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Workshop Fri, Jul 10, 2026 • 4:00 PM – 1:00 AM PDT GRAND BALLROOM 103

Foundation Models for Structured Data

Nick Erickson ⋅ Xiyuan Zhang ⋅ Mononito Goswami ⋅ Lennart Purucker ⋅ Boran Han ⋅ Maximilian Schambach ⋅ Arjun Ashok ⋅ Rajat Sen
Project Page [ OpenReview] 

Abstract

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Schedule

Timezone: America/Los_Angeles
5:00 PM
Opening Remarks
Nick Erickson ⋅ Lennart Purucker ⋅ Maximilian Schambach
6:25 PM
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
Shi-ang Qi ⋅ Vahid Balazadeh ⋅ Michael Cooper ⋅ Russell Greiner ⋅ Rahul G. Krishnan
6:35 PM
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
Jonas Petersen ⋅ Gian-Alessandro Lombardi ⋅ Riccardo Maggioni ⋅ Camilla Mazzoleni ⋅ Federico Martelli ⋅ Philipp C Petersen
6:45 PM
Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs
Nafiseh Ghoroghchian ⋅ Roc Zhang ⋅ Shuyi Han ⋅ Alex Labach ⋅ George Stein
6:55 PM
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Dariush Wahdany ⋅ Jesse Cresswell ⋅ Naiqing Guan ⋅ Atiyeh Ashari ⋅ Franziska Boenisch ⋅ Adam Dziedzic
7:05 PM
Poster Session
8:00 PM
Lunch
10:20 PM
Latent Instructions as Context Surrogates: Enhancing Frozen Time Series Forecasters with Instance-Adaptive Prompts
Zehao Xiao ⋅ Shifeng Xie ⋅ Lei ZAN ⋅ Malik TIOMOKO ⋅ Jianfeng Zhang ⋅ Lujia Pan ⋅ zhang keli ⋅ Ievgen Redko
10:30 PM
Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Images
Jan P Walter ⋅ Shashank Agnihotri ⋅ Margret Keuper
10:40 PM
Training Fair Tabular Foundation Models
Patrik Kenfack ⋅ Jesse Cresswell ⋅ Anthony Caterini ⋅ Samira Ebrahimi Kahou ⋅ Ulrich Aïvodji
10:50 PM
Bounded Context Management for Tabular Foundation Models on Stream Learning
Jinmo Lee ⋅ Doyun Choi ⋅ Moongi Choi ⋅ Jaemin Yoo
11:30 PM
Best Paper Award & Closing Remarks
Nick Erickson ⋅ Lennart Purucker ⋅ Maximilian Schambach
11:45 PM
Poster Session
SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training
Kees Jan de Vries ⋅ Mustafa Radha ⋅ Mathijs de Jong
Mix, Don’t Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Aaryan Nagpal ⋅ Debdeep Sanyal ⋅ Dhruv Kumar ⋅ Murari Mandal ⋅ Saurabh Deshpande
MEG-GPT-2: A Generative Foundation Model for MEG with Structured Neural Representations
SungJun Cho ⋅ Chetan Gohil ⋅ ʻŌiwi Parker Jones ⋅ Mark Woolrich
PerturbPFN: Probing the Limits of Synthetic Priors in Perturbation Modelling
Yuche Gao ⋅ Jose Miguel Hernandez-Lobato ⋅ Siyuan Guo
Feature Space Translation Framework for Cross-Device Alignment and Derivation of Clinically Relevant Retinal Biomarkers from Foundation Model Embeddings
Sparsh Rastogi ⋅ Akshat Bakshi ⋅ Sriharisesh Jidugu ⋅ Imran Razzak ⋅ Jatin Bedi ⋅ Sahil Thakur
Structured Physical Attributes Enable Efficient Foundation Models for Land-Surface Prediction
