# Announcing WOLA’21 (Workshop on Local Algorithms)

The fifth WOLA (Workshop on Local Algorithms) will be virtual, and take place June 14-15. Registration is free, but required: please fill this form by June 10th to attend.

Local algorithms — that is, algorithms that compute and make decisions on parts of the output considering only a portion of the input — have been studied in a number of areas in theoretical computer science and mathematics. Some of the related areas include sublinear-time algorithms, distributed algorithms, streaming algorithms, (massively) parallel algorithms, inference in large networks, and graphical models. These communities have similar goals but a variety of approaches, techniques, and methods. This workshop is aimed at fostering dialogue and cross-pollination of ideas between the various communities.

This year, the workshop will include:

• A poster session: Please submit your poster proposal (title and abstract) at by May 26th. Everyone is invited to contribute. This session will take place on gather.town.
• Invited long talks: the tentative schedule is available, and features talks by James Aspnes, Jelani Nelson, Elaine Shi, Christian Sohler, Uri Stemmer, and Mary Wootters.
• Junior-Senior social meetings
• An AMA (Ask Me Anything) session, moderated by Merav Parter
• A Slack channel
• An Open Problems session

The Program Committee of WOLA 2021 is comprised of:

• Venkatesan Guruswami (CMU)
• Elchanan Mossel (MIT)
• Merav Parter (Weizmann Institute of Science)
• Sofya Raskhodnikova (chair) (Boston University)
• Gregory Valiant (Stanford)

and the organizing committee:

• Sebastian Brandt (ETH)
• Yannic Maus (Technion)
• Slobodan Mitrović (MIT)

For more detail, see the website; to stay up to date with the latest announcements concerning WOLA, join our mailing list!

# News for April 2021

A somewhat “sublinear” month of April, as far as property testing is concerned, with only one paper. (We may have missed some; if so, please let us know in the comments!)

Graph Streaming Lower Bounds for Parameter Estimation and Property Testing via a Streaming XOR Lemma, by Sepehr Assadi and Vishvajeet N (arXiv). This paper establishes space vs. pass trade-offs lower bounds for streaming algorithms, for a variety of graph tasks: that is, of the sort “any $$m$$-pass-streaming algorithm for task $$\mathcal{T}$$ must use memory at least $$f(m)$$.” The tasks considered include graph property estimation (size of the maximum matching, of the max cut, of the weight of the MST) and property testing for sparse graphs (connectivity, bipartiteness, and cycle-freeness). The authors obtained exponentially improved lower bounds for those, via reductions to a relatively standard problem, (noisy) gap cycle counting, for which they establish their main lower bound. As a key component of their proof, they prove a general direct product result (XOR lemma) for the streaming setting, showing that the advantage for solving the XOR of $$\ell$$ copies of a streaming predicate $$f$$ decreases exponentially with $$\ell$$.