activity
20142023
most citedRethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning

12 citations · 26 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR2023

RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction

Song-Li Wu, Liang Du, Jia-Qi Yang +4

Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user. Most recent cutting-edge methods…

cs.CL2023★ 1 cited

BatchPrompt: Accomplish more with less

Jianzhe Lin, Maurice Diesendruck, Liang Du +1

As the ever-increasing token limits of large language models (LLMs) have enabled long context as input, prompting with single data samples might no longer an efficient way. A strai…

cs.IR2023

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction

Chen Zhu, Liang Du, Hong Chen +4

Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditio…

cs.CV2023★ 12 cited

Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning

Jiayi Han, Longbin Zeng, Liang Du +2

In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distr…

cs.LG2021★ 2 cited

Multilingual Molecular Representation Learning via Contrastive Pre-training

Zhihui Guo, Pramod Sharma, Andy Martinez +2

Molecular representation learning plays an essential role in cheminformatics. Recently, language model-based approaches have gained popularity as an alternative to traditional expe…

cs.LG2015

Unsupervised Feature Selection with Adaptive Structure Learning

Liang Du, Yi-Dong Shen

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrin…

cs.LG2014★ 11 cited

Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval

Liang Wu, Hui Xiong, Liang Du +6

The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been…