ProbML 2026 (formerly AABI) invites submissions on probabilistic ML (both Bayesian and otherwise!), July 5 in Seoul (co-located with ICML). Website: probml.cc. Tracks: proceedings (PMLR), workshop, fast track. New focus includes applications in healthcare and climate!
Submit by: 20 March 2026.
Predictions can affect the thing they aim to predict. Think of self-fulfilling economic forecasts or self-negating routing (Google maps etc.). Can we learn from samples that reacted to our model? And what if the ground truth also reacts?
👇Answers👇
arxiv.org/abs/2602.04402
w/ @krikamol.bsky.social
🚨 Paper Alert: Empirical Decision Theory 🚨
We introduce “Empirical Decision Theory”, a framework for decision making that is radically empirical: Instead of positing an abstract state space, we work with observed act–consequence protocols only. Check it out!
👉https://arxiv.org/abs/2512.05677👈
Reminder that this is today at 7 pm! Please join us if you are at #NeurIPS2025
👋 I’m at @neuripsconf.bsky.social in San Diego 🌴Hit me up if you’d like to talk imprecise probabilities, learning theory, good old statistics, or simply anything Bayesian 🤓 #NeurIPS2025 #NeurIPS #NeurIPS25
I had a great time visiting the @Stanford
Trustworthy AI lab (stairlab.stanford.edu) last week. My talk on empirical AI alignment (lnkd.in/d_SxW4YP) and incentive-aware AI regulation sparked a lot of fruitful discussion💡
Now off to #NeurIPS2025 in San Diego. 🌴
Something worrying me: many seem to change their research direction out of FOMO, reacting to (the obvious) recent trend: "If I don't do this, someone else will do it!"
One of the key perks we have in academia is the freedom to set our own agenda.* If someone else can—and WILL—do it, why would you?
🎉 Honored to receive the IJAR Young Researcher Award (🥈)
The award is given to researchers who demonstrate excellence at an early stage of their scientific careers, sponsored by the International Journal of Approximate Reasoning (IJAR).
isipta25.sipta.org/awards
Read the paper here:
📄 arxiv.org/abs/2403.04629
#MachineLearning #BayesianOptimization #XAI #HumanInTheLoop #Exosuits #ShapleyValues
🗣️ Reviewer feedback on our paper:
“Practical relevance with a well-motivated human-in-the-loop use case.”
“A pleasure to read... good figures, insightful explanations.”
“Novel integration of Shapley values into Bayesian Optimization for interpretability.”
🦿 Exosuits support the lower back during physical labor — like pallet building.
📽️ See them in action:
💡 Why does this matter?
Because it helps personalize wearable robotic exosuits more efficiently — by enabling human users to understand and intervene in the process.
🧠 We open up the black box using Shapley values — to explain why Bayesian Optimization chooses specific parameters.
Bayesian Optimization is great for black box optimization — but often a black box itself.
🔍 Why does it pick the parameters it does?
We wanted to find out.👇
Glad to share this paper was accepted at @ecmlpkdd.org !
We show interpreting BayesOpt helps personalize soft exosuits (see picture)
Great collaboation w/ Fede Croppi, @giuseppe88.bsky.social et al.
@lmumuenchen.bsky.social @munichcenterml.bsky.social
@harvard.edu @wyssinstitute.bsky.social
Getting coverage guarantees over functional surrogate models? This is what A. Gray and V. Gopakumar (they did the heavy lifting) have done using conformal predictions and zonotopes over reduced dimensions.
It will be present at UAI 2025 (@auai.org), but a preview is here: arxiv.org/abs/2501.18426.
In case you are interested in very recent advances of learning under imprecision with probability sets, you can check out the recent and fantastic SIPTA virtual talk by @krikamol.bsky.social, now on youtube: www.youtube.com/watch?v=gGPF...
Helen Alber's DAGStat 2025 talk yesterday showed how LLMs streamline sentence classification and how to fix their mistakes.
💡 The solution? A two-step approach: LLMs pre-classify, experts refine, and Sim-SIMEX corrects errors, boosting efficiency and handling imbalanced data as well as MC-SIMEX.
If you're attending #NeurIPS2024, don't miss our spotlight poster on multicriteria benchmarking TODAY 11am in West Ballroom A-D
Talk: neurips.cc/virtual/2024...
Paper: openreview.net/pdf?id=jXxvS...
Where? West Ballroom A-D #6501
When? Thu 12 Dec 11 a.m. - 2 p.m. local time
#neurips #neurips24
#MachineLearning is all about learning parameters from data, right? Well, not quite…
🤸♂️Actually, it sometimes works the other way around.
🤔Curious how?
👉Check out our poster on reciprocal learning: neurips.cc/virtual/2024...
@neuripsconf.bsky.social #NeurIPS2024 #NeurIPS
