16 citations · 32 across the 6 of their papers we have counts for
7 papers
Diagnostic Runtime Monitoring with Martingales
Ali Hindy, Rachel Luo, Somrita Banerjee +3
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution…
Equivariant Neural Network for Factor Graphs
Fan-Yun Sun, Jonathan Kuck, Hao Tang +1
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution. An algorithm that performs inference on a factor gra…
Sample-Efficient Safety Assurances using Conformal Prediction
Rachel Luo, Shengjia Zhao, Jonathan Kuck +4
When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an un…
Privacy Preserving Recalibration under Domain Shift
Rachel Luo, Shengjia Zhao, Jiaming Song +3
Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recal…
Belief Propagation Neural Networks
Jonathan Kuck, Shuvam Chakraborty, Hao Tang +4
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still l…
Approximating the Permanent by Sampling from Adaptive Partitions
Jonathan Kuck, Tri Dao, Hamid Rezatofighi +2
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is…