23 citations · 48 across the 6 of their papers we have counts for
6 papers
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors
Ying Zhou, Ben He, Le Sun
With the launch of ChatGPT, large language models (LLMs) have attracted global attention. In the realm of article writing, LLMs have witnessed extensive utilization, giving rise to…
On the Reliability of Watermarks for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +7
As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effecti…
An error term in the Central Limit Theorem for sums of discrete random variables
Dmitry Dolgopyat, Kasun Fernando
We consider sums of independent identically distributed random variables whose distributions have atoms. Such distributions never admit an Edgeworth expansion of order bu…
Limit Theorems for a class of unbounded observables with an application to "Sampling the Lindelöf hypothesis"
Kasun Fernando, Tanja I. Schindler
We prove the Central Limit Theorem (CLT), the first order Edgeworth Expansion and a Mixing Local Central Limit Theorem (MLCLT) for Birkhoff sums of a class of unbounded heavily osc…
Robust normalizing flows using Bernstein-type polynomials
Sameera Ramasinghe, Kasun Fernando, Salman Khan +1
Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e.g., instrumentation errors, or added random noise. Since flow m…
Expansions in the local and the central limit theorems for dynamical systems
Kasun Fernando, Françoise Pène
We study higher order expansions both in the Berry-Esséen estimate (Edgeworth expansions) and in the local limit theorems for Birkhoff sums of chaotic probability preserving dynami…