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20202026
most citedOn Approximating Total Variation Distance

16 citations · 17 across the 8 of their papers we have counts for

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cs.DS2026

Approximating the Trace Distance Between Product Quantum States

Kun He, Dimitrios Myrisiotis, Junhong Nie +1

We study the trace distance \[D_{\mathrm{tr}}(ρ,σ) =\frac12\|ρ-σ\|_1, ρ=\bigotimes_{i=1}^nρ_i,\quad σ=\bigotimes_{i=1}^nσ_i, \] when the two exponentially large states are specifie…

cs.DS2025

Algorithms and Hardness for Estimating Statistical Similarity

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel +3

We introduce and study the computational problem of determining statistical similarity between probability distributions. For distributions and over a finite sample space,…

cs.DS2024

Computational Explorations of Total Variation Distance

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel +3

We investigate some previously unexplored (or underexplored) computational aspects of total variation (TV) distance. First, we give a simple deterministic polynomial-time algorithm…

cs.DS2023★ 1 cited

Total Variation Distance Meets Probabilistic Inference

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel +3

In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-prese…

cs.DS2022★ 16 cited

On Approximating Total Variation Distance

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel +3

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV d…