Anirudh Rayas @RayasAnirudh
PhD student @ASU (@ASUEnergy). High dimensional statistics, Graphical models, ML anirudhrayas.github.io Tempe, AZ Joined May 2021-
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I am excited to share a new paper titled “Designing for Disclosure in Data Visualizations” set to appear at IEEE VIS 2025 (link below). 1/n
This workshop looks genuinely like a great step for statistics. What I like most is that it is young star researchers as speakers, and not a bunch of geriatrics talking about old stuff, like most stats conferences. fsmlims.wixsite.com/fsml25
Learning Networks from Wide-Sense Stationary Stochastic Processes ift.tt/2BwJPyi
since high dimensions are all the hype now, what is your favorite and most counter-intuitive high-dim phenomenon? this is still my favorite one:
🎓💻 Check out Krisha Mehta's @KrishaMehta2 testimonial! A a 2022 Fatima Fellowship fellow, she worked under her mentor Marouen Ben Guebila @marouenbg. She's now doing Ph.D. in computer science at the University of Chicago. [Watch her testimonial: youtube.com/watch?v=D2Pu0H…]
Just delivered 4 lectures on low-dimensional models for deep learning at ACDL yesterday to over 150 graduate students. Now all slides are online, it is a very good summary of our work in the last several years. acdl2023.icas.cc/prof-qing-qus-…
Happy to share the results of our latest project with @lugosi_gabor! TL;DR: Online-to-PAC conversions allow translating comparator-dependent regret bounds to algorithm-dependent generalization bounds. arxiv.org/abs/2305.19674 Read on for some more details! 1/
Highly recommended if you are interested in the convergence of SGD for smooth nonconvex functions. Prior literature uses various assumptions to carry out the analysis. We show how they are related, and propose a new assumption that is weaker than them all.
Highly recommended if you are interested in the convergence of SGD for smooth nonconvex functions. Prior literature uses various assumptions to carry out the analysis. We show how they are related, and propose a new assumption that is weaker than them all. https://t.co/o3BsnRKGxX
1 more reason to love testable learning. Classical stat. learning says: uniform convergence (small rademacher cmplxty on given dist) is sufficient but not necessary for learning. Indeed, "Empirical Risk Minimization" can fit classifiers to random noise & yet succeed in learning.
1 more reason to love testable learning. Classical stat. learning says: uniform convergence (small rademacher cmplxty on given dist) is sufficient but not necessary for learning. Indeed, "Empirical Risk Minimization" can fit classifiers to random noise & yet succeed in learning. https://t.co/J8dwBcPau6
.@kevinjtian and I tried to write a blog post and idk what happened but next thing you know we had half a monograph. For those intimidated by proofs of Quantum Singular Value Transformation: try our versions! arxiv.org/abs/2302.14324
Isotropic Gaussian Processes on Finite Spaces of Graphs (AISTATS 2023) Have you ever wanted a Gaussian process taking unweighted graphs as inputs, e.g. for Bayesian optimization in drug design? Here we propose such models in a principled way. (1/10) arxiv.org/abs/2211.01689
Excited to announce the Workshop on Information-theoretic Methods for Trustworthy Machine Learning at the Simons Institute from May 22nd-25th! Stay tuned for more details: simons.berkeley.edu/workshops/asu-…
A course by Emmanuel Candès with an interesting selection of topics and a great set of notes. Some topics covered: Multiple hypothesis testing, False discovery rate (and control via Benjamini-Hochberg), Knockoff filters, e-values, conformal inference etc. candes.su.domains/teaching/stats…
A great mathematician named Madhava, working in Kerala, India, discovered the power series for sin(x) in the 1300s, hundreds of years before Newton, Leibniz, Gregory, Taylor, et al. In this video, master teacher @garyrubinstein shows how Madhava did it: youtube.com/watch?v=mzigLb…
Decoupling: a 🧵. Computing functions of dependent random variables X₁, …, Xₙ can be quite tricky. Wouldn't it be nice if we could replace these by their "independent copies" X̃₁, …, X̃ₙ, where each Xᵢ, X̃ᵢ are identical in distr but the X̃ᵢ are mutually independent?
🔖New preprint: arxiv.org/abs/2302.09826 Answering some (not all!) questions about the expressivity of #topology in the context of #MachineLearning for graphs. TL;DR: persistent homology is awesome and can 'simulate' a corresponding WL test (even for higher orders). 🤯 🧵1/6
Our lastest work with @_brloureiro, @Aufinal & @l_arnaboldi on a unifying theory of SGD for 2-layer nets arxiv.org/pdf/2302.05882 that bridges many different regimes (mean-field PDEs, high-dimensional ODEs, classical gradient flow...) together
Even sparser random projections for Johnson-Lindenstrauss transforms and oblivious subspace embeddings! New preprint on arXiv: arxiv.org/abs/2302.06165 With @minilek
🤖Princeton ML Theory Summer School🤖 June 26 - June 30, 2023 Lectures by @CPehlevan, Philippe Rigollet, @ArthurJacot3, Jeffrey Pennington, Ben Adlam Apply at mlschool.princeton.edu Share widely @Princeton @EPrinceton @NSF
Glad to announce that our work “Dynamic Interventions for Networked Contagions,” co-authored with Sid Banerjee (@banerjee_sid) and Jon Kleinberg, has been accepted at @TheWebConf 2023 (formerly known as WWW 2023)! Preprint: arxiv.org/abs/2205.13394 A thread 🧵 1/n

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Jenny @Jenny879879
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The Innovation Studio @TheAIFactory
1K Followers 3K Following Creating bold new AI-fueled companies that solve real-world problems #AI #IndustrialAI #ML #Innovation #VentureStudio #StartupStudio
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🍃🌳🌲 Avi Chil... @AviMohan21
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Druv Pai @druv_pai
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The GAME School at AS... @gameschoolasu
631 Followers 430 Following Our program aims to educate the next generation of learners and empowers them with technofluency — its development, application and implications.
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Microsoft Research @MSFTResearch
561K Followers 2K Following We advance science and technology to benefit humanity.
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Thomas Kipf @tkipf
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Jascha Sohl-Dickstein @jaschasd
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Zico Kolter @zicokolter
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Machine Learning Stre... @MLStreetTalk
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