3 citations · 4 across the 7 of their papers we have counts for
8 papers
Position: Capability Control Should be a Separate Goal From Alignment
Shoaib Ahmed Siddiqui, Eleni Triantafillou, David Krueger +1
Foundation models are trained on broad data distributions, yielding generalist capabilities that enable many downstream applications but also expand the space of potential misuse a…
From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization
Shoaib Ahmed Siddiqui, Adrian Weller, David Krueger +3
Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a small set of (even seemingly-unrelated) e…
Integrating uncertainty quantification into randomized smoothing based robustness guarantees
Sina Däubener, Kira Maag, David Krueger +1
Deep neural networks have proven to be extremely powerful, however, they are also vulnerable to adversarial attacks which can cause hazardous incorrect predictions in safety-critic…
Exploring the design space of deep-learning-based weather forecasting systems
Shoaib Ahmed Siddiqui, Jean Kossaifi, Boris Bonev +4
Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well u…
Permissive Information-Flow Analysis for Large Language Models
Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf +7
Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from on…
Input Space Mode Connectivity in Deep Neural Networks
Jakub Vrabel, Ori Shem-Ur, Yaron Oz +1
We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Mode connectivity was originally studied within parameter space, where it desc…
Protecting against simultaneous data poisoning attacks
Neel Alex, Shoaib Ahmed Siddiqui, Amartya Sanyal +1
Current backdoor defense methods are evaluated against a single attack at a time. This is unrealistic, as powerful machine learning systems are trained on large datasets scraped fr…
A deeper look at depth pruning of LLMs
Shoaib Ahmed Siddiqui, Xin Dong, Greg Heinrich +4
Large Language Models (LLMs) are not only resource-intensive to train but even more costly to deploy in production. Therefore, recent work has attempted to prune blocks of LLMs bas…