152 citations · 265 across the 12 of their papers we have counts for
13 papers
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective
Yuening Li, Zeyu Wang, Zhen Zhang +8
Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions, faces critical stability chal…
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Qingyun Liu, Bo Yan, Yang Liu +15
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emergi…
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
Soyeon Kim, Yuji Roh, Geon Heo +1
Generative models must ensure both privacy and fairness for Trustworthy AI. While these goals have been pursued separately, recent studies propose to combine existing privacy and f…
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui +8
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various task…
Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data di…
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
Steven Euijong Whang, Yuji Roh, Hwanjun Song +1
Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. H…
Sample Selection for Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning…
Responsible AI Challenges in End-to-end Machine Learning
Steven Euijong Whang, Ki Hyun Tae, Yuji Roh +1
Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve…
FairBatch: Batch Selection for Model Fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preproce…
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
Geon Heo, Yuji Roh, Seonghyeon Hwang +2
As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially…
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of d…
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
Ki Hyun Tae, Yuji Roh, Young Hun Oh +2
The wide use of machine learning is fundamentally changing the software development paradigm (a.k.a. Software 2.0) where data becomes a first-class citizen, on par with code. As ma…
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
Yuji Roh, Geon Heo, Steven Euijong Whang
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a…