Search Results for author:
Found 11 papers, 2 papers with code
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks.
Improving Fair Training under Correlation Shifts
First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness.
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications.
Sample Selection for Fair and Robust Training
In this work, we propose a sample selection-based algorithm for fair and robust training.
Responsible AI Challenges in End-to-end Machine Learning
Second, responsible AI must be broadly supported, preferably in all steps of machine learning.
FairBatch: Batch Selection for Model Fairness
We address this problem via the lens of bilevel optimization.
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification.
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
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 data bias and poisoning.
FR-GAN: Fair and Robust Training
We consider the problem of fair and robust model training in the presence of data poisoning.
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks.
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data.
