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S. Hassani

4 papers hereh-index 11687 citations18 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • math.OC1
  • stat.ML1
same name
  • S. Hassani — 209 papers
  • S. Hassani — 13 papers, h 19
  • S. Hassani — 8 papers
  • S. Hassani — 1 paper, h 1
  • S. Hassani — 1 paper
  • S. Hassani — 1 paper

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

most citedStochastic Submodular Maximization: The Case of Coverage Functions

12 citations · 21 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2018

Discrete Sampling using Semigradient-based Product Mixtures

Alkis Gotovos, Hamed Hassani, Andreas Krause +1

We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in m…

math.OC2017★ 9 cited

Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

In this paper, we study the problem of \textit{constrained} and \textit{stochastic} continuous submodular maximization. Even though the objective function is not concave (nor conve…

cs.LG2017★ 12 cited

Stochastic Submodular Maximization: The Case of Coverage Functions

Mohammad Reza Karimi, Mario Lucic, Hamed Hassani +1

Stochastic optimization of continuous objectives is at the heart of modern machine learning. However, many important problems are of discrete nature and often involve submodular ob…

stat.ML2017

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Olivier Bachem, Mario Lucic, S. Hamed Hassani +1

Uniform deviation bounds limit the difference between a model's expected loss and its loss on an empirical sample uniformly for all models in a learning problem. As such, they are…

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