Looking forward to the workshop!

Danica Sutherland@djsutherland.ml · 4mo ago

I'm co-organizing a workshop on Hypothesis Testing at ICML (Seoul, in July): testing.ml There's been a ton of cool recent work in this area, but there hasn't been (afaik) an ML-venue workshop about it in a decade+. ML methods for testing, testing hypotheses about ML, it's all here!

Adaptive experimentation workshop at Gatsby!

aaeworkshop.bsky.social@aaeworkshop.bsky.social · 4mo ago

🚀 Excited to announce the Advances in Adaptive Experimentation (AAE) Workshop! 📅 June 18–19 🔗 t.co/Zar0qWhgA8 Join us at @gatsbyucl.bsky.social in London for two days at the frontier of causal inference 🧩, adaptive experiments 🔁, and bandits/RL 🤖!

At #NeurIPS ? Visit our posters! 🧵 Demystifying Spectral Feature Learning for Instrumental Variable Regression: #2600, Wed 11am Regularized least squares learning with heavy-tailed noise is minimax optimal: #3012, Wed 4:30pm ✨spotlight✨ 1/2

🔥 WANTED: Student Researcher to join me, @vdebortoli.bsky.social, Jiaxin Shi, Kevin Li and @arthurgretton.bsky.social in DeepMind London. You'll be working on Multimodal Diffusions for science. Apply here google.com/about/career...

Looking forward to next week's Winter School on Causality and Explainable AI! xai-winter-school.github.io

Thank you for a fantastic conference!

Motonobu Kanagawa@motonobu-kanagawa.bsky.social · 11mo ago

Thank you very much @arthurgretton.bsky.social for your great talk!

Sequential kernel embedding for mediated and time-varying dose response curves Appearing in Bernoulli: projecteuclid.org/journals/ber... with preprint here: arxiv.org/abs/2111.03950 ...along with code! github.com/liyuan9988/K... Rahul Singh, Liyuan Xu

Research Fellow position open at @gatsbyucl.bsky.social to work with me and Jason Hartford on Causality in Biological Systems! Apply at link, deadline is 27 August: www.ucl.ac.uk/work-at-ucl/...

UCL – University College London

UCL is consistently ranked as one of the top ten universities in the world (QS World University Rankings 2010-2022) and is No.2 in the UK for research power (Research Excellence Framework 2021).

ucl.ac.uk

Accelerated Diffusion Models via Speculative Sampling, at #icml25 ! 16:30 Tuesday July 15 poster E-3012 arxiv.org/abs/2501.05370 @vdebortoli.bsky.social Galashov @arnauddoucet.bsky.social

Distributional diffusion models with scoring rules at #icml25 Fewer, larger denoising steps using distributional losses! Wednesday 11am poster E-1910 arxiv.org/pdf/2502.02483 @vdebortoli.bsky.social Galashov Guntupalli Zhou @sirbayes.bsky.social @arnauddoucet.bsky.social

Distributional Reduction paper with H. Van Assel, @ncourty.bsky.social, T. Vayer , C. Vincent-Cuaz, and @pfrossard.bsky.social is accepted at TMLR. We show that both dimensionality reduction and clustering can be seen as minimizing an optimal transport loss 🧵1/5. openreview.net/forum?id=cll...

Composite Goodness-of-fit Tests with Kernels, now out in JMLR! www.jmlr.org/papers/v26/2... Test if your distribution comes from ✨any✨ member of a parametric family. Comes in MMD and KSD flavours, and with code. @oscarkey.bsky.social @fxbriol.bsky.social Tamara Fernandez

Turns out that overfitting is the right approach when you want to generalize to new tasks!

Dimitri Meunier@dimitrimeunier.bsky.social · last yr.

🚨 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: 🧵👇

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

Looking forward to this!

Diane Larlus@dlarlus.bsky.social · last yr.

