10 citations · 15 across the 4 of their papers we have counts for
4 papers
Verification of ML Systems via Reparameterization
Jean-Baptiste Tristan, Joseph Tassarotti, Koundinya Vajjha +2
As machine learning is increasingly used in essential systems, it is important to reduce or eliminate the incidence of serious bugs. A growing body of research has developed machin…
A Formal Proof of PAC Learnability for Decision Stumps
Joseph Tassarotti, Koundinya Vajjha, Anindya Banerjee +1
We present a formal proof in Lean of probably approximately correct (PAC) learnability of the concept class of decision stumps. This classic result in machine learning theory deriv…
Sketching for Latent Dirichlet-Categorical Models
Joseph Tassarotti, Jean-Baptiste Tristan, Michael Wick
Recent work has explored transforming data sets into smaller, approximate summaries in order to scale Bayesian inference. We examine a related problem in which the parameters of a…
Augur: a Modeling Language for Data-Parallel Probabilistic Inference
Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti +3
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to spec…