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A. Majumdar

73 papers hereh-index 416.3k citations408 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author24
  • first author3
  • middle author8
  • last author38

Across the 73 of 73 papers where every author was matched, so the position is known.

fields
  • cs.LG17
  • cs.CV13
  • eess.SP8
  • eess.IV7
  • cs.AI5
  • cs.CC5
same name
  • A. Majumdar — 105 papers, h 54
  • A. Majumdar — 50 papers, h 19
  • A. Majumdar — 14 papers, h 6
  • A. Majumdar — 13 papers, h 10
  • A. Majumdar — 10 papers, h 110
  • A. Majumdar — 7 papers, h 31

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132026
most citedHierarchical Representation Learning for Kinship Verification

81 citations · 168 across the 67 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.GT2025

Bayesian Recovery for Probabilistic Coalition Structures

Angshul Majumdar

Probabilistic Coalition Structure Generation (PCSG) is NP-hard and can be recast as an l0​-type sparse recovery problem by representing coalition structures as sparse coefficient…

cs.LG2025

Dictionary-Transform Generative Adversarial Networks

Angshul Majumdar

Generative adversarial networks (GANs) are widely used for distribution learning, yet their classical formulations remain theoretically fragile, with ill-posed objectives, unstable…

cs.GT2025

Near-Optimal Coalition Structures in Polynomial Time

Angshul Majumdar

We study the classical coalition structure generation (CSG) problem and compare the anytime behavior of three algorithmic paradigms: dynamic programming (DP), MILP branch-and-bound…

cs.LG2025

A Unified Matrix Factorization Framework for Classical and Robust Clustering

Angshul Majumdar

This paper presents a unified matrix factorization framework for classical and robust clustering. We begin by revisiting the well-known equivalence between crisp k-means clustering…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.