44 citations · 45 across the 3 of their papers we have counts for
6 papers
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Deqing Fu, Huangyuan Su, Rajat Sen +4
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliar…
TabFM: A Zero-Shot Foundation Model for Tabular Data
Weihao Kong, Erez Louidor Ilan, Shuxin Nie +6
Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-paramete…
A Combinatorial Approach to Robust PCA
Weihao Kong, Mingda Qiao, Rajat Sen
We study the problem of recovering Gaussian data under adversarial corruptions when the noises are low-rank and the corruptions are on the coordinate level. Concretely, we assume t…
Transformers can optimally learn regression mixture models
Reese Pathak, Rajat Sen, Weihao Kong +1
Mixture models arise in many regression problems, but most methods have seen limited adoption partly due to these algorithms' highly-tailored and model-specific nature. On the othe…
A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen +1
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot…
Linear Regression using Heterogeneous Data Batches
Ayush Jain, Rajat Sen, Weihao Kong +2
In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relations…