Nicholas Kraabel ⋅ Jiangtao Liu ⋅ Yuchen Bian ⋅ Daniel Kifer ⋅ Chaopeng Shen
OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
Pietro Belligoli ⋅ Nikita Makarov ⋅ Sayedali Shetab Boushehri ⋅ Fabian Schmich ⋅ Raul Rodriguez-Esteban ⋅ Michael Menden
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Dariush Wahdany ⋅ Jesse Cresswell ⋅ Naiqing Guan ⋅ Atiyeh Ashari ⋅ Franziska Boenisch ⋅ Adam Dziedzic
Nonlinear RNNs as a Compute Shortcut for Time Series Foundation Models
Levente Zolyomi ⋅ David Stap ⋅ Sebastian Böck ⋅ Günter Klambauer ⋅ Sepp Hochreiter
ReGeN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Moulik Gupta ⋅ Aaditya Jain ⋅ Dhruv Kumar ⋅ Murari Mandal ⋅ Saurabh Deshpande
Benchmarking Attention for Tabular Foundation Models
Maximilian Schambach ⋅ Clemens Biehl ⋅ Sam Thelin
Beyond Accuracy: Toward Trustworthy Tabular Foundation Models in Industrial Applications
Johannes von Keler ⋅ Matthias Woehrle ⋅ Jan Achterhold ⋅ Mark Schillinger ⋅ Maria Lyssenko ⋅ Luiz Ricardo Douat
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi ⋅ Eilam Shapira ⋅ Shoham Grunblat ⋅ Mor Ventura ⋅ Elad Hoffer ⋅ Gioia Blayer ⋅ David Holzmüller ⋅ Lennart Purucker ⋅ Gael Varoquaux ⋅ Frank Hutter ⋅ Roi Reichart
Realistic Evaluation of TabPFN v2.5 in Open Environments
Zijian Cheng ⋅ 贾 子怡 ⋅ Lan-Zhe Guo
Agentic Data Intelligence for General Tabular Modeling
Jun-Peng Jiang ⋅ An-Yang Ji ⋅ Jia-Yi Zhu ⋅ Han-Jia Ye
TabPFN-TSRA: Retrieval-Augmented TabPFN for Time-Series Forecasting
Zijian Tang ⋅ Ying Zhang ⋅ Zhenjun Liu ⋅ Sibo Cai ⋅ Shibin Yue ⋅ Meili Zhang
CausalTab: Pretraining Across Causal Environments for Tabular Causal Discovery
Zi-Rong Li ⋅ Si-Yang Liu ⋅ Tian-Zuo Wang ⋅ Han-Jia Ye
LLM-Guided Hard Negative Mining for Structured Product Data Matching
Manoj Chandrashekar Rao ⋅ Mohammed Abraar ⋅ Raj Dandekar ⋅ Prathamesh Dinesh Joshi ⋅ Rajat Dandekar ⋅ Sreedath Panat
Large-Scale Pretraining unlocks Few-Shot Prediction for Relational Data
Rishabh Ranjan ⋅ Vignesh Kothapalli ⋅ Harshvardhan Agarwal ⋅ Charilaos Kanatsoulis ⋅ Roshan R Upendra ⋅ Tom Palczewski ⋅ Carlos Guestrin ⋅ Jure Leskovec
Context-Aware Learning Curve Extrapolation with Prior-Data Fitted Networks
Cheng Yan ⋅ Steven Adriaensen ⋅ Tom Viering
Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
Ayşe Betül Yüce ⋅ Chris J Leffler ⋅ Sarun Varghese ⋅ Myra Spiliopoulou ⋅ Sebastian Stober
In-Context Learning Under Regime Change
Carson Dudley ⋅ Xiaofeng Liu ⋅ Samet Oymak ⋅ Yutong Bi
SimGuard: Context-Aware Anomaly Filtering via Similarity-Guided Error Detection
Amir Raza ⋅ Mayank Jauhari ⋅ Vikash Sharma ⋅ Vipul Joshi ⋅ Boris Oreshkin ⋅ Anurag Tripathi
Pocket Foundation Models: Distilling TFMs into CPU-Ready GBDTs
