13 citations · 26 across the 11 of their papers we have counts for
5 papers · 1 filter
The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations
Tyler LaBonte, John C. Hill, Xinchen Zhang +2
Modern machine learning models are prone to over-reliance on spurious correlations, which can often lead to poor performance on minority groups. In this paper, we identify surprisi…
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares pr…
Sharp analysis of out-of-distribution error for "importance-weighted" estimators in the overparameterized regime
Kuo-Wei Lai, Vidya Muthukumar
Overparameterized models that achieve zero training error are observed to generalize well on average, but degrade in performance when faced with data that is under-represented in t…
Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence
Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
We study the problem of estimating the stationary mass -- also called the unigram mass -- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This pr…
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Chiraag Kaushik, Ran Liu, Chi-Heng Lin +5
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is wide…