16 citations · 21 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
Approximate Inference via Weighted Rademacher Complexity
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. We leverage this observation and introduce…