Categories: FAANG

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

*= Equal Contributions
Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from users with user contributing data samples from a -dimensional distribution with mean . Our goal is to recover the linear subspace shared by using the data points from all users, where every data point from user is formed by adding an independent…

Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement

The growing demand for personalized and private on-device applications highlights the importance of source-free unsupervised domain adaptation (SFDA) methods, especially for time-series data, where individual differences produce large domain shifts. As sensor-embedded mobile devices become ubiquitous, optimizing SFDA methods for parameter utilization and data-sample efficiency in time-series contexts becomes crucial.…

October 22, 2024

In "FAANG"

Self-Supervised Learning with Gaussian Processes

Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear classification, etc. To ensure smoothness of the representation space,…

January 31, 2026

In "FAANG"

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures

his paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a `reasoning' function acts on a string of digits to produce the label. More generally, the paper considers the learning of logical functions with gradient descent (GD) on neural networks. It is first shown that in order…

October 25, 2022

In "FAANG"

AI Generated Robotic Content