Search Results for author: Nigel Shadbolt

Found 11 papers, 2 papers with code

Projected Subnetworks Scale Adaptation

no code implementations27 Jan 2023 Siddhartha Datta, Nigel Shadbolt

Large models support great zero-shot and few-shot capabilities.

Multiple Modes for Continual Learning

no code implementations29 Sep 2022 Siddhartha Datta, Nigel Shadbolt

Adapting model parameters to incoming streams of data is a crucial factor to deep learning scalability.

Continual Learning

Interpolating Compressed Parameter Subspaces

no code implementations19 May 2022 Siddhartha Datta, Nigel Shadbolt

Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution).

Continual Learning

Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks

no code implementations7 Mar 2022 Siddhartha Datta, Nigel Shadbolt

clean labels, which motivates this paper's work on the construction of multi-agent backdoor defenses that maximize accuracy w. r. t.

Backdoor Attack

Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire

no code implementations28 Jan 2022 Siddhartha Datta, Nigel Shadbolt

Malicious agents in collaborative learning and outsourced data collection threaten the training of clean models.

Backdoor Attack

Hiding Behind Backdoors: Self-Obfuscation Against Generative Models

no code implementations24 Jan 2022 Siddhartha Datta, Nigel Shadbolt

Attack vectors that compromise machine learning pipelines in the physical world have been demonstrated in recent research, from perturbations to architectural components.

BIG-bench Machine Learning

Widen The Backdoor To Let More Attackers In

no code implementations9 Oct 2021 Siddhartha Datta, Giulio Lovisotto, Ivan Martinovic, Nigel Shadbolt

As collaborative learning and the outsourcing of data collection become more common, malicious actors (or agents) which attempt to manipulate the learning process face an additional obstacle as they compete with each other.

Backdoor Attack

Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation

no code implementations20 Jan 2021 Nitin Agrawal, Reuben Binns, Max Van Kleek, Kim Laine, Nigel Shadbolt

Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis.

Human-Computer Interaction

Like trainer, like bot? Inheritance of bias in algorithmic content moderation

1 code implementation5 Jul 2017 Reuben Binns, Michael Veale, Max Van Kleek, Nigel Shadbolt

This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence.


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