Aditya Tanna ⋅ Nassim Bouarour ⋅ Mohamed Bouadi ⋅ Vinay k Sankarapu ⋅ Pratinav Seth
Context Window Failures in Relational Foundation Models
Denis Correa ⋅ Francisco Galuppo Azevedo
SurvPFN: Towards Foundation Models for Survival Predictions
Samuel Böhm ⋅ Lennart Purucker ⋅ Frank Hutter ⋅ Pascal Schlosser
Tabular Foundation Models Are Effectively Shallow
Irene Cannistraci ⋅ Julia Vogt
Towards Continuous-time Causal Foundation Models
Dennis Thumm ⋅ Ruben Wiedemann ⋅ Ying Chen
Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs
Nafiseh Ghoroghchian ⋅ Roc Zhang ⋅ Shuyi Han ⋅ Alex Labach ⋅ George Stein
Text-Attention Guided In-Context Forecasting for Multimodal Time Series Prediction
Yuyan Guo ⋅ Mingzhe Zhang ⋅ Vishwesh Ramanathan ⋅ Maryam Motamedi ⋅ Nafiseh Ghoroghchian ⋅ Shuyi Han ⋅ Alex Labach ⋅ Anthony Caterini ⋅ Yi Sui
Benchmarking Multimodal Clinical Foundation Models to Reveal Significant Demographic Disparities
Milan Mohan ⋅ Saketh Lingisetty ⋅ Umar Ahmad ⋅ Avi Kumar ⋅ Shankar Harikrishnan ⋅ Sanjana Chamarty ⋅ Kevin Zhu
Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
Xinghong Fu ⋅ Yanhong Li ⋅ GEORGIOS PAPAIOANNOU ⋅ Yoon Kim
FlexTab: Towards a Flexible Encoder-Decoder Architecture for Tabular In-Context Learning
Marek Polewczyk ⋅ Maximilian Schambach ⋅ Marco Spinaci ⋅ Sam Thelin ⋅ Johannes Höhne
When Simpler ICL Outperforms Pretrained Tabular Foundation Models for RNA Editing
Ran Eisenberg ⋅ Efraim Rahamim ⋅ Erez Levanon ⋅ Ofir Lindenbaum
TARPA: Retrieval Priors for Time-Series Foundation Models on Audience Retention
Dmitriy Petukhov ⋅ Nikolai Kharchevnikov ⋅ Leon Useinov ⋅ Sviatoslav A Stumpf ⋅ Valeriia Efimova
Polynomial Input Preconditioning for Zero-Shot Time Series Forecasting
Jerry Han ⋅ Alex Kawaja ⋅ Benjamin Cole ⋅ Elad Hazan
Dr-CiK: A Testbed for Foresight-Driven Agents
Yihong Tang ⋅ Andrew Williams ⋅ Arjun Ashok ⋅ Vincent Zheng ⋅ Lijun Sun ⋅ Alexandre Drouin ⋅ Issam Laradji ⋅ Étienne Marcotte ⋅ Valentina Zantedeschi
Towards Pretraining Text Encoders for TabPFN
Mustafa Tajjar ⋅ Alexander Tobias Pfefferle ⋅ Lennart Purucker ⋅ Frank Hutter
FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
Karim Othman ⋅ Jonas Petersen ⋅ Matei C Ignuta-Ciuncanu ⋅ Camilla Mazzoleni ⋅ Federico Martelli ⋅ Gian-Alessandro Lombardi ⋅ Riccardo Maggioni ⋅ Philipp C Petersen
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
Srijan Sood ⋅ Maxime Kawawa-Beaudan ⋅ Daniel Borrajo ⋅ Manuela Veloso
Training Fair Tabular Foundation Models
Patrik Kenfack ⋅ Jesse Cresswell ⋅ Anthony Caterini ⋅ Samira Ebrahimi Kahou ⋅ Ulrich Aïvodji
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Devansh Gupta ⋅ Shiv Tavker ⋅ Suchitra Sathyanarayana ⋅ Dmitry Efimov ⋅ Gitanjali Bhutani ⋅ Boris Oreshkin
Can LLMs Use Relational Transformer Embeddings?
