14 citations · 58 across the 43 of their papers we have counts for
4 papers · 1 filter
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness
Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer learning 'simple' features over more 'complex' ones, even when the latter may be more informati…
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
Deqing Fu, Tian-Qi Chen, Robin Jia +1
Transformers excel at in-context learning (ICL) -- learning from demonstrations without parameter updates -- but how they do so remains a mystery. Recent work suggests that Transfo…
Regularization and Optimal Multiclass Learning
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
The quintessential learning algorithm of empirical risk minimization (ERM) is known to fail in various settings for which uniform convergence does not characterize learning. It is…
Fairness in Matching under Uncertainty
Siddartha Devic, David Kempe, Vatsal Sharan +1
The prevalence and importance of algorithmic two-sided marketplaces has drawn attention to the issue of fairness in such settings. Algorithmic decisions are used in assigning stude…