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Jonathan Niles-Weed

13 papers hereh-index 8897 citations13 works total

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

author position
  • first author2
  • middle author5
  • last author6

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • math.ST4
  • math.CO2
  • stat.ML2
  • cs.DS1
same name
  • Jonathan Niles-Weed — 11 papers, h 7
  • Jonathan Niles-Weed — 10 papers, h 9
  • Jonathan Niles-Weed — 6 papers, h 3
  • Jonathan Niles-Weed — 4 papers, h 2
  • Jonathan Niles-Weed — 4 papers, h 3
  • Jonathan Niles-Weed — 4 papers, h 3

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
20172025
most citedEarly-Learning Regularization Prevents Memorization of Noisy Labels

261 citations · 347 across the 12 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

math.CO2022★ 1 cited

A second moment proof of the spread lemma

Elchanan Mossel, Jonathan Niles-Weed, Nike Sun +1

This note concerns a well-known result which we term the ``spread lemma,'' which establishes the existence (with high probability) of a desired structure in a random set. The sprea…

math.CO2022

On the Second Kahn--Kalai Conjecture

Elchanan Mossel, Jonathan Niles-Weed, Nike Sun +1

For any given graph H, we are interested in pcrit​(H), the minimal p such that the Erdős-Rényi graph G(n,p) contains a copy of H with probability at least 1/2.…

cs.LG2022

The Consistency of Adversarial Training for Binary Classification

Natalie S. Frank, Jonathan Niles-Weed

Robustness to adversarial perturbations is of paramount concern in modern machine learning. One of the state-of-the-art methods for training robust classifiers is adversarial train…

cs.LG2022★ 3 cited

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification

Natalie S. Frank, Jonathan Niles-Weed

Adversarial training is one of the most popular methods for training methods robust to adversarial attacks, however, it is not well-understood from a theoretical perspective. We pr…

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