Francisco Galuppo Azevedo ⋅ Clarissa L Loures
Latent Instructions as Context Surrogates: Enhancing Frozen Time Series Forecasters with Instance-Adaptive Prompts
Zehao Xiao ⋅ Shifeng Xie ⋅ Lei ZAN ⋅ Malik TIOMOKO ⋅ Jianfeng Zhang ⋅ Lujia Pan ⋅ zhang keli ⋅ Ievgen Redko
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
Rasa Hosseinzadeh ⋅ Alex Labach ⋅ Zexin Xue ⋅ Shuyi Han ⋅ Valentin Thomas ⋅ Anthony Caterini
Covariance-Aware Transformers for Quadratic Programming and Decision Making
Kutay Tire ⋅ Zhang ⋅ Ege Onur Taga ⋅ Samet Oymak
From Time-Series to Text: Multimodal and Agentic Approaches for Automated Drilling Report Generation
Ankush Mishra ⋅ Sebastiaan Buiting ⋅ Soumyadipta Sengupta ⋅ Abdallah Benzine
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
Chenghan Wu ⋅ Zongmin Yu ⋅ Boai Sun ⋅ Liu Yang
Foundation Models for Sparse, Multi-Relational Risk Prediction in Global Supply Chains
Ruohong Li ⋅ George Pu ⋅ James Nordlund ⋅ Prasanth Meiyappan
Are Time Series Foundation Models Ready for Oil and Gas Drilling Anomaly Detection?
Soumyadipta Sengupta ⋅ Abdallah Benzine ⋅ Sebastiaan Buiting ⋅ Ankush Mishra
Retrieval-Augmented Foundation Model Enhances Risk Prediction Using Electronic Health Records
Saeed Shurrab ⋅ Mariam Al-Omari ⋅ Dana E Samad ⋅ Farah E. Shamout
Causal Foundation Models Perform Better without Post-treatment Variables
Junha Ham ⋅ Deokgyu Kim ⋅ Doeun Kim ⋅ Serjin Kim ⋅ Sanghack Lee
Inducing Causal Order through Tabular In-Context Learning
Sascha Xu ⋅ Sarah Mameche ⋅ Jilles Vreeken
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
Jonas Petersen ⋅ Gian-Alessandro Lombardi ⋅ Riccardo Maggioni ⋅ Camilla Mazzoleni ⋅ Federico Martelli ⋅ Philipp C Petersen
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
Yingshuo Wang ⋅ Xian Sun ⋅ Yanhang Li ⋅ Zhichao Fan ⋅ Zexin Zhuang
Mutual Information-Guided Corruption for Improved Self-Supervised Representation Learning in Tabular Data
Michael Lawson ⋅ Emerald Sy ⋅ Kehui Zhang ⋅ Raymond H Chan ⋅ Kannie W Chan ⋅ Rosa Chan
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Lorenzo Longarini ⋅ Alessandro Rongoni ⋅ Simone Silenzi ⋅ Emanuele Frontoni ⋅ Riccardo Rosati
Adapt the Data, Not the Model: Input-Space Adaptation for Frozen Time-Series Predictors
Omer Gotfrid ⋅ Dvir Aran ⋅ Barak Gahtan ⋅ Alex Bronstein
\plurel-to-\rdbpfn: Schema-Guided Synthetic Relational Pretraining
Mohammad S Abolhasani ⋅ Viswanath Ganapathy
A Generative Foundation Model for Heterogeneous Tabular Data
