not pictured: adversarially robust streaming via differential privacy. Now that I can correctly conjure up the right name each time, I hope that work can progress smoothly

Four papers, titled: - A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators - A Framework for Adversarially Robust Streaming Algorithms - Adversarially Robust Streaming via Dense--Sparse Trade-offs - Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters

Step one in understanding adversarially robust streaming: being able to remember which one of these papers is which.

Amazing! Yesterday I would have said that computing any graph statistics with node-level LDP would be practically impossible

The current state of professional translation is that you get sent a badly machine translated piece of technical writing, your agency says "you just have to proof this, so we won't pay full translation rates" and then you have to translate the whole thing again anyway

An introduction to local differential privacy protocols using block designs The design of protocols for local differential privacy (or LDP) has been a topic of considerable research interest in recent years. LDP protocols utilise the randomised encoding of outcomes of an expe...

Super cool expository paper on block designs and local differential privacy, showing a similar result to mine from last year but for a broader class of protocols ("pure" LDP functions and (r,λ)-designs)

arxiv.org/abs/2602.02744

Gradient optimization methods: the benefits of instability YouTube video by Sydney Mathematical Research Institute - SMRI

Gradient optimization methods: the benefits of instability — Peter Bartlett, UC Berkeley

www.youtube.com/watch?v=wEgT...

#MathSky #SMRISeminar

taking some artistic liberty in not using a log plot here

I really need to switch to a newer Obsidian Zotero connector, but I've got such heavy scripting debt in this ancient abandonware plugin that I cant fathom how long it'll take me to port everything over

Accepted at #ITCS2026: congratulations to Abigail and Vikrant!
itcs-conf.org @abigailgentle.com

Just caught myself writing D(P\|Q) on paper

genuinely great learning-by-teaching opportunity

I'm running a mock-conference for students using OpenReview, and have developed an appreciation for how nuanced that software is. But also an appreciation for conference chairs because of how much of a headache that software is

e-petitions e-petitions

My petition to the 🇦🇺 Australian government: make part-time PhD students' stipends tax exempt!

📋 Read and sign here: www.aph.gov.au/e-petitions/...
⏰ Deadline: October 1

I am a little sad they didn't attach their groundbreaking new information theory work for me to read

with great power (arXiv endorsement ability) comes great responsibility (quack emails)

Necessity of Block Designs for Optimal Locally Private Distribution Estimation Local differential privacy represents the gold standard for preserving the privacy of data before it leaves the device, and distribution estimation under this model has been well studied. Recently, pr...

New (solo) #privacy paper by @abigailgentle.com, PhD student at
@sydneycompsci.bsky.social: "Necessity of Block Designs for Optimal Locally Private Distribution Estimation," to be presented at the Information Theory Workshop (ITW'25)!

arxiv.org/abs/2508.05110

Thank you differential privacy bot :)

These objects have been used for private learning (and statistics in general) both implicitly and explicitly for some time. But this resolved my personal curiosity as to whether other optimal constructions could exist.

Necessity of Block Designs for Optimal Locally Private Distribution Estimation Local differential privacy represents the gold standard for preserving the privacy of data before it leaves the device, and distribution estimation under this model has been well studied. Recently, pr...

Workshop paper up on arXiv!

Bringing together a collection of necessary conditions on optimal LDP frequency estimation algorithms, and showing they restrict us to a specific class of combinatorial objects.

arxiv.org/abs/2508.05110

submitted a paper to arXiv, which has already passed peer review, but I'm still staring at a wall wondering if there's anything obviously dumb I've missed

or at least makes me want to study derandomized polynomial testing

P=BPP is so interesting a problem it almost makes me want to study complexity theory

Photograph of an Australian magpie perched on an outdoor lunch table

lunch with a colleague

word: painful
LaTeX: irritating
markdown: impossible

Greatest unsolved problem of our time: creating nice tables in a document

Does anyone know of a good collaborative pdf mark-up website or program? Kami works ok, but is a bit broken, regularly asks for permissions, and needs aggressive access to either Google drive or OneDrive

God bless the LLMs: "I added a new macro for X late in writing, please identify where I've used X and replace it with the macro" is saving my life regularly (I plan my writing poorly)

Annoyed that vscode has turned out to be the best LaTeX editor I've used so far. I'm sure nvim is competitive, but I haven't used it since undergrad

Quite unbelievable density of cool talks going on, even while its past midnight in the US where most of these researchers are based

current level of usage is purely for fun, just seeing how well they do on tasks I've already completed myself