DeepAI AI Chat
Log In Sign Up

Learning List-Level Domain-Invariant Representations for Ranking

12/21/2022
by   Ruicheng Xian, et al.
University of Massachusetts Amherst
University of Illinois at Urbana-Champaign
0

Domain adaptation aims to transfer the knowledge acquired by models trained on (data-rich) source domains to (low-resource) target domains, for which a popular method is invariant representation learning. While they have been studied extensively for classification and regression problems, how they apply to ranking problems, where the data and metrics have a list structure, is not well understood. Theoretically, we establish a domain adaptation generalization bound for ranking under listwise metrics such as MRR and NDCG. The bound suggests an adaptation method via learning list-level domain-invariant feature representations, whose benefits are empirically demonstrated by unsupervised domain adaptation experiments on real-world ranking tasks, including passage reranking. A key message is that for domain adaptation, the representations should be analyzed at the same level at which the metric is computed, as we show that learning invariant representations at the list level is most effective for adaptation on ranking problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/28/2019

Deep causal representation learning for unsupervised domain adaptation

Studies show that the representations learned by deep neural networks ca...
10/15/2022

Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation

Unsupervised representation learning for speech audios attained impressi...
12/17/2014

Learning unbiased features

A key element in transfer learning is representation learning; if repres...
10/09/2020

Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation

The success of supervised learning hinges on the assumption that the tra...
08/31/2022

Transfering Low-Frequency Features for Domain Adaptation

Previous unsupervised domain adaptation methods did not handle the cross...
04/05/2021

Reducing Racial Bias in Facial Age Prediction using Unsupervised Domain Adaptation in Regression

We propose an approach for unsupervised domain adaptation for the task o...
11/16/2017

Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment

A novel approach for unsupervised domain adaptation for neural networks ...