions, with M. Ruggiero and S.G. Walker
  
• Annals of Applied Probability, 2012
  
• [AOAP] [ArXiv]
  
• A class of measure-valued Markov chains and Bayesian nonparametrics, with A. Guglielmi and S.G. Walker
• Bernoulli, 2012
  
• [BEJ] [ArXiv]
  
• On a class of random probability measures with a general predictive structure, with I. Pruenster and S.G. Walker
• Scandinavian Journal of Statistics, 2011
  
• [SJS]
  
• Bayesian nonparametric inference for species variety with a two parameter Poisson-Dirichlet process prior, with A. Lijoi, R.H. Mena and I. Pruenster
• Journal of the Royal Statistical Society Series B, 2009
  
• [JRSSB]
  
• A Gibbs-sampler based random process in Bayesian nonparametrics, with M. Ruggiero and S.G. Walker
  
• Electronic Journal of Statistics, 2009
  
• [EJS]
• A generalized constructive definition for the Dirichlet process, with S.G. Walker
• Statistics and Probability Letters, 2008
  
• [SPL]

    Other publications

• On Johnson's "sufficientness" postulates for feature sampling models, with F. Camerlenghi and E. Dolera
• Mathematics - A festschrift for Eugenio Regazzini's 75th birthday, 2021
• [Mathematics] [ArXiv]
• A compound Poisson perspective of Ewens-Pitman sampling model, with E. Dolera
• Mathematics - A festschrift for Eugenio Regazzini's 75th birthday, 2021
• [Mathematics] [ArXiv]
• Discussion of "Sparse graphs using exchangeable random measures" by F. Caron and E.B. Fox, with M. Battiston
• Journal of the Royal Statistical Society Series B, 2017
• [JRSSB] [ArXiv]
• Addendum to "On a general Maclaurin's inequality" with S.G.Walker
• Proceedings of the American Mathematical Society, 2018
• [PAMS]
• Bayesian nonparametric inference for shared species richness in multiple populations, with S. Bacallado and L. Trippa
  
• Journal of Statistical Planning and Inference - Special issue on Bayesian nonparametrics, 2015
• [JSPI]
• Bayesian inference on population structure: from parametric to nonparametric modeling, with M. De Iorio and Y.W. Teh
• Nonparametric Bayesian Inference in Biostatistics, 2015
• [Springer]
• Are Gibbs-type priors the most natural generalization of the Dirichlet process?, with P. De Blasi, A. Lijoi, R.H. Mena, I. Pruenster and M. Ruggiero
  
• IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on Bayesian nonparametrics, 2015
• [TPAMI] [ArXiv]
• On a class of sigma-stable Poisson-Kingman models and an effective marginalized sampler, with M. Lomeli and Y.W. Teh
  
• Statistics and Computing - Special issue MCMSki IV, 2015
• [SC]
• Discussion of "On simulation and properties of stable law" by L. Devroye and L.F. James, with B. Nipoti
• Statistical Methods and Applications, 2014
• [SMA]
• Two tales about Bayesian nonparametric modeling, with P. De Blasi, A. Lijoi, R.H. Mena and I. Pruenster
  
• Workshop on "Bayesian Statistical Sciences" at JSM, 2012
• [JSM]
  
• Contributions to the Dirichlet process and related classes of random probability measures
• Ph.D. Thesis, Dipartimento di Scienze delle Decisioni, Università Commerciale "L. Bocconi", 2009
  
• [Università "L. Bocconi"]

    Submitted papers

• Transform-scaled process priors for trait allocations in Bayesian nonparametrics, with M. Beraha
  [ArXiv] 2024
• Minimax-optimal estimation of high-order missing masses, and the rare-type match problem, with Z. Naulet
  [ArXiv] 2024
• Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem, with S. Fortini
  [ArXiv] 2024
• On strong posterior contraction rates for Besov-Laplace priors in the white noise model, with E. Dolera and M. Giordano
  [ArXiv] 2024
• Quasi-Bayesian sequential deconvolution, with S. Fortini
  [ArXiv] 2024
• Asymptotic behavior of clusters in hierarchical species sampling models, with S. Feng and J.E. Paguyo
  [ArXiv] 2024
• Gaussian credible intervals in Bayesian nonparametric estimation of the unseen, with C. Contardi and E. Dolera
  [ArXiv] 2025
• Law of large numbers and central limit theorem for Ewens-Pitman model, with C. Contardi and E. Dolera
  [ArXiv] 2025
• 
  

• ArXiv
• Google Scholar
• IMS
• ISBA
• ELLIS
• UMI
• De Castro Statistics Initiative
• SciLua