2 citations · 2 across the 2 of their papers we have counts for
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Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization
Shivam Handa, Martin Rinard
We present a new synthesis algorithm to solve program synthesis over noisy datasets, i.e., data that may contain incorrect/corrupted input-output examples. Our algorithm uses an ab…
Program Synthesis Over Noisy Data with Guarantees
Shivam Handa, Martin Rinard
We explore and formalize the task of synthesizing programs over noisy data, i.e., data that may contain corrupted input-output examples. By formalizing the concept of a Noise Sourc…
An Order-Aware Dataflow Model for Parallel Unix Pipelines
Shivam Handa, Konstantinos Kallas, Nikos Vasilakis +1
We present a dataflow model for modelling parallel Unix shell pipelines. To accurately capture the semantics of complex Unix pipelines, the dataflow model is order-aware, i.e., the…
Inductive Program Synthesis Over Noisy Data
Shivam Handa, Martin Rinard
We present a new framework and associated synthesis algorithms for program synthesis over noisy data, i.e., data that may contain incorrect/corrupted input-output examples. This fr…
Compositional Inference Metaprogramming with Convergence Guarantees
Shivam Handa, Vikash Mansinghka, Martin Rinard
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of…