2 citations · 2 across the 5 of their papers we have counts for
7 papers
Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
Navodita Sharma, Abhishek Shetty, Max Simchowitz +1
Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as reg…
Attention Is Not All You Need for Diffraction
Elizabeth J. Baggett, Edward G. Friedman, Abhishek Shetty +5
Determining crystal symmetry from powder X-ray diffraction is a central problem in materials characterization, yet multiple space groups can produce indistinguishable patterns, mak…
Learning with Monotone Adversarial Corruptions
Kasper Green Larsen, Chirag Pabbaraju, Abhishek Shetty
We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model. In this m…
Dimension-Free Correlated Sampling for the Hypersimplex
Joseph, Naor, Nitya Raju +4
Sampling from multiple distributions so as to maximize overlap has been studied by statisticians since the 1950s. Since the 2000s, such correlated sampling from the probability sim…
Low-Rank Thinning
Annabelle Michael Carrell, Albert Gong, Abhishek Shetty +2
The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match t…
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos +1
We study the problem of learning under arbitrary distribution shift, where the learner is trained on a labeled set from one distribution but evaluated on a different, potentially a…
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference
Don Kurian Dennis, Abhishek Shetty, Anish Sevekari +2
We study the problem of progressive ensemble distillation: Given a large, pretrained teacher model , we seek to decompose the model into smaller, low-inference cost student mode…