5 papers
Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
Conlan Olson, Linjun Zhang, Zhun Deng +1
Individual fairness, the notion that "similar individuals should be treated similarly," provides a strong and flexible fairness guarantee for algorithmic decision makers. However,…
Generalization error of min-norm interpolators in transfer learning
Yanke Song, Kenneth Gu, Sohom Bhattacharya +1
This paper establishes the generalization error of pooled min--norm interpolation in transfer learning, where data from diverse distributions are available. Min-norm interp…
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
Radu Lecoiu, Debarghya Mukherjee, Pragya Sur
Self-distillation has emerged as a promising technique for improving model performance in modern machine learning systems. We develop the statistical foundations of self-distillati…
Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
Anvit Garg, Sohom Bhattacharya, Pragya Sur
Model collapse occurs when generative models degrade after repeatedly training on their own synthetic outputs. We study this effect in overparameterized linear regression in a sett…
Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators
Longlin Wang, Yanke Song, Kuanhao Jiang +1
Approximate Message Passing (AMP) algorithms enable precise characterization of certain classes of random objects in the high-dimensional limit, and have found widespread applicati…