Sparsification for communication-efficient distributed symmetry-breaking @SimonsInstituteTOC

Simons Institute | Sparsification for communication-efficient distributed symmetry-breaking @SimonsInstituteTOC | Uploaded 1 week ago | Updated 20 hours ago
Yannic Maus (TU Graz)
https://simons.berkeley.edu/talks/yannic-maus-tu-graz-2024-08-01
Sublinear Graph Simplification

We will present several sparsification approaches beneficial for designing algorithms in the CONGEST model, with a particular focus on local symmetry-breaking problems such as graph coloring, ruling sets, and the Lovász Local Lemma.

Understanding Generalization from Pre-training Loss to Downstream Tasks

Circuit Minimization with QBF and SAT-Based Exact Synthesis

Sublinear time algorithms for better than 1/2 approximation algorithms for max-cut on expanders

Counterexample-Guided Inference of Modular Specifications

Machine learning models of differential gene expression

Computing a fixed point of contraction maps in polynomial queries

Bioacoustics and Machine Learning as Key Tools in Conservation

$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.$

Lessons from Texas, COVID-19, and the 737 Max: Efficiency vs. Resilience | Richard M. Karp...