2 citations · 3 across the 5 of their papers we have counts for
5 papers
Coupled Training with Privileged Information and Unlabeled Data
Jiahao Shi, Omar Hagrass, Jason M. Klusowski
In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model i…
Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies
Sijin Chen, Omar Hagrass, Jason M. Klusowski
Decoding strategies play a pivotal role in text generation for modern language models, yet a puzzling gap divides theory and practice. Surprisingly, strategies that should intuitiv…
Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
Omar Hagrass, Bharath Sriperumbudur, Krishnakumar Balasubramanian
We explore the minimax optimality of goodness-of-fit tests on general domains using the kernelized Stein discrepancy (KSD). The KSD framework offers a flexible approach for goodnes…
Spectral Regularized Kernel Goodness-of-Fit Tests
Omar Hagrass, Bharath K. Sriperumbudur, Bing Li
Maximum mean discrepancy (MMD) has enjoyed a lot of success in many machine learning and statistical applications, including non-parametric hypothesis testing, because of its abili…
Spectral Regularized Kernel Two-Sample Tests
Omar Hagrass, Bharath K. Sriperumbudur, Bing Li
Over the last decade, an approach that has gained a lot of popularity to tackle nonparametric testing problems on general (i.e., non-Euclidean) domains is based on the notion of re…