#PAISS2025 summer school in the Alps with amazing speakers, including @dimadamen.bsky.social @arthurgretton.bsky.social @science4all.org @yann-lecun.bsky.social @gabrielpeyre.bsky.social @claireve.bsky.social and many more ▶️ Apply here: paiss.inria.fr/apply-2025/

Kernel Single Proxy Control for Deterministic Confounding at #AISTATS25 Proxy causal learning generally requires two proxy variables - a treatment and an outcome proxy. When is it possible to use just one? arxiv.org/abs/2308.04585 Liyuan Xu

Credal Two-Sample Tests of Epistemic Uncertainty at #AISTATS25 Compare credal sets: convex sets of prob measures where elements capture aleatoric uncertainty; set represents epistemic uncertainty. arxiv.org/abs/2410.12921 @slchau.bsky.social Schrab @sejdino.bsky.social @krikamol.bsky.social

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

Density Ratio-based Proxy Causal Learning Without Density Ratios 🤔 at #AISTATS2025 An alternative bridge function for proxy causal learning with hidden confounders. arxiv.org/abs/2503.08371 Bozkurt, Deaner, @dimitrimeunier.bsky.social, Xu

Announcing : The 2nd International Summer School on Mathematical Aspects of Data Science mathsdata2025.github.io EPFL, Sept 1–5, 2025 Speakers: Bach @bachfrancis.bsky.social Bandeira Mallat Montanari Peyré @gabrielpeyre.bsky.social For PhD students & early-career researchers Apply before May 15!

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression #ICLR25 openreview.net/forum?id=ReI... NNs ✨better than fixed-feature (kernel, sieve) when target has low spatial homogeneity, ✨more sample-efficient wrt Stage 1 Kim, @dimitrimeunier.bsky.social, Suzuki, Li

Deep MMD Gradient Flow Without Adversarial Training at #ICLR2025 openreview.net/forum?id=Pf8... Do you have a GAN critic? Then you have a diffusion! Adaptive MMD gradient flow trained on a forward diffusion, competitive performance on image generation! Galashov, @vdebortoli.bsky.social

Looking forward to this!

NAVER LABS Europe@naverlabseurope.bsky.social · last yr.

Applications are 📣OPEN📣 for #PAISS2025 THE AI summer school in #Grenoble 1-5 Sept! Speakers so far @yann-lecun.bsky.social @dimadamen.bsky.social @arthurgretton.bsky.social @gabrielpeyre.bsky.social @science4all.org A. Cristia J. Revaud M. Caron J. Carpentier M. Vladimirova ➡️ paiss.inria.fr

Our joint paper with Geoffrey Wolfer @gwolfer.bsky.social "Variance-Aware Estimation of the Kernel Mean Embedding" accepted for publication in the Journal of Machine Learning Research 🥳 arxiv.org/abs/2210.06672

Variance-Aware Estimation of Kernel Mean Embedding

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, prope...

arxiv.org

Congratulations @lestermackey.bsky.social !!

Michele Guindani@mguindani.bsky.social · last yr.

Congratulations to @lestermackey.bsky.social for receiving the 2025 COPSS Award! 🎉👏 Lester is currently the Chair of the Section on Bayesian Statistical Sciences (SBSS) of the American Statistical Association.

Hey ELLIS PhD students, need to travel but low on funds? Learn how ELSA can help with that: bit.ly/4kqjyel #ELLISPhD #MobilityFund #SustainableAI #ProjectsBuildingOnELLIS

I already advertised for this document when I posted it on arXiv, and later when it was published. This week, with the agreement of the publisher, I uploaded the published version on arXiv. Less typos, more references and additional sections including PAC-Bayes Bernstein. arxiv.org/abs/2110.11216

The slides of my talk at OIST are online: pierrealquier.github.io/slides/okina... Thanks to the organisers, and thanks to Frank Nielsen for the photo of my talk 🙏 Link to the paper: arxiv.org/abs/2412.18539

Video now public from the talk "Learning to act in noisy contexts using deep proxy learning" at the NeurIPS'24 Workshop on Causal Representation Learning! Video: neurips.cc/virtual/2024... Slides: www.gatsby.ucl.ac.uk/~gretton/cou...

super happy about this preprint! we can *finally* perform efficient exploration and find near-optimal stationary policies in infinite-horizon linear MDPs, and even use it for imitation learning :) working with @neu-rips.bsky.social and @lviano.bsky.social on this was so much fun!!