decision sciences

Asymptotically Exact Variational Bayes for High-Dimensional Probit Models

Prof. Daniele Durante, Department of Decision Sciences, Bocconi University, Italy

Nov 27, 15:30 - 16:30

B1 L4 R4102

Bayesian approach to inference decision sciences

Abstract There are several Bayesian models where the posterior density is not available in a closed and tractable form. In these situations, Markov chain Monte Carlo algorithms and approximate methods, such as variational Bayes and expectation-propagation, provide common solutions to perform posterior inference. However, in high-dimensional studies, with large or even huge p, these approaches still face open problems in terms of scalability and quality of the posterior approximation. Notably, such issues also arise in basic predictor-dependent models for binary data which appear in a wide

Yulian Wu

Ph.D. Student, Computer Science

machine learning privacy Trustworthy AI decision making decision sciences privacy-preserving AI trustworthy machine learning Robustness Reinforcement Learning quantum machine learning bioinformatics direct preference optimization

Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).