2 citations · 3 across the 6 of their papers we have counts for
9 papers
An Algorithmic Upper Bound for Permanents via a Permanental Schur Inequality
Aditi Laddha, Madhusudhan Reddy Pittu
Computing the permanent of a non-negative matrix is a computationally challenging, \#P-complete problem with wide-ranging applications. We introduce a novel permanental analogue of…
Maximizing the Minimum Eigenvalue in Constant Dimension
Adam Brown, Aditi Laddha, Mohit Singh
In an instance of the minimum eigenvalue problem, we are given a collection of vectors , and the goal is to pick a subset $B\subseteq [n…
Approximation Algorithms for the Weighted Nash Social Welfare via Convex and Non-Convex Programs
Adam Brown, Aditi Laddha, Madhusudhan Reddy Pittu +1
In an instance of the weighted Nash Social Welfare problem, we are given a set of indivisible items, , and agents, , where each agent $i \in \math…
Efficient Determinant Maximization for All Matroids
Adam Brown, Aditi Laddha, Madhusudhan Pittu +1
Determinant maximization provides an elegant generalization of problems in many areas, including convex geometry, statistics, machine learning, fair allocation of goods, and networ…
Determinant Maximization via Matroid Intersection Algorithms
Adam Brown, Aditi Laddha, Madhusudhan Pittu +2
Determinant maximization problem gives a general framework that models problems arising in as diverse fields as statistics \cite{pukelsheim2006optimal}, convex geometry \cite{Khach…
A Unified Approach to Discrepancy Minimization
Nikhil Bansal, Aditi Laddha, Santosh S. Vempala
We study a unified approach and algorithm for constructive discrepancy minimization based on a stochastic process. By varying the parameters of the process, one can recover various…
Convergence of Gibbs Sampling: Coordinate Hit-and-Run Mixes Fast
Aditi Laddha, Santosh Vempala
The Gibbs Sampler is a general method for sampling high-dimensional distributions, dating back to Turchin, 1971. In each step of the Gibbs Sampler, we pick a random coordinate and…
Reducing Isotropy and Volume to KLS: Faster Rounding and Volume Algorithms
He Jia, Aditi Laddha, Yin Tat Lee +1
We show that the volume of a convex body in in the general membership oracle model can be computed to within relative error using $\widetilde{O}(n^{3…
Strong Self-Concordance and Sampling
Aditi Laddha, Yin Tat Lee, Santosh Vempala
Motivated by the Dikin walk, we develop aspects of an interior-point theory for sampling in high dimension. Specifically, we introduce a symmetric parameter and the notion of stron…