158 citations · 343 across the 75 of their papers we have counts for
12 papers · 1 filter
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
Cheng Zhang, Jianyi Cheng, Ilia Shumailov +2
The inference of Large language models (LLMs) requires immense computation and memory resources. To curtail these costs, quantisation has merged as a promising solution, but existi…
Beyond Labeling Oracles: What does it mean to steal ML models?
Avital Shafran, Ilia Shumailov, Murat A. Erdogdu +1
Model extraction attacks are designed to steal trained models with only query access, as is often provided through APIs that ML-as-a-Service providers offer. Machine Learning (ML)…
Human-Producible Adversarial Examples
David Khachaturov, Yue Gao, Ilia Shumailov +3
Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers…
SEA: Shareable and Explainable Attribution for Query-based Black-box Attacks
Yue Gao, Ilia Shumailov, Kassem Fawaz
Machine Learning (ML) systems are vulnerable to adversarial examples, particularly those from query-based black-box attacks. Despite various efforts to detect and prevent such atta…
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?
David Glukhov, Ilia Shumailov, Yarin Gal +2
Large language models (LLMs) have exhibited impressive capabilities in comprehending complex instructions. However, their blind adherence to provided instructions has led to concer…
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
Anvith Thudi, Hengrui Jia, Casey Meehan +2
Differentially private stochastic gradient descent (DP-SGD) is the canonical approach to private deep learning. While the current privacy analysis of DP-SGD is known to be tight in…