no code implementations • 25 Aug 2023 • Alexander Tsvetkov, Alon Kipnis

We propose an unsupervised method to extract keywords and keyphrases from texts based on a pre-trained language model (LM) and Shannon's information maximization.

no code implementations • 24 Aug 2023 • Alon Kipnis

We propose a method to determine whether a given article was entirely written by a generative language model versus an alternative situation in which the article includes some significant edits by a different author, possibly a human.

no code implementations • 29 May 2023 • Alon Kipnis

When the expected number of samples $n$ and number of categories $N$ go to infinity while $\epsilon$ is small, the minimax risk asymptotes to $2\Phi(-n N^{2-2/p} \epsilon^2/\sqrt{8N})$; $\Phi(x)$ is the normal CDF.

no code implementations • 9 Jan 2020 • Alon Kipnis, Galen Reeves

We show that the Wasserstein distance between a bitrate-$R$ compressed version of $X$ and its observation under an AWGN-channel of signal-to-noise ratio $2^{2R}-1$ is sub-linear in the problem dimension.

2 code implementations • 30 Oct 2019 • Alon Kipnis

We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known.

no code implementations • 10 Jan 2019 • Alon Kipnis, John C. Duchi

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator.

no code implementations • 2 Aug 2017 • Alon Kipnis, John C. Duchi

We study the squared error risk in this estimation as a function of the number of samples and one-bit measurements $n$.

Statistics Theory Statistics Theory

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