5 citations · 12 across the 5 of their papers we have counts for
6 papers
The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
The fine-tuning of large vision-language foundation models remains an underexplored area, particularly regarding its impact on learning gains and catastrophic forgetting. Inspired…
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis
Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task…
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Laura Niss, Yuekai Sun, Ambuj Tewari
Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic bia…
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
Laura Niss, Ambuj Tewari
Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with -contaminated rewards. We allow an adversary to g…
Debiasing representations by removing unwanted variation due to protected attributes
Amanda Bower, Laura Niss, Yuekai Sun +1
We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficien…
Fair Pipelines
Amanda Bower, Sarah N. Kitchen, Laura Niss +3
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fair…