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Found 6 papers, 0 papers with code
Verification of ML Systems via Reparameterization
Proof assistants can be used to formally verify machine learning systems by constructing machine checked proofs of correctness that rule out such bugs.
A Formal Proof of PAC Learnability for Decision Stumps
We present a formal proof in Lean of probably approximately correct (PAC) learnability of the concept class of decision stumps.
Scaling Hierarchical Coreference with Homomorphic Compression
In this case, we need the representation to be a homomorphism so that the hash of unions and differences of sets can be computed directly from the hashes of operands.
Sketching for Latent Dirichlet-Categorical Models
We examine a related problem in which the parameters of a Bayesian model are very large and expensive to store in memory, and propose more compact representations of parameter values that can be used during inference.
Augur: Data-Parallel Probabilistic Modeling
We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.
Augur: a Modeling Language for Data-Parallel Probabilistic Inference
In this paper, we present a probabilistic programming language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs.
