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08/13/2023
MDB: Interactively Querying Datasets and Models
As models are trained and deployed, developers need to be able to system...
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05/25/2023
Rectifying Group Irregularities in Explanations for Distribution Shift
It is well-known that real-world changes constituting distribution shift...
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05/05/2023
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
Pre-trained large language models (LMs) struggle to perform logical reas...
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04/10/2023
Scallop: A Language for Neurosymbolic Programming
We present Scallop, a language which combines the benefits of deep learn...
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03/02/2023
Do Machine Learning Models Learn Common Sense?
Machine learning models can make basic errors that are easily hidden wit...
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02/09/2023
Learning to Select Pivotal Samples for Meta Re-weighting
Sample re-weighting strategies provide a promising mechanism to deal wit...
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05/17/2021
DeepMerge: Learning to Merge Programs
Program merging is ubiquitous in modern software development. Although c...
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06/01/2019
Synthesizing Datalog Programs using Numerical Relaxation
The problem of learning logical rules from examples arises in diverse fi...
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02/25/2019
PolyDroid: Learning-Driven Specialization of Mobile Applications
The increasing prevalence of mobile apps has led to a proliferation of r...
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01/24/2019
Learning Neurosymbolic Generative Models via Program Synthesis
Significant strides have been made toward designing better generative mo...
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09/30/2010
Mantis: Predicting System Performance through Program Analysis and Modeling
We present Mantis, a new framework that automatically predicts program p...
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09/16/2010