PyAerial can now do much faster knowledge discovery from tabular data via rule learning with a tree-based rule extractor (v1.1)! If you are interested in knowledge discovery or interpretable machine learning for high-stakes decision making, check out PyAerial. github.com/DiTEC-projec...

GitHub - DiTEC-project/pyaerial: Scalable association rule mining from tabular datasets.

Scalable association rule mining from tabular datasets. - DiTEC-project/pyaerial

github.com

Erkan Karabulut@erkankarabulut.bsky.social · 11mo ago

New software paper published in the SoftwareX journal, describing our neurosymbolic scalable association rule learner PyAerial for knowledge discovery and interpretable inference! 🔎 See the practical implications, capabilities, and benchmarking below! 📜 lnkd.in/eQubA7MD 🐍 lnkd.in/eDWnVWFr 🧵1/8

#IJCAI2026, #ECAI2026 decisions are out. Despite having written a rebuttal in hard personal circumstances, receiving no response to our rebuttal from reviewers, SPCs, ACs, or PCs is really heartbreaking.

Tabular foundation models can learn association rules out of the box! For the non-expert: If you work with tables and want to discover interesting patterns, tabular foundation models (TFMs) can quickly find significant, interesting, and generalizable patterns with no additional training. 🧵1/7

No. 2 #BJGPTop10 AI in lung cancer detection: AI algorithm identifies risk of lung cancer 4 months earlier doi.org/10.3399/BJGP... #LungCancer #CancerResearch #AIHealthcare #DigitalHealth #EarlyDetection #MedicalInnovation #ArtificialIntelligence #PrimaryCare #BJGP #research

Join the TRL Seminar tomorrow for talks on query ambiguity in open-domain data analysis @daniel-gomm.bsky.social, the SQaLE dataset for training text-to-SQL models @cowolff.bsky.social, and pattern/rule inference from tabular data @erkankarabulut.bsky.social. Info: trl-lab.github.io/trl-seminar/

PyAerial documentation is now much more comprehensive and has a lot of examples: pyaerial.readthedocs.io/en/latest/in... If you are interested in interpretable machine learning or knowledge discovery, check out PyAerial!

Yesterday, I gave a seminar on Scalable Knowledge Discovery with Neurosymbolic Rule Learning at the University of Amsterdam’s Data Science Center. Here’s a blog post with code samples and experiments on knowledge discovery from tabular data: 📄 erkankarabulut.github.io/blog/uva-dsc....

UvA Data Science Center Seminar: Scalable Knowledge Discovery

A walkthrough of scalable knowledge discovery from tabular data using neurosymbolic methods, covering association rule mining, its challenges, and neurosymbolic solutions with Aerial.

erkankarabulut.github.io

Why is defining consciousness not trivial? I am attending the ECAI2025 workshop ACAI - Awakening Consciousness in Artificial Intelligence. And hearing different sound and consistent, at first, definitions or assumptions around consciousness made me think of this question.

I'm excited to present our new method for enhancing knowledge discovery from tabular data using tabular foundation models at the #ECAI2025 conference workshops (ANSyA) this Sunday, October 26th. See our paper, Discovering Association Rules in High-Dimensional Small Tabular Data, below 👇. 🧵1/2

Tabular foundation models can boost knowledge discovery from high-dimensional tabular data with few instances (e.g., gene expression or rare disease data, 1K+ columns and <100 rows)! 📄 Short Paper (Accepted at ECAI 2025 workshops, ANSyA): arxiv.org/pdf/2509.20113 🐍 Code: tinyurl.com/3z8cmuhw 🧵1/8

New software paper published in the SoftwareX journal, describing our neurosymbolic scalable association rule learner PyAerial for knowledge discovery and interpretable inference! 🔎 See the practical implications, capabilities, and benchmarking below! 📜 lnkd.in/eQubA7MD 🐍 lnkd.in/eDWnVWFr 🧵1/8

I will be at the NeSy2025 conference this week, presenting Aerial scalable neurosymbolic tabular rule learner! Feel free to reach out if you are interested in knowledge discovery, interpretable inference!

Erkan Karabulut@erkankarabulut.bsky.social · last yr.

Rule learning is valuable but overlooked in modern AI; we argue it merits more focus for two reasons: knowledge discovery and interpretable inference. Our NeSy 2025 paper presents a state-of-the-art neurosymbolic tabular rule learner. 📄 arxiv.org/pdf/2504.19354 🐍 github.com/DiTEC-projec... 🧵1/8

PyAerial looks much nicer now, with new CI/CD implemented, 50+ test cases and visualizations of association rules! Learning rules over large tables for knowledge discovery and interpretable inference is much easier with PyAerial. github.com/DiTEC-projec...

GitHub - DiTEC-project/pyaerial: A Python package of the Aerial neurosymbolic association rule mining algorithm from tabular datasets.

A Python package of the Aerial neurosymbolic association rule mining algorithm from tabular datasets. - DiTEC-project/pyaerial

github.com

Erkan Karabulut@erkankarabulut.bsky.social · last yr.

New Python package for scalable association rule mining! We propose a novel neurosymbolic method for scalable rule mining from tabular data that can be used for both knowledge discovery and fully interpretable inference. 🧩 github.com/DiTEC-projec... 📜 arxiv.org/pdf/2504.19354 🧵1/5

I wrote a new blog post that introduce our new neurosymbolic tabular rule learner (accepted at NeSy 2025) in an intuitive level. Feel free to reach out if you wanna discuss about it! 📄 erkankarabulut.github.io/blog/scalabl...

Erkan Karabulut@erkankarabulut.bsky.social · last yr.

Rule learning is valuable but overlooked in modern AI; we argue it merits more focus for two reasons: knowledge discovery and interpretable inference. Our NeSy 2025 paper presents a state-of-the-art neurosymbolic tabular rule learner. 📄 arxiv.org/pdf/2504.19354 🐍 github.com/DiTEC-projec... 🧵1/8

Rule learning is valuable but overlooked in modern AI; we argue it merits more focus for two reasons: knowledge discovery and interpretable inference. Our NeSy 2025 paper presents a state-of-the-art neurosymbolic tabular rule learner. 📄 arxiv.org/pdf/2504.19354 🐍 github.com/DiTEC-projec... 🧵1/8

New Python package for scalable association rule mining! We propose a novel neurosymbolic method for scalable rule mining from tabular data that can be used for both knowledge discovery and fully interpretable inference. 🧩 github.com/DiTEC-projec... 📜 arxiv.org/pdf/2504.19354 🧵1/5

On Wednesday (16th of April) at the NWO ICT.Open, I will present our work on Interpretable Decision-Making in (Digital Twins of) Smart Environments for high-stakes decision-making. If you are interested, please visit me on the exhibition floor! See the details below 👇

There are only so many words and actions we can say or do, and they are no match for the complexity of what's going on in our brain. Whatever anyone says or does, it is not exactly what they meant to say or do. It's just a misalignment of computation and memory to output.

There is something inhuman in quantifying and optimizing things that I can not pinpoint. It is not only that we miss-quantity the unquantifiable or misrepresent the intangible to pursue as goals, but deeper. It is depressing to think every human institution rests on quantification and optimization.

Together with @kareemyousrii.bsky.social, we announce the Neurosymbolic Generative Models special track at the NeSy 2025 Conference 🎉 Call for Papers is live! 2025.nesyconf.org/nesy-generat... See you in Santa Cruz in Sep 2025! @nesyconf.org

Hello Bluesky! I am a researcher focusing on neurosymbolic knowledge discovery and decision-making in smart environments.