Pretrained deep models outperform GBDTs in Learning-To-Rank under label
scarcity

Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity

31 July 2023

K. K. Thekumparampil

Michael Shavlovsky

Yesh Dattatreya

Sujay Sanghavi

Papers citing "Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity"

9 / 9 papers shown

Title
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization Alan Jeffares Tennison Liu Jonathan Crabbé F. Imrie M. Schaar CML 50 22 0 09 Mar 2023
Toward Understanding Privileged Features Distillation in Learning-to-Rank Shuo Yang Sujay Sanghavi Holakou Rahmanian J. Bakus S.V.N. Vishwanathan 118 14 0 19 Sep 2022
PTab: Using the Pre-trained Language Model for Modeling Tabular Data Guangyi Liu Jie-jin Yang Ledell Yu Wu LMTD 65 34 0 15 Sep 2022
Transfer Learning with Deep Tabular Models Roman Levin Valeriia Cherepanova Avi Schwarzschild Arpit Bansal C. Bayan Bruss Tom Goldstein A. Wilson Micah Goldblum OOD FedML LMTD 75 58 0 30 Jun 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 325 4,077 0 24 May 2022
Understanding self-supervised Learning Dynamics without Contrastive Pairs Yuandong Tian Xinlei Chen Surya Ganguli SSL 138 281 0 12 Feb 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Y. S. Rawat M. Shah 238 509 0 15 Jan 2021
TabTransformer: Tabular Data Modeling Using Contextual Embeddings Xin Huang A. Khetan Milan Cvitkovic Zohar Karnin ViT LMTD 157 417 0 11 Dec 2020
Pretrained Transformers for Text Ranking: BERT and Beyond Jimmy J. Lin Rodrigo Nogueira Andrew Yates VLM 224 611 0 13 Oct 2020