Categories: FAANG

Agnostically Learning Single-Index Models using Omnipredictors

We give the first result for agnostically learning Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations. All prior work either held only in the realizable setting or required the activation to be known. Moreover, we only require the marginal to have bounded second moments, whereas all prior work required stronger distributional assumptions (such as anticoncentration or boundedness). Our algorithm is based on recent work by [GHK+23] on omniprediction using predictors satisfying calibrated multiaccuracy. Our analysis is simple and relies on the relationship between…

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping

While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to gradient heterogeneity across layers, unlike…

September 30, 2025

In "FAANG"

FORML: Learning to Reweight Data for Fairness

Machine learning models are trained to minimize the mean loss for a single metric, and thus typically do not consider fairness and robustness. Neglecting such metrics in training can make these models prone to fairness violations when training data are imbalanced or test distributions differ. This work introduces Fairness Optimized…

September 3, 2022

In "FAANG"

Transfer Learning in Scalable Graph Neural Network for Improved Physical Simulation

In recent years, graph neural network (GNN) based models showed promising results in simulating complex physical systems. However, training dedicated graph network simulator can be costly, as most models are confined to fully supervised training. Extensive data generated from traditional simulators is required to train the model. It remained unexplored…

February 15, 2025

In "FAANG"

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