Abhinav Kumar

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TLDR; The PSF has made the decision to put our community and our shared diversity, equity, and inclusion values ahead of seeking $1.5M in new revenue. Please read and share. pyfound.blogspot.com/2025/10/NSF-...
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A Martingale Kernel Two-Sample Test The Maximum Mean Discrepancy (MMD) is a widely used multivariate distance metric for two-sample testing. The standard MMD test statistic has an intractable null distribution typically requiring costly...

A nice variant of the kernel two-sample test. arxiv.org/abs/2510.11853

Sketch of the idea: The MMD is the core of a commonly used nonparametric test for distribution testing. It works by embedding distributions into a RKHS and comparing their mean embeddings. [+]

As a grad student, the biologist Yitzhi “Patrick” Cai helped program 𝘌. 𝘤𝘰𝘭𝘪 bacteria to become a biosensor for arsenic contamination in drinking water. Today, he is leading a global effort to build the first-ever synthetic eukaryotic genome. www.quantamagazine.org/hes-gleaning...

We have two new mentees who are offering their time via office hours! Please show Sandeep Silwal and Kevin Tian some love and sign up to meet them!
let-all.com/officehours....

The paper this talk is based on is quite impressive arxiv.org/abs/2507.04441 one of those cases where you see direct real actionable insight using the categorical hammer.

YouTube video by Institute for Advanced Study Simulating Time With Square-Root Space (And With Details) - Ryan Williams

Today at IAS, I gave a 2 hr 15 mins lecture on why TIME[t] is in SPACE[√(t log t)]. You can watch it here!
www.youtube.com/watch?v=ThLv...

Aligning an AI with human preferences might be hard. But there is more than one AI out there, and users can choose which to use. Can we get the benefits of a fully aligned AI without solving the alignment problem? In a new paper we study a setting in which the answer is yes.

Egan conjecture holds Given a Euclidean simplex of dimension n⩾2 let its radii of inscribed and circumscribed spheres be r and R, and the distance between the centers of th…

Sergei Drozdov has published his nice proof using hyperbolic simplexes of the necessary and sufficient condition on the radii of spheres that sit inside and outside a Euclidean simplex in any dimension.

www.sciencedirect.com/science/arti...

The workshops focused on (in chronological order):

- Variational Inference (youtube.com/playlist?lis...)
- Optimal Transport (youtube.com/playlist?lis...)
- Parallel Computing (youtube.com/playlist?lis...), and
- Computational Physics (youtube.com/playlist?lis...).

The mathematician Lingrui Ge recently helped find a new way to understand the solutions of almost-periodic functions, important equations that appear in quantum physics. The work has helped cement an intriguing connection between number theory and physics. www.quantamagazine.org/ten-martini-...

Pretty cool: the "Fundamental Examples" of independence structures in Non-Commutative Probability.

Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, Jose Zubizarreta
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
https://arxiv.org/abs/2508.17099

Adi Shamir's advice to young researchers:

1. Read, read, read. Back in the eighties, I read every cryptography paper out there. Once that became impossible, I read the abstract of every paper. Now I read at least every title.

An Ode to the Spherical Cow How Imperfect Models Drive Scientific Discovery

This week's post is about why spherical cows are physics' mascot ⚛️🧪

open.substack.com/pub/nirmalya...

Big fan of this perspective:

YouTube video by Mixed Integer Programming Lessons from Paula Harris / by Sophie Huiberts

This is about one of my greatest inspirations. It would mean a lot to me if you gave it a watch

Regardless of whether you plan to use them in applications, everyone should learn about Gaussian processes, and Bayesian methods. They provide a foundation for reasoning about model construction and all sorts of deep learning behaviour that would otherwise appear mysterious.

YouTube video by Causal Foundations E5: What Confounding Really Is

After a bit of a summer pause, I'm back to making episodes. In this episode, I explain the notion of confounding, and clarify why confounders should not be thought of as alternate explanations of an observed effect.

youtu.be/kAgS7cltBhM

Randomized trials (RCTs) help evaluate if deploying AI/ML systems actually improves outcomes (e.g., survival rates in a healthcare context).

But AI/ML systems can change: Do we need a new RCT every time we update the model? Not necessarily, as we show in our UAI paper! arxiv.org/abs/2502.09467

YouTube video by LoG Meetup NYC It's Time to Say Goodbye to Hard (equivariance) Constraints - Andrew Gordon Wilson

I had a great time presenting "It's Time to Say Goodbye to Hard Constraints" at the Flatiron Institute. In this talk, I describe a philosophy for model construction in machine learning. Video now online! www.youtube.com/watch?v=LxuN...

Armin Keki\'c, Jan Schneider, Dieter B\"uchler, Bernhard Sch\"olkopf, Michel Besserve
Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
https://arxiv.org/abs/2507.14901

Hirahara, Illango, and Loff posted on the arXiv a lovely result, showing that determining the communication complexity of a function f is NP-hard. A fundamental question first asked by Yao in '79. The proof is very clean and elegant. A fun read for the weekend!

arxiv.org/pdf/2507.104...

📢ICML alert: In the afternoon poster session, I'll present our paper: "Contextures: Representations from Contexts".

Our central argument: "Representations are learned from the association between input 𝑋 and context variable 𝐴"

📍East: E-1708, July 15, 4.30-7pm

📜 openreview.net/pdf?id=4GZwFPz…

YouTube video by Daily Stoic 7 Simple Daily Habits That Will Change Your Life (Stoic-Inspired)

Here's 7 habits to start this week! youtu.be/cqjf4DJyAaA?...

Learning Actionable Counterfactual Explanations in Large State Spaces

Keziah Naggita, Matthew Walter, Avrim Blum

Action editor: Taylor Killian

https://openreview.net/forum?id=tXnVRpRlR8

#actions #explanations #features

And here are my posters:

Poster 1 - Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms.
Thursday 11:00, E-2212

Poster 2 - Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data.
Friday, Scaling Up Interventions Model workshop.

Zhiyi Dong, Zixuan Liu, Yongyi Mao
On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
https://arxiv.org/abs/2507.06552

YouTube video by Institut des Hautes Etudes Scientifiques (IHES) Satya N. Majumdar - 1/3 Nonequilibrium Point Processes with Long-range Correlations (...)

I've just finished watching this - youtu.be/M802ElI4u4k?... - trilogy of lectures. All very interesting stuff (and exceptional blackboard work!) - well worth a look (as are a few of the other videos from this same Summer School).

Erik Jahn, Frederick Eberhardt, Leonard J. Schulman
Lower Bounds on the Size of Markov Equivalence Classes
https://arxiv.org/abs/2506.20933

Towards characterizing the value of edge embeddings in Graph Neural Networks Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our understanding of finer...

In recent work arxiv.org/abs/2410.09867 with D. Rohatgi, @tm157.bsky.social, @zacharylipton.bsky.social , J. Lu and A. Moitra, we revisit fine-grained expressiveness in GNNs---beyond the usual symmetry (Weisfeiler-Lehman) lens. Paper will appear in ICML '25, thread below.