Xiangjian Jiang ⋅ Mingxuan Liu ⋅ Nikola Simidjievski ⋅ Tassilo Klein ⋅ Mateja Jamnik
Language Pretraining Gives Structured Forecasters a Sequential Prior
Zhenghan Tai ⋅ Alexis Roger ⋅ Prateek Humane ⋅ Gwen Legate ⋅ Andrei Mircea ⋅ Vasilii Feofanov ⋅ Irina Rish
What You Pretrain On Matters: Synthetic Task Distributions Determine Tabular Foundation Model Quality
Mohamed Bouadi ⋅ Nassim Bouarour ⋅ Shivam Dubey ⋅ Varun Kulkarni ⋅ Aditya Tanna ⋅ Vinay k Sankarapu
Speedrunning Tabular Foundation Model Pretraining
Salih Bora Öztürk ⋅ Alexander Tobias Pfefferle ⋅ Frank Hutter
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
Günther Schindler ⋅ Maximilian Schambach ⋅ Johannes Höhne
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany ⋅ Matthew Jagielski ⋅ Jesse Cresswell ⋅ Adam Dziedzic ⋅ Franziska Boenisch
Inspectable Tabular Foundation Models via In-Context Kernel Learning
Ratmir Miftachov ⋅ Bruno Charron ⋅ Simon Valentin
Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
Myung Jun Kim ⋅ Maximilian Schambach ⋅ Frank Essenberger ⋅ Andre Sres ⋅ Johannes Höhne
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
Shi-ang Qi ⋅ Vahid Balazadeh ⋅ Michael Cooper ⋅ Russell Greiner ⋅ Rahul G. Krishnan
TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
Duong Nguyen ⋅ Mohammed Jawhar ⋅ Nicolas CHESNEAU
Towards Benchmarking Time Series Foundation Models on Native Scientific Data
Lewis ODonnell ⋅ Arinbjörn Kolbeinsson ⋅ Benedikt Kolbeinsson ⋅ Marc Deisenroth
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
Rui Wang ⋅ Renhao Xue ⋅ Ray Razi ⋅ Huan Song ⋅ Hannah R Marlowe
BALMS: Benchmarking Agentic LLMs for Mental Health Sensing
Yu Y Wu ⋅ Arvind Pillai ⋅ Yuliang Chen ⋅ Sudarshan Regmi ⋅ Tess Z Griffin ⋅ Michael V Heinz ⋅ Lisa Marsch ⋅ Nicholas C Jacobson ⋅ Andrew Campbell
FairOpt-PFN: Amortized Counterfactual Fairness with Optimal Fair Targets
Enes Hasani ⋅ Jake Robertson ⋅ Frank Hutter
Semantics or Structure? Auditing Text Sensitivity in Multimodal Time-Series Forecasting
Karthik Sridhar ⋅ Atharva Gupta ⋅ Nishant Pradhan ⋅ Murari Mandal ⋅ Dhruv Kumar ⋅ Saurabh Deshpande
Benchmarking Tabular Foundation Models for Churn Prediction
Sobhan Seyedzadeh ⋅ Sobhan Seyedzadeh ⋅ Mostafa Karimi
SymboLLM-FE: LLM Accelerated Symbolic Regression for Automated Feature Engineering
Zijian Cheng ⋅ 贾 子怡 ⋅ Zhi Zhou ⋅ Yu-Feng Li ⋅ Lan-Zhe Guo
Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function
Atharva Gupta ⋅ Dhruv Kumar ⋅ Murari Mandal ⋅ Saurabh Deshpande
Bounded Context Management for Tabular Foundation Models on Stream Learning
