96 citations · 280 across the 33 of their papers we have counts for
10 papers · 1 filter
Matched-Pair Experimental Design with Active Learning
Weizhi Li, Gautam Dasarathy, Visar Berisha
Matched-pair experimental designs aim to detect treatment effects by pairing participants and comparing within-pair outcome differences. In many situations, the overall effect size…
Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health
Pavel Dolin, Weizhi Li, Gautam Dasarathy +1
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled…
Advanced Tutorial: Label-Efficient Two-Sample Tests
Weizhi Li, Visar Berisha, Gautam Dasarathy
Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-sample test, which evaluates whet…
Unraveling overoptimism and publication bias in ML-driven science
Pouria Saidi, Gautam Dasarathy, Visar Berisha
Machine Learning (ML) is increasingly used across many disciplines with impressive reported results. However, recent studies suggest published performance of ML models are often ov…
Smoothly Giving up: Robustness for Simple Models
Tyler Sypherd, Nathan Stromberg, Richard Nock +2
There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algori…
Active Sequential Two-Sample Testing
Weizhi Li, Prad Kadambi, Pouria Saidi +3
A two-sample hypothesis test is a statistical procedure used to determine whether the distributions generating two samples are identical. We consider the two-sample testing problem…
A label-efficient two-sample test
Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy +1
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consid…
Finding the Homology of Decision Boundaries with Active Learning
Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy +1
Accurately and efficiently characterizing the decision boundary of classifiers is important for problems related to model selection and meta-learning. Inspired by topological data…
Regularization via Structural Label Smoothing
Weizhi Li, Gautam Dasarathy, Visar Berisha
Regularization is an effective way to promote the generalization performance of machine learning models. In this paper, we focus on label smoothing, a form of output distribution r…
Reducing the Model Order of Deep Neural Networks Using Information Theory
Ming Tu, Visar Berisha, Yu Cao +1
Deep neural networks are typically represented by a much larger number of parameters than shallow models, making them prohibitive for small footprint devices. Recent research shows…