5 citations · 9 across the 18 of their papers we have counts for
4 papers · 1 filter
How Reusable Are Benchmarks with Richer Feedback?
Youssef Allouah, John Duchi
We study whether benchmarks reliably guide model selection as developers adapt to evaluation feedback across multiple criteria. We find that the worst-case test-set size needed to…
What to Forget in Unlearning? Forget Set Curation for Language Models
Animesh Jha, Arpandeep Khatua, Youssef Allouah +1
Machine unlearning aims to remove targeted data or behaviors from a trained model without retraining from scratch. Yet most evaluations assume that the examples to forget are alrea…
The Distillation Game: Adaptive Evaluations & Efficient Defenses
Youssef Allouah, Mahdi Haghifam, Sanmi Koyejo +1
Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitate. We study this trade-off t…
Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
Chengxi Li, Youssef Allouah, Rachid Guerraoui +2
In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation r…