1 citations · 2 across the 5 of their papers we have counts for
5 papers
Distribution Learning with Valid Outputs Beyond the Worst-Case
Nick Rittler, Kamalika Chaudhuri
Generative models at times produce "invalid" outputs, such as images with generation artifacts and unnatural sounds. Validity-constrained distribution learning attempts to address…
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training dist…
Agnostic Multi-Group Active Learning
Nick Rittler, Kamalika Chaudhuri
Inspired by the problem of improving classification accuracy on rare or hard subsets of a population, there has been recent interest in models of learning where the goal is to gene…
A Two-Stage Active Learning Algorithm for -Nearest Neighbors
Nick Rittler, Kamalika Chaudhuri
-nearest neighbor classification is a popular non-parametric method because of desirable properties like automatic adaption to distributional scale changes. Unfortunately, it ha…
A Deep Learning Approach to Probabilistic Forecasting of Weather
Nick Rittler, Carlo Graziani, Jiali Wang +1
We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information t…