no code implementations • 9 May 2023 • Yachuan Liu, Bohan Zhang, Qiaozhu Mei, Paramveer Dhillon
Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented groups due to the prevalence of spurious features.
no code implementations • 12 Feb 2021 • Paramveer Dhillon, Sinan Aral
In recent years, there has been significant interest in understanding users' online content consumption patterns.
no code implementations • 29 Oct 2020 • Jeremy Yang, Dean Eckles, Paramveer Dhillon, Sinan Aral
We apply our approach in two large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers with the aim of maximizing long-term revenue.
no code implementations • NeurIPS 2013 • Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).
no code implementations • NeurIPS 2013 • Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).
no code implementations • NeurIPS 2011 • Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks.