39 citations · 143 across the 29 of their papers we have counts for
10 papers · 1 filter
Continual Learning via Self-Probe Gradients
Dongkyu Cho, Rumi Chunara, Sungmin Cha
Adapting pretrained models to new data can cause catastrophic forgetting of previously learned behavior. When only a few past samples remain, they give continual learning methods s…
Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay
Martin Marek, Dongkyu Cho, Shikai Qiu +3
Models trained on a new task typically degrade on prior tasks, a phenomenon known as forgetting. Traditionally, mitigating forgetting has required replaying stored exemplars from p…
Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
Dongkyu Cho, Miao Zhang, Rumi Chunara
Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs…
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations
Dongkyu Cho, Rumi Chunara
Data augmentation is a promising tool for enhancing out-of-distribution generalization, where the key is to produce diverse, challenging variations of the source domain via costly…
Forget Forgetting: Continual Learning in a World of Abundant Memory
Dongkyu Cho, Taesup Moon, Rumi Chunara +2
Continual learning (CL) has traditionally focused on minimizing exemplar memory, a constraint often misaligned with modern systems where GPU time, not storage, is the primary bottl…
Disparate Effect Of Missing Mediators On Transportability of Causal Effects
Vishwali Mhasawade, Rumi Chunara
Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when ap…
Understanding Disparities in Post Hoc Machine Learning Explanation
Vishwali Mhasawade, Salman Rahman, Zoe Haskell-Craig +1
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while…
Causal Multi-Level Fairness
Vishwali Mhasawade, Rumi Chunara
Algorithmic systems are known to impact marginalized groups severely, and more so, if all sources of bias are not considered. While work in algorithmic fairness to-date has primari…
Fairness Violations and Mitigation under Covariate Shift
Harvineet Singh, Rina Singh, Vishwali Mhasawade +1
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set. Stability against changes in data distribution is an import…
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Nabeel Abdur Rehman, Maxwell Matthaios Aliapoulios, Disha Umarwani +1
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection fr…