Tomorrow, Zak will talk about his new deep RL method for hard exploration problems. Join us! The talk will be hosted by Csaba.
Antoine Moulin
@antoine-mln.bsky.social
doing a phd in RL/online learning on questions related to exploration and adaptivity > https://antoine-moulin.github.io/
Summer school on Decisions and Theory for AI! July 13-17 Leiden University Lecturers: Gergely Neu @neu-rips.bsky.social Claire Vernade @claireve.bsky.social Marco Avella Medina Peter Grünwald @petergrunwald.bsky.social Myself Application deadline: April 30 sites.google.com/view/dtailsu...
DTAIL
Modern AI systems are no longer just engineering artefacts: they are decision-makers operating under uncertainty, constraints, and societal needs. This summer school concerns the intersection of decis...
sites.google.com
Michael @mkearnsphilly.bsky.social ) and I wrote a blog post about our experiences using AI for research, and our thoughts on what these developments will mean for research, publication, and education: www.amazon.science/blog/how-ai-...
How AI is changing the nature of mathematical research
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
amazon.science
So excited for the RL theory meets experiment workshop tomorrow at NeurIPS: arlet-workshop.github.io/neurips2025/... Talks look AMAZING and you can hear me be the foolish experimentalist on a panel
Schedule | ARLET
The session will cover invited talks, contributed talks and posters. The tentative schedule in Pacific Daylight Time (GMT−7) can be found below.
arlet-workshop.github.io
Very excited to share our preprint: Self-Speculative Masked Diffusions We speed up sampling of masked diffusion models by ~2x by using speculative sampling and a hybrid non-causal / causal transformer arxiv.org/abs/2510.03929 w/ @vdebortoli.bsky.social, Jiaxin Shi, @arnauddoucet.bsky.social
We're finally out of stealth: percepta.ai We're a research / engineering team working together in industries like health and logistics to ship ML tools that drastically improve productivity. If you're interested in ML and RL work that matters, come join us 😀
Percepta | A General Catalyst Transformation Company
Transforming critical institutions using applied AI. Let's harness the frontier.
percepta.ai
I am happy to share that our paper "Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs" was accepted at Neurips 2025 ! 🥳 Huge thanks to my co-authors @rflamary.bsky.social and Bertrand Thirion ! arxiv.org/abs/2506.12025 (1/5)
Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs
Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimizat...
arxiv.org
News 🎉 We’re thrilled to announce our final panelist: David Silver! Don’t miss David and our amazing lineup of speakers—submit your latest RL work to our NeurIPS workshop. 📅 Extended deadline: Sept 2 (AoE)
We've extended the deadline for our workshop's calls for papers/ideas! Submit your work by August 29 AoE. Instructions on the website: arlet-workshop.github.io/neurips2025/...
Call for Papers | ARLET
A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design.
arlet-workshop.github.io
The OpenReview link for our calls (for papers and ideas) is available, submit here: openreview.net/group?id=Neu... We look forward to receiving your submissions!
last year's edition was so much fun I'm really looking forward to this one!! join us in San Diego :))
ARLET@arletworkshop.bsky.social · last yr.Delighted to announce that the second edition of our workshop has been accepted as a #NeurIPS2025 workshop!
Join us for Nneka's presentation tomorrow! Last talk before the summer break.
Join us tomorrow for Dave's talk! He will present his recent work on randomised exploration, which received an outstanding paper award at ALT 2025 earlier this year.
new preprint with the amazing @lviano.bsky.social and @neu-rips.bsky.social on offline imitation learning! learned a lot :) when the expert is hard to represent but the environment is simple, estimating a Q-value rather than the expert directly may be beneficial. lots of open questions left though!
🚨 New paper accepted at SIMODS! 🚨 “Nonlinear Meta-learning Can Guarantee Faster Rates” arxiv.org/abs/2307.10870 When does meta learning work? Spoiler: generalise to new tasks by overfitting on your training tasks! Here is why: 🧵👇
Nonlinear Meta-Learning Can Guarantee Faster Rates
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. The main aim of ...
arxiv.org
Dhruv Rohatgi will be giving a lecture on our recent work on comp-stat tradeoffs in next-token prediction at the RL Theory virtual seminar series (rl-theory.bsky.social) tomorrow at 2pm EST! Should be a fun talk---come check it out!!
