39 citations · 90 across the 39 of their papers we have counts for
21 papers · 1 filter
Spectral Perturbation Bounds for Low-Rank Approximation with Applications to Privacy
Phuc Tran, Nisheeth K. Vishnoi, Van H. Vu
A central challenge in machine learning is to understand how noise or measurement errors affect low-rank approximations, particularly in the spectral norm. This question is especia…
Perturbation Bounds for Low-Rank Inverse Approximations under Noise
Phuc Tran, Nisheeth K. Vishnoi
Low-rank pseudoinverses are widely used to approximate matrix inverses in scalable machine learning, optimization, and scientific computing. However, real-world matrices are often…
Coresets for Clustering Under Stochastic Noise
Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi +2
We study the problem of constructing coresets for -clustering when the input dataset is corrupted by stochastic noise drawn from a known distribution. In this setting, eval…
Efficient Diffusion Models for Symmetric Manifolds
Oren Mangoubi, Neil He, Nisheeth K. Vishnoi
We introduce a framework for designing efficient diffusion models for -dimensional symmetric-space Riemannian manifolds, including the torus, sphere, special orthogonal group an…
Maximizing Submodular Functions for Recommendation in the Presence of Biases
Anay Mehrotra, Nisheeth K. Vishnoi
Subset selection tasks, arise in recommendation systems and search engines and ask to select a subset of items that maximize the value for the user. The values of subsets often dis…
Fair Ranking with Noisy Protected Attributes
Anay Mehrotra, Nisheeth K. Vishnoi
The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information ret…