93 citations · 446 across the 32 of their papers we have counts for
5 papers · 1 filter
SelecMix: Debiased Learning by Contradicting-pair Sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak +4
Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labe…
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
Damien Teney, Yong Lin, Seong Joon Oh +1
Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation a…
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
Damien Teney, Maxime Peyrard, Ehsan Abbasnejad
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model,…
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
Violetta Shevchenko, Ehsan Abbasnejad, Anthony Dick +2
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vi…
Active Learning by Feature Mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney +3
The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is e…