Accepted Papers

Orals

• Analysis of the Attention in Tabular Language Models [recording]
  Aneta Koleva, Martin Ringsquandl, Volker Tresp

• Transfer Learning with Deep Tabular Models [recording]
  Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

• STable: Table Generation Framework for Encoder-Decoder Models [recording]
  Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Pałka, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek

• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second [recording]
  Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

• Towards Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning [recording]
  David Vos, Till Döhmen, Sebastian Schelter

• RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild [recording]
  Weiyao Wang, Byung-Hak Kim, Varun Ganapathi

Posters

• SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
  Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, Tom Goldstein

• Generic Entity Resolution Models
  Jiawei Tang, Yifei Zuo, Lei Cao, Samuel Madden

• Towards Foundation Models for Relational Databases [video pitch]
  Liane Vogel, Benjamin Hilprecht, Carsten Binnig

• Diffusion models for missing value imputation in tabular data [video pitch]
  Shuhan Zheng, Nontawat Charoenphakdee

• STab: Self-supervised Learning for Tabular Data
  Ehsan Hajiramezanali, Max W Shen, Gabriele Scalia, Nathaniel Lee Diamant

• CASPR: Customer Activity Sequence based Prediction and Representation
  Damian Konrad Kowalczyk, Pin-Jung Chen, Sahil Bhatnagar

• Conditional Contrastive Networks
  Emily Mu, John Guttag

• Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers
  Rajat Agarwal, Anand Muralidhar, Agniva Som, Hemant Kowshik

• The Need for Tabular Representation Learning: An Industry Perspective
  Joyce Cahoon, Alexandra Savelieva, Andreas C Mueller, Avrilia Floratou, Carlo Curino, Hiren Patel, Jordan Henkel, Markus Weimer, Roman Batoukov, Shaleen Deep, Venkatesh Emani, Richard Wydrowski, Nellie Gustafsson

• STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
  Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, Jinwoo Shin

• Tabular Data Generation: Can We Fool XGBoost?
  EL Hacen Zein, Tanguy Urvoy

• SiMa: Federating Data Silos using GNNs
  Anonymized

• Self Supervised Pre-training for Large Scale Tabular Data
  Sharad Chitlangia, Anand Muralidhar, Rajat Agarwal

• RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training
  Zui Chen, Lei Cao, Samuel Madden

• MapQA: A Dataset for Question Answering on Choropleth Maps
  Shuaichen Chang, David Palzer, Jialin Li, Eric Fosler-Lussier, Ningchuan Xiao

• MET: Masked Encoding for Tabular Data
  Kushal Alpesh Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain

• Active Learning with Table Language Models
  Martin Ringsquandl, Aneta Koleva

• Structural Embedding of Data Files with MAGRITTE [video pitch]
  Gerardo Vitagliano, Mazhar Hameed, Felix Naumann