Jinmo Lee ⋅ Doyun Choi ⋅ Moongi Choi ⋅ Jaemin Yoo
Optimizing Pre-Training of Tabular Foundation Models by Shaping Geometry
Humzah Merchant ⋅ Sriniketh Vangaru ⋅ Randall Balestriero
Beyond Task-Specific Classifiers: In-Context Inference for Time Series Classification Foundation Models
Juntao Fang ⋅ Shifeng Xie ⋅ Shengbin Nie ⋅ Yuhui Ling ⋅ Yuming Liu ⋅ Zijian Li ⋅ zhang keli ⋅ Lujia Pan ⋅ Themis Palpanas ⋅ Ruichu Cai
Towards Evaluating Data Priors for Tabular Foundation Models
Zeynep Türkmen ⋅ Kürşat Kaya ⋅ Alexander Tobias Pfefferle ⋅ Frank Hutter
A Causal Foundation Model for Structure and Outcome Prediction
Max Zhu ⋅ Martino Mansoldo ⋅ Chinghao Wang ⋅ Stefan Groha
A Multi-Agent Pipeline for Source-Grounded Synthetic Note Generation from Longitudinal Structured EHR
Nina Fatehi ⋅ Reihaneh Hassanzadeh ⋅ Meysam Ghaffari ⋅ Animesh Agarwal ⋅ Carlos Morato
Ensembling Tabular Foundation Models: A Diversity Ceiling and a Calibration Trap
Aditya Tanna ⋅ Yash J Desai ⋅ Pratinav Seth ⋅ Mohamed Bouadi ⋅ Nassim Bouarour ⋅ Vinay k Sankarapu
HGR-TabE: Universal Tabular Embeddings via Maximal Correlation Alignment
Niharika D'Souza ⋅ Liane Vogel ⋅ Kavitha Srinivas ⋅ Sola Shirai ⋅ Oktie Hassanzadeh ⋅ Horst Samulowitz
Lookahead Automated Feature Engineering for Tabular Prediction via Kaggle-Guided Knowledge Transfer
Si-Yang Liu ⋅ Zong-Da Li ⋅ Chenming Xu ⋅ Han Li ⋅ Rui-Qiao Chen ⋅ Han-Jia Ye
Revisiting Metafeatures to Explain Model Differences on Tabular Data
Markus Herre ⋅ Andrej Tschalzev ⋅ Sascha Marton ⋅ Christian Bartelt
Evolutionary Feature Engineering for Structured Data
Ege Onur Taga ⋅ Yilin Zhuang ⋅ Muhammed Emrullah Ildiz ⋅ Petros Mol ⋅ Abhimanyu Das ⋅ Karthik Duraisamy ⋅ Samet Oymak
Causal Foundation Models with Continuous Treatments
Christopher Stith ⋅ Medha Barath ⋅ Vahid Balazadeh ⋅ Jesse Cresswell ⋅ Rahul G. Krishnan
Causal Foundation Models for Time Series based on Prior-Data fitted Networks
Dennis Thumm ⋅ Arik Reuter ⋅ Jake Robertson ⋅ Shi Bin Hoo ⋅ Adrian Weller ⋅ Frank Hutter ⋅ Ying Chen ⋅ Bernhard Schölkopf
Implicit Reward Alignment For Training Causally-Coherent Tabular Data Generators
Matea Gjika ⋅ Giuseppe Iannone ⋅ Luca Sfragara ⋅ Pavithra Harsha ⋅ Georgia Perakis
GITCO: Gated Inference-Time Context Optimization in TSFMs
Manya Pandey ⋅ Dhruv Kumar ⋅ Murari Mandal ⋅ Saurabh Deshpande
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Franco Martino ORourke ⋅ Ana Trisovic ⋅ Dimitris Bertsimas
Do Tabular Foundation Models Learn Rules or Memorize Exemplars?
