Ritabrata Dutta | DeepAI

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Featured Co-authors

Samuel Kaski
118 publications
Yulan He
43 publications
Kerrie Mengersen
41 publications
Jukka Corander
35 publications
Antonietta Mira
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Michael U. Gutmann
24 publications
Jukka-Pekka Onnela
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Paul Blomstedt
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Fabrizio Ruggeri
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Sohan Seth
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Bastien Chopard
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∙ 05/31/2022

Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization

Bayesian Likelihood-Free Inference methods yield posterior approximation...

0 Lorenzo Pacchiardi, et al. ∙

∙ 05/25/2022

RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators

In this paper, we study the task of improving the cohesion and coherence...

0 Rilwan A. Adewoyin, et al. ∙

∙ 12/15/2021

Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization

Probabilistic forecasting consists of stating a probability distribution...

0 Lorenzo Pacchiardi, et al. ∙

∙ 09/10/2021

Bayesian inference of PolII dynamics over the exclusion process

Transcription is a complex phenomenon that permits the conversion of gen...

0 Massimo Cavallaro, et al. ∙

∙ 04/08/2021

Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators

We propose a framework for Bayesian Likelihood-Free Inference (LFI) base...

0 Lorenzo Pacchiardi, et al. ∙

∙ 12/20/2020

Score Matched Conditional Exponential Families for Likelihood-Free Inference

To perform Bayesian inference for stochastic simulator models for which ...

0 Lorenzo Pacchiardi, et al. ∙

∙ 12/17/2020

High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations

The accurate prediction of precipitation is important to allow for relia...

0 Sherman Lo, et al. ∙

∙ 10/13/2020

Interpretable pathological test for Cardio-vascular disease: Approximate Bayesian computation with distance learning

Cardio/cerebrovascular diseases (CVD) have become one of the major healt...

0 Ritabrata Dutta, et al. ∙

∙ 08/20/2020

TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall

Climate models (CM) are used to evaluate the impact of climate change on...

0 Rilwan Adewoyin, et al. ∙

∙ 06/29/2020

Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England

A mathematical model for the COVID-19 pandemic spread in England is pres...

0 Ritabrata Dutta, et al. ∙

∙ 09/28/2019

Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption

Approximate Bayesian computation (ABC) provides us with a way to infer p...

0 Lorenzo Pacchiardi, et al. ∙

∙ 04/08/2018

Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water

Molecular dynamics (MD) simulations give access to equilibrium and/or dy...

0 Ritabrata Dutta, et al. ∙

∙ 04/07/2018

Likelihood-Free Parameter Estimation for Dynamic Queueing Networks

Many complex real-world systems such as airport terminals, manufacturing...

0 Anthony Ebert, et al. ∙

∙ 11/13/2017

ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation

ABCpy is a highly modular scientific library for Approximate Bayesian Co...

0 Ritabrata Dutta, et al. ∙

∙ 10/03/2017

Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing

A numerical model that quantitatively describes how platelets in a shear...

0 Ritabrata Dutta, et al. ∙

∙ 09/26/2017

Bayesian Inference of Spreading Processes on Networks

Infectious diseases are studied to understand their spreading mechanisms...

0 Ritabrata Dutta, et al. ∙

∙ 09/26/2017

Bayesian Inference of Network Epidemics

Infectious diseases are studied to understand their spreading mechanisms...

0 Ritabrata Dutta, et al. ∙

∙ 03/30/2016

Bayesian inference in hierarchical models by combining independent posteriors

Hierarchical models are versatile tools for joint modeling of data sets ...

0 Ritabrata Dutta, et al. ∙

∙ 05/19/2015

Modelling-based experiment retrieval: A case study with gene expression clustering

Motivation: Public and private repositories of experimental data are gro...

0 Paul Blomstedt, et al. ∙

∙ 02/19/2015

Classification and Bayesian Optimization for Likelihood-Free Inference

Some statistical models are specified via a data generating process for ...

0 Michael U. Gutmann, et al. ∙

∙ 07/18/2014

Likelihood-free inference via classification

Increasingly complex generative models are being used across disciplines...

0 Michael U. Gutmann, et al. ∙

∙ 10/08/2013

Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space

We address the problem of retrieving relevant experiments given a query ...

0 Ritabrata Dutta, et al. ∙