1 citations · 1 across the 4 of their papers we have counts for
10 papers
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
Amrith Setlur, Oscar Li, Virginia Smith
We categorize meta-learning evaluation into two settings: [ID], in which the train and test tasks are sampled from the same underlying tas…
Is Support Set Diversity Necessary for Meta-Learning?
Amrith Setlur, Oscar Li, Virginia Smith
Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks. For classification problems…
Explaining The Efficacy of Counterfactually Augmented Data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy +1
In attempts to produce ML models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in…
Robust Handwriting Recognition with Limited and Noisy Data
Hai Pham, Amrith Setlur, Saket Dingliwal +7
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that…
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations
Amrith Setlur, Barnabas Poczos, Alan W Black
This paper extends recent work on nonlinear Independent Component Analysis (ICA) by introducing a theoretical framework for nonlinear Independent Subspace Analysis (ISA) in the pre…
Covariate Distribution Aware Meta-learning
Amrith Setlur, Saket Dingliwal, Barnabas Poczos
Meta-learning has proven to be successful for few-shot learning across the regression, classification, and reinforcement learning paradigms. Recent approaches have adopted Bayesian…