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Double announcement!
🤖 TabPack: an effective, practical baseline for medium-to-large tabular datasets. Competitive out of the box on benchmarks and trainable on a modern MacBook
📦 TabPackage: a Python package for training TabPack and building similar models
👇 Links & ThreadView quoted post on X
This Monday, Nov 2, Dmitry Baranchuk (@DmitryBaranchuk) will present his paper:
Representation-Space MMD for Diffusion Language Models
Project also co-led by Ilya Drobyshevskiy (@free001style) and Ilia Sudakov (@sudakov_il)!View quoted post on X
I've long argued for more diverse evaluation setups in tabular ML research. This will continue. It was a key motivation behind our new paper
A thread on data scaling in MLPs, GBDTs and tabular foundation models, and what it means for generalization.
arxiv.org/abs/2610.26794View quoted post on X
arxiv.org Revisiting Tabular Deep Regression: Objectives and the Limits of... While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom.... 
arxiv.org Representation-Space MMD for Diffusion Language Models We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference distributions in the feature space of a... Introducing a simple method to post-train both discrete and continuous diffusion LMs.
It's based on Maximum Mean Discrepancy (MMD) that matches token-level feature distributions. Our method improves generation and speed on OWT/GSM8K and scales to 16B models. 🧵View quoted post on X
