Bioacoustics and Machine Learning as Key Tools in Conservation @SimonsInstituteTOC

Simons Institute | Bioacoustics and Machine Learning as Key Tools in Conservation @SimonsInstituteTOC | Uploaded 2 months ago | Updated 22 hours ago
Kerry Dunleavy (Conservation Metrics)
https://simons.berkeley.edu/talks/kerry-dunleavy-conservation-metrics-2024-06-30
Decoding Communication in Nonhuman Species III (co-hosted with Project CETI)

$Hypothesis selection with computational constraints Maryam Aliakbarpour (Rice University) https://simons.berkeley.edu/talks/maryam-aliakbarpour-rice-university-2024-06-21 Extroverted Sublinear Algorithms Hypothesis selection is a fundamental problem in learning theory and statistics. Given a dataset and a finite set of candidate distributions, the goal is to select a distribution that matches the data as well as possible. More specifically, suppose we have sample access to an unknown distribution $P$ over a domain $mathcal{X}$. We assume that $P$ is well-approximated by one of $n$ distributions in a class (called hypotheses), denoted $mathcal{H} coloneqq {H_1, H_2, ldots, H_n}$. The goal is to design an algorithm that outputs a distribution $hat{H} in mathcal{H}$ whose total variation distance from $P$ is nearly minimal. In this work, we study the hypothesis selection problem under memory and time constraints. In particular, we discuss how to achieve a nearly optimal tradeoff between memory usage and sample complexity. Additionally, we explore methods to obtain optimal accuracy via algorithms with nearly optimal time complexity.$

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