
CounterexampleGuided Learning of Monotonic Neural Networks
The widespread adoption of deep learning is often attributed to its auto...
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Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of re...
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DataDriven Inference of Representation Invariants
A representation invariant is a property that holds of all values of abs...
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Overfitting in Synthesis: Theory and Practice (Extended Version)
In syntaxguided synthesis (SyGuS), a synthesizer's goal is to automatic...
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Overfitting in Synthesis: Theory and Practice (Extender Version)
In syntaxguided synthesis (SyGuS), a synthesizer's goal is to automatic...
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Overfitting in Synthesis: Theory and Practice
In syntaxguided synthesis (SyGuS), a synthesizer's goal is to automatic...
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Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for...
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Generating and Sampling Orbits for Lifted Probabilistic Inference
Lifted inference scales to large probability models by exploiting symmet...
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DataDriven Loop Invariant Inference with Automatic Feature Synthesis
We present LoopInvGen, a tool for generating loop invariants that can pr...
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Probabilistic Program Abstractions
Abstraction is a fundamental tool for reasoning about complex systems. P...
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Todd Millstein
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