28 citations · 325 across the 73 of their papers we have counts for
8 papers · 1 filter
The Power of Uniform Sampling for Coresets
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H. -C. Jiang +4
Motivated by practical generalizations of the classic -median and -means objectives, such as clustering with size constraints, fair clustering, and Wasserstein barycenter, we…
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang, Soledad Villar, Carey E. Priebe +4
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a…
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Jingfeng Wu, Difan Zou, Vladimir Braverman +2
We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional dist…
Pretrained Models for Multilingual Federated Learning
Orion Weller, Marc Marone, Vladimir Braverman +2
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous…
The White-Box Adversarial Data Stream Model
Miklos Ajtai, Vladimir Braverman, T. S. Jayram +4
We study streaming algorithms in the white-box adversarial model, where the stream is chosen adaptively by an adversary who observes the entire internal state of the algorithm at e…
New Coresets for Projective Clustering and Applications
Murad Tukan, Xuan Wu, Samson Zhou +2
-projective clustering is the natural generalization of the family of -clustering and -subspace clustering problems. Given a set of points in , the g…