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researcher

Aounon Kumar

Harvard University

14 papers hereh-index 131.5k citations21 works total

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

author position
  • first author9
  • middle author5

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CL2
  • cs.CV2
  • cs.AI1
  • cs.DS1
  • cs.IR1
affiliations
  • Harvard University
Homepage
same name
  • Aounon Kumar — 5 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
20182026
most citedCan AI-Generated Text be Reliably Detected?

163 citations · 256 across the 13 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023★ 4 cited

Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks

Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei +4

In light of recent advancements in generative AI models, it has become essential to distinguish genuine content from AI-generated one to prevent the malicious usage of fake materia…

cs.CL2023★ 25 cited

Certifying LLM Safety against Adversarial Prompting

Aounon Kumar, Chirag Agarwal, Suraj Srinivas +3

Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt to bypass the safety guardrails of an LLM and cause it to produce ha…

cs.LG2023

Provable Robustness for Streaming Models with a Sliding Window

Aounon Kumar, Vinu Sankar Sadasivan, Soheil Feizi

The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to b…

cs.CL2023★ 163 cited

Can AI-Generated Text be Reliably Detected?

Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian +2

Large Language Models (LLMs) perform impressively well in various applications. However, the potential for misuse of these models in activities such as plagiarism, generating fake…

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