Todd Millstein

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Counterexample-Guided Learning of Monotonic Neural Networks

The widespread adoption of deep learning is often attributed to its auto...
06/16/2020 ∙ by Aishwarya Sivaraman, et al. ∙ 7 ∙ share

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Scaling Exact Inference for Discrete Probabilistic Programs

Probabilistic programming languages (PPLs) are an expressive means of re...
05/18/2020 ∙ by Steven Holtzen, et al. ∙ 0 ∙ share

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Data-Driven Inference of Representation Invariants

A representation invariant is a property that holds of all values of abs...
03/26/2020 ∙ by Anders Miltner, et al. ∙ 0 ∙ share

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Overfitting in Synthesis: Theory and Practice (Extended Version)

In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatic...
05/17/2019 ∙ by Saswat Padhi, et al. ∙ 0 ∙ share

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Overfitting in Synthesis: Theory and Practice (Extender Version)

In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatic...
05/17/2019 ∙ by Saswat Padhi, et al. ∙ 0 ∙ share

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Overfitting in Synthesis: Theory and Practice

In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatic...
05/17/2019 ∙ by Saswat Padhi, et al. ∙ 0 ∙ share

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Symbolic Exact Inference for Discrete Probabilistic Programs

The computational burden of probabilistic inference remains a hurdle for...
04/03/2019 ∙ by Steven Holtzen, et al. ∙ 0 ∙ share

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Generating and Sampling Orbits for Lifted Probabilistic Inference

Lifted inference scales to large probability models by exploiting symmet...
03/12/2019 ∙ by Steven Holtzen, et al. ∙ 8 ∙ share

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Data-Driven Loop Invariant Inference with Automatic Feature Synthesis

We present LoopInvGen, a tool for generating loop invariants that can pr...
07/07/2017 ∙ by Saswat Padhi, et al. ∙ 0 ∙ share

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Probabilistic Program Abstractions

Abstraction is a fundamental tool for reasoning about complex systems. P...
05/28/2017 ∙ by Steven Holtzen, et al. ∙ 0 ∙ share

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Todd Millstein

Featured Co-authors

Guy Van den Broeck

Aditya Nori

Rahul Sharma

Saswat Padhi

Golnoosh Farnadi

David Walker

Steven Holtzen

Anders Miltner

Aishwarya Sivaraman

Counterexample-Guided Learning of Monotonic Neural Networks

Scaling Exact Inference for Discrete Probabilistic Programs

Data-Driven Inference of Representation Invariants

Overfitting in Synthesis: Theory and Practice (Extended Version)

Overfitting in Synthesis: Theory and Practice (Extender Version)

Overfitting in Synthesis: Theory and Practice

Symbolic Exact Inference for Discrete Probabilistic Programs

Generating and Sampling Orbits for Lifted Probabilistic Inference

Data-Driven Loop Invariant Inference with Automatic Feature Synthesis

Probabilistic Program Abstractions