Amir Rezaei Balef ⋅ Mykhailo Koshil ⋅ Behzad Nourani-Koliji ⋅ Katharina Eggensperger
SCBench: A Testbed for Causal Inference with Time Series Panel Data
Megan Richards ⋅ Saeyoung Rho ⋅ Kyunghyun Cho
PRAGMA: A Foundation Model for Banking Event Sequences
Maxim Ostroukhov ⋅ Ruslan Mikhailov ⋅ Vladimir Iashin ⋅ Artem Sokolov ⋅ Andrei Akshonov ⋅ Vitaly Protasov ⋅ Dmitrii Beloborodov ⋅ Vince Mullin ⋅ Roman Enzmann ⋅ Georgios Kolovos ⋅ Jason Renders ⋅ Pavel Nesterov ⋅ Anton Repushko
Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification
Samuel McDowell ⋅ Nathan Stromberg ⋅ Lalitha Sankar
Probing Clinical Concepts in an EHR Foundation Model via Sparse Autoencoders
Shashank Yadav ⋅ David M Routman ⋅ Andrew Y. K. Foong
Learned Sequence Representations over Raw Credit Events for Credit-Abuse Scoring
Tianming Zhou ⋅ Jiarui Xu ⋅ Nitesh Kumar ⋅ Alexander Statnikov
Impact-Driven Event Covariates for Time-Series Foundation Models
Elham Alipour ⋅ xiaoli zhang ⋅ Boyang Jin
Are Tabular Foundation Model Rankings Reliable? A Generalizability Theory Analysis of RelBench and DBInfer
Dinesh Katupputhur Ramprasath ⋅ Tom Palczewski ⋅ Joe Meyer ⋅ Roshan R Upendra ⋅ Minghua Li
Probing Memorization of Tabular In-Context Learning
Francesco Capano ⋅ Jonas Böhler
On the Uncertainty in Prior-Data Fitted Network Pretraining
Manuel Hülskamp ⋅ Julius Kobialka ⋅ Emanuel Sommer ⋅ David Rügamer
Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
Benjamin Redhead ⋅ Thomas Lee ⋅ Peng Gu ⋅ Víctor Elvira ⋅ Amos Storkey
RAD-TFM: Robust and Domain-Adapted Tabular Foundation Models
Matthew Peroni ⋅ Franck Le ⋅ Vadim Sheinin
Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
Andrej Tschalzev ⋅ Stefan Lüdtke ⋅ Heiner Stuckenschmidt ⋅ Christian Bartelt
TSQueryBench: LLM-as-a-Judge for Time-Series Explanations
Preetham Sivalingam ⋅ Murari Mandal ⋅ Dhruv Kumar ⋅ Saurabh Deshpande
Towards Effective LLM Reasoning for Time Series Classification
Jiahui Zhou ⋅ Dan Li ⋅ Wenjie Feng ⋅ Lin Li ⋅ Jian Lou ⋅ See-Kiong Ng
Beyond Average Leaderboards: When Explicit Graph Priors Help Tabular Foundation Models
Franck Le ⋅ Keith Grueneberg ⋅ Erich M Nahum ⋅ Vadim Sheinin
TiViT: Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers
Simon Roschmann ⋅ Quentin Bouniot ⋅ Vasilii Feofanov ⋅ Ievgen Redko ⋅ Zeynep Akata
DataSynK: Causal-Symbolic EHR Synthesis for Tabular Foundation Models in Low-Resource Settings
Eduarda Chagas ⋅ Roberta Viola ⋅ Juarez Monteiro ⋅ Francisco Galuppo Azevedo ⋅ Saulo F Saturnino ⋅ Adriano Veloso
TableFactory: Generating Semantically Linked Tabular Data via Multi-Agent Behavioral Simulation
Mingxuan Liu ⋅ Xiangjian Jiang ⋅ Johannes Hoffart ⋅ Tassilo Klein
Bayesian Tabular Few-shot Learning with Causal Information
Ole Ossen ⋅ Jake Robertson ⋅ Arik Reuter ⋅ Magnus Bühler ⋅ Lennart Purucker ⋅ Frank Hutter
Learning to Cool: State Space Models for Smarter Automobile AC Systems
Forrest Felsch ⋅ Sam Mikhak ⋅ Taehyung Wang
EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining
Payal Chandak ⋅ Gregory Kondas ⋅ Isaac S Kohane ⋅ Matthew McDermott
Memory Efficient Tabular Foundation Models
Shuting Luo ⋅ Monika M Kanaan ⋅ Cameron Gordon ⋅ Anna Leontjeva ⋅ Simon Lucey
Should LLMs Explain Every Time-Series Alert? A Reliability-Routed Audit for Structured Data Monitoring
WANG Yibo ⋅ Shuai Wang ⋅ Zhang Ting ⋅ Huang Runqing ⋅ Tsin H Koo ⋅ Zhiqiang Cao