🇺🇦 #NowPlaying on KEXP's #VarietyMix
Joey Bada$$:
🎵 Land of the Free
#JoeyBada$$
https://caobadeelers.bandcamp.com/album/joey-bada-land-of-the-free-caoba-deelers-rmx
https://open.spotify.com/track/7BjygzvUpf8P7X6uWzIhsF
Alphabet's Intrinsic, which builds AI models and software for industrial robots, joins Google; it will remain a distinct entity and work with Google DeepMind (Rebecca Szkutak/TechCrunch)
https://techcrunch.com/2026/02/25/alphabet-owned-r…
Trump taps 'alpha male' influencer as new envoy for American tourism and values - Alternet.org
https://www.alternet.org/trump-influencer/
#GrindayFriday for this week is Los Angeles powerviolence outfit APARTMENT 213 and their new EP from September, 'Glamour': https://apartment213.bandcamp.com/album/glamour
I love so…
"Two marsupials thought extinct for 6,000 years found alive in Indonesian Papua"
#Indonesia #Animals
High-Dimensional Robust Mean Estimation with Untrusted Batches
Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu, Junze Yin
https://arxiv.org/abs/2602.20698 https://arxiv.org/pdf/2602.20698 https://arxiv.org/html/2602.20698
arXiv:2602.20698v1 Announce Type: new
Abstract: We study high-dimensional mean estimation in a collaborative setting where data is contributed by $N$ users in batches of size $n$. In this environment, a learner seeks to recover the mean $\mu$ of a true distribution $P$ from a collection of sources that are both statistically heterogeneous and potentially malicious. We formalize this challenge through a double corruption landscape: an $\varepsilon$-fraction of users are entirely adversarial, while the remaining ``good'' users provide data from distributions that are related to $P$, but deviate by a proximity parameter $\alpha$.
Unlike existing work on the untrusted batch model, which typically measures this deviation via total variation distance in discrete settings, we address the continuous, high-dimensional regime under two natural variants for deviation: (1) good batches are drawn from distributions with a mean-shift of $\sqrt{\alpha}$, or (2) an $\alpha$-fraction of samples within each good batch are adversarially corrupted. In particular, the second model presents significant new challenges: in high dimensions, unlike discrete settings, even a small fraction of sample-level corruption can shift empirical means and covariances arbitrarily.
We provide two Sum-of-Squares (SoS) based algorithms to navigate this tiered corruption. Our algorithms achieve the minimax-optimal error rate $O(\sqrt{\varepsilon/n} \sqrt{d/nN} \sqrt{\alpha})$, demonstrating that while heterogeneity $\alpha$ represents an inherent statistical difficulty, the influence of adversarial users is suppressed by a factor of $1/\sqrt{n}$ due to the internal averaging afforded by the batch structure.
toXiv_bot_toot
If you are not an expert on how our DNA works and how it forms who we are: read this masterpiece of Philip Ball:
https://www.marginaliareviewofbooks.com/post/alpha-genome
In a strongly worded decision this week, a federal judge ordered that the Voice of America
— its mission to provide news for countries around the world largely shut down for the past year by the Trump administration
— come roaring back to life.
Whether or not that actually happens is anybody’s guess.
The government filed notice Thursday to appeal U.S. District Court Judge Royce C. Lamberth’s order two days earlier to put hundreds of VOA employees who have been on paid l…
Great new catchy, brief, punchy EP by Brooklyn garage punks DR. DENCE. Almost has an Aussie pub rock feel, but like, yknow, in New York. It rips. Four great, fast, fuzzy songs.
https://doctoredtapes.bandcamp.com/album/sick-dumb-spam