Dylan Foster 🐢@djfoster.bsky.social · last yr.Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier arxiv.org/abs/2502.12465 New paper (another fun internship project!) with Dhruv Rohatgi, Adam Block, Audrey Huang (ahahaudrey.bsky.social), and Akshay Krishnamurthy (akshaykr.bsky.social). 1/11
new work on computing distances between stochastic processes ***based on sample paths only***! we can now: - learn distances between Markov chains - extract "encoder-decoder" pairs for representation learning - with sample- and computational-complexity guarantees read on for some quick details.. 1/n
A new blog post with intuitions behind continuous-time Markov chains, a building block of diffusion language models, like @inceptionlabs.bsky.social's Mercury and Gemini Diffusion. This post touches on different ways of looking at Markov chains, connections to point processes, and more.
Discrete Diffusion: Continuous-Time Markov Chains
A tutorial explaining some key intuitions behind continuous time Markov chains for machine learners interested in discrete diffusion models: alternative representations, connections to point processes...
inference.vc
Mattes Mollenhauer, Nicole M\"ucke, Dimitri Meunier, Arthur Gretton: Regularized least squares learning with heavy-tailed noise is minimax optimal https://arxiv.org/abs/2505.14214 https://arxiv.org/pdf/2505.14214 https://arxiv.org/html/2505.14214
Later today, Sikata and Marcel will talk about their recent work on oracle-efficient RL with ensembles. Join us!
Excited to share what I've been up to: bringing text diffusion to Gemini! Diffusion models are _fast_, and hold immense promise to challenge autoregressive models as the de facto standard for language modeling.
The tutorials, workshops, and community events for #COLT2025 have been announced! Exciting topics, and impressive slate of speakers and events, on June 30! The workshops have calls for contributions (⏰ May 16, 19, and 25): check them out! learningtheory.org/colt2025/ind...
Announcing the first workshop on Foundations of Post-Training (FoPT) at COLT 2025! 📝 Soliciting abstracts/posters exploring theoretical & practical aspects of post-training and RL with language models! 🗓️ Deadline: May 19, 2025
looking forward to giving a talk at the 2025 GHOST day in the beautiful city of Poznan (Poland) --- hope to see you there this weekend!
Is Best-of-N really the best we can do for language model inference? New paper (appearing at ICML) led by the amazing Audrey Huang (ahahaudrey.bsky.social) with Adam Block, Qinghua Liu, Nan Jiang, and Akshay Krishnamurthy (akshaykr.bsky.social). 1/11
Spectral Representation for Causal Estimation with Hidden Confounders at #AISTATS2025 A spectral method for causal effect estimation with hidden confounders, for instrumental variable and proxy causal learning arxiv.org/abs/2407.10448 Haotian Sun, @antoine-mln.bsky.social, Tongzheng Ren, Bo Dai
We'll be presenting our work on Oracle-Efficient Reinforcement Learning for Max Value Ensembles at the RL theory seminar! Been following this series for a while, super excited we get to present some of our work. 🥳
RL Theory Virtual Seminars@rl-theory.bsky.social · last yr.Last seminars before the summer break: 04/29: Max Simchowitz (CMU) 05/06: Jeongyeol Kwon (Univ. of Widsconsin-Madison) 05/20: Sikata Sengupta & Marcel Hussing (Univ. of Pennsylvania) 05/27: Dhruv Rohatgi (MIT) 06/03: David Janz (Univ. of Oxford) 06/10: Nneka Okolo (MIT)
new paper online: "CONFIDENCE SEQUENCES FOR GENERALIZED LINEAR MODELS VIA REGRET ANALYSIS" TL;DR: we reduce the problem of designing tight confidence sets for statistical models to proving the existence of small regret bounds in an online prediction game read on for a quick thread 👀👀👀 1/
Last seminars before the summer break: 04/29: Max Simchowitz (CMU) 05/06: Jeongyeol Kwon (Univ. of Widsconsin-Madison) 05/20: Sikata Sengupta & Marcel Hussing (Univ. of Pennsylvania) 05/27: Dhruv Rohatgi (MIT) 06/03: David Janz (Univ. of Oxford) 06/10: Nneka Okolo (MIT)
We are organising EWRL in Tübingen in September. We are working on the program and we will soon be able to make first announcements. Stay tuned, follow @ewrl18.bsky.social
EWRL@ewrl-org.bsky.social · last yr.Mark your calendars, EWRL is coming to Tübingen! 📅 When? September 17-19, 2025. More news to come soon, stay tuned!
