8 citations · 10 across the 6 of their papers we have counts for
7 papers
DiRe: Diversity-promoting Regularization for Dataset Condensation
Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri
In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datase…
Online Adaptive Mahalanobis Distance Estimation
Lianke Qin, Aravind Reddy, Zhao Song
Mahalanobis metrics are widely used in machine learning in conjunction with methods like -nearest neighbors, -means clustering, and -medians clustering. Despite their impo…
Adaptive and Dynamic Multi-Resolution Hashing for Pairwise Summations
Lianke Qin, Aravind Reddy, Zhao Song +2
In this paper, we propose Adam-Hash: an adaptive and dynamic multi-resolution hashing data-structure for fast pairwise summation estimation. Given a data-set $X \subset \mathbb{R}^…
Dynamic Tensor Product Regression
Aravind Reddy, Zhao Song, Lichen Zhang
In this work, we initiate the study of \emph{Dynamic Tensor Product Regression}. One has matrices and a…
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
Aravind Reddy, Ryan A. Rossi, Zhao Song +6
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbi…
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances
Hunter Lang, Aravind Reddy, David Sontag +1
Several works have shown that perturbation stable instances of the MAP inference problem in Potts models can be solved exactly using a natural linear programming (LP) relaxation. H…
Improved Guarantees for k-means++ and k-means++ Parallel
Konstantin Makarychev, Aravind Reddy, Liren Shan
In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved ap…