Search Results for author: Jonathan Kuck

Found 6 papers, 3 papers with code

Equivariant Neural Network for Factor Graphs

no code implementations29 Sep 2021 Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon

Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.

Inductive Bias

Sample-Efficient Safety Assurances using Conformal Prediction

no code implementations28 Sep 2021 Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.

Conformal Prediction Robotic Grasping

Privacy Preserving Recalibration under Domain Shift

no code implementations21 Aug 2020 Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese

In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.

Privacy Preserving

Approximating the Permanent by Sampling from Adaptive Partitions

1 code implementation NeurIPS 2019 Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics.

Approximate Inference via Weighted Rademacher Complexity

1 code implementation27 Jan 2018 Jonathan Kuck, Ashish Sabharwal, Stefano Ermon

Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size.

LEMMA

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