Tom Viering @ ICML @TViering
Assistant professor. I am investigating the question: how much data do we need for AI? I am interested in transformers, Bayesian ML, and meta-learning. tomviering.nl Delft, Netherlands Joined November 2011-
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Very happy today with the interesting questions at the posters when presenting our work: Alpha-PFN Fast Entropy Search via ICL! Find model weights here: github.com/automl/AlphaPFN Let me know if you have any questions or want to chat at ICML!
Drowning in 6,000+ ICML papers? 😵I vibecoded an ICML 2026 paper filter: scores all 6,341 accepted papers against keyword you define, then outputs a one page offline HTML file with day/time/room + filters. Uses simple keyword matching (no LLMs). github.com/tomviering/ICM…
Are you also getting lost in the ICML schedule? Here is an overview for your week!
🗓️ Sat, FMSD Workshop, 15:45–17:00 Context-Aware Learning Curve Extrapolation We adapt LC-PFN transformer with register tokens to curb overfitting, and condition extrapolation on learner identity to capture learner-specific curve dynamics. 🔗 openreview.net/forum?id=oN4FI…
Excited to be presenting 3 papers at ICML this year, would love to chat if you're around! MalTree: Tracing Malware Evolution using Embeddings at Scale α-PFN: Fast Entropy Search via In-Context Learning Context-Aware Learning Curve Extrapolation with Prior-Data Fitted Networks
🗓️ Wed, Poster Session 4, 14:30–16:15, Hall A α-PFN: Fast Entropy Search We use Prior-Data Fitted Networks (PFNs) to accelerate Predictive, Max, and Joint Entropy Search up to 72x. We amortize the expensive Bayesian Optimization acquisition function. automl.github.io/AlphaPFN/
🗓️ Tue, Poster Session 2, 14:00–15:45, Hall A MalTree: Tracing Malware Evolution using Embeddings at Scale The first Malware Family Tree, built with Transformer embeddings over large-scale malware corpora. By my MSc student Akash Amalan. 🔗 aj730.github.io/PhylogeneticsF…
@eadeli @euanashley @drfeifei @StanfordHAI @StanfordMed Seems similar to: arxiv.org/abs/1612.06890 are you aware of this work? How does it compare to yours? Very interesting work, thanks!
@FrankRHutter Cool! I was looking forward to reading this since my visit to Freiburg! I am particular fascinated by the generative abilities... In March I will give a lecture about PFNs and this will be included for sure :).
I'm hiring for a fully funded #PhD position in #ComputerVision on 'Detailed Video Understanding' at @LIACS @UniLeiden. Apply before 22nd June. More info👇 universiteitleiden.nl/en/vacancies/2…
The deadline for @NeurIPSConf 2024 has passed, but it doesn't mean we don't work hard to have another fantastic conference! Right now, we are in need for REVIEWERS. If you know someone suitable for this role, please fill in this form: forms.gle/W3Yrdqxu4jE11Q…
🚀 We're looking for a PhD student to join our team at @tudelft! Deadline for applications is January 5. Please share, and feel free to contact me if you have any questions.
Together with Julia Olkhovskaia I am looking for a (fully paid) PhD student to work on multi-armed bandits and reinforcement learning theory. Please share! More details: fransoliehoek.net/wp/vacancies/ Note: we hope to recruit through the ELLIS PhD program. Deadline Nov. 15!
@AliciaCurth @Jeffaresalan Great work! Excellent read!
Every StatML intro class covers complexity-error U-curves, so @Jeffaresalan & I asked ourselves whether the info from these classes is enough to explain double descent too? Our #NeurIPS23 paper does a roundtrip of The Elements of Statistical Learning and answers “Yes”! Long🧵1/n
@napoperez1998 Soro is right, please apply! We have several students from Latin America in our faculty.
I am hiring a PhD student. The topic will be related to deep meta-learning on learning curves. You can apply here: tudelft.nl/over-tu-delft/…
@Burevestnik0829 Hi! If you are from TU Delft I have BSc or MSc projects. If you are from abroad but Europe, we can look at Erasmus. Other options also possible. DM me for more info :)
I will soon open up a PhD position for deep learning on learning curves. See here: tomviering.nl/vacancy.html Challenge: Developing deep meta-learning algorithms to model and understand learning curve patterns in machine learning. Impact: Faster, better, more cost-efficient ML.
And one more new MOOC, by Hongrui Wang, Amira lnouty, Luca laurenti, Wendelin Bohmer and @TViering. Covers AI concepts of clustering, dimensionality reduction, and introduces you to reinforcement and deep learning. Starts May 16th, so enroll now! online-learning.tudelft.nl/courses/ai-ski…
New MOOC by @TViering and Hanne Kekkonen: learn the basics of (supervised) machine learning, e.g. various classification and regression machine learning algorithms to solve real-life problems with scikit-learn in Python. Starts May 16th, so enroll now! online-learning.tudelft.nl/courses/ai-ski…
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