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Posts by Collection
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publications
On robust vs fast solving of qualitative constraints
This paper introduces the notion of Robustness to Qualitative Constraint Networks and finds a tradeoff between speed and robustness in heuristics for solving QCNs.
Explaining Learned Reward Functions with Counterfactual Trajectories
We propose a method for explaining reward functions by showing the rewards given to counterfactual trajectories.
Immunization against harmful fine-tuning attacks
LLMs can be fine-tuned with harmful data to remove their safeguards. We formalize the problem and set out conditions for a solution.
Representation Noising: A Defence Mechanism Against Harmful Finetuning
We propose Representation Noising which prevents harmful fine-tuning by removing harmful representations.
Position: Safety Must Precede the Deployment of Open-Ended AI Agents
Open-ended AI is a growing paradigm where AI continuously explores novel and interesting artifacts. This position paper describes specific safety challenges in Open-Ended AI and how they can be mitigated.
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
This survey paper reviews the literature on Representation Engineering, a technique for controlling LLMs through their internal representations. We set out a unifying taxonomy, describe methods and applications and showcase weaknesses and opportunities.
Detecting Offensive Cyber Agents: A Detection-in-Depth Approach
AI agents can now orchestrate cyberattacks, increasing their speed, scale, and autonomy while lowering costs. This report frames the offensive cyber agent detection challenge, introduces detection-in-depth as a strategic framework for policymakers and defenders, and proposes five concrete detection mechanisms.
talks
Talk 1 on Relevant Topic in Your Field
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Tutorial 1 on Relevant Topic in Your Field
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Talk 2 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
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Teaching experience 2
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