Formal Guarantees in Machine Learning, Statistics and Optimization

Abstract

This talk reviews formal guarantees in classical supervised machine learning, as well as a posteriori statistical guarantees, a priori statistical guarantees, and guarantees for optimization.

Formal Guarantees in Machine Learning, Statistics and Optimization

Abstract

Documents and media

Speaker(s)

Francis Bach

Events

Introduction and Welcome

Why Statistical Thinking is Transforming Programming Language Research

Relational Proofs of Probabilistic Programs and Applications to Cryptography

Probabilistic Programming for Sequential Monte Carlo?

Formally Verified Compilation of Probabilistic Programs

Program Analysis of Probabilistic Programs

Probabilistic and Differentiable Programming in Scientific Simulators

Towards Verified Stochastic Variational Inference for Probabilistic Programs

Social Science: From Prediction to Modeling to Understanding

Denotational Verification of Probabilistic Programming

Thinking Compositionally about Inference

Formal Guarantees in Machine Learning, Statistics and Optimization

Reactive Probabilistic Programming

See also