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Achievement: Pair Extraordinairex2Achievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: Pair Extraordinairex2Achievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

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@NeuralLikelihoodFreeInference @Kinds-of-Intelligence-CFI

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LoryPack/README.md

Hi there 👋

I am a Research Associate at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where I work on predictability and cognitive-oriented evaluation of AI systems, together with Prof José Hernández-Orallo and Dr Lucy Cheke.

I previously worked on detecting lying in large language models with Dr Owain Evans and on technical standards for AI for the EU AI Act at the Future of Life Institute. I am deeply interested in AI policy (particularly at the EU level).

I obtained a PhD in Statistics and Machine Learning at Oxford, during which I worked on Bayesian simulation-based inference, generative models and probabilistic forecasting (with applications to meteorology). My supervisors were Prof. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford).

Before my PhD studies, I obtained a Bachelor's degree in Physical Engineering from Politecnico di Torino (Italy) and an MSc in Physics of Complex Systems from Politecnico di Torino and Université Paris-Sud, France. I carried out my MSc thesis at LightOn, a machine learning startup in Paris.

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  1. LLM-LieDetector LLM-LieDetector Public

    Code for the ICLR 2024 paper "How to catch an AI liar: Lie detection in black-box LLMs by asking unrelated questions"

    Jupyter Notebook 62 9

  2. eth-cscs/abcpy eth-cscs/abcpy Public

    ABCpy package

    Python 113 34

  3. BPMF BPMF Public

    Python implementation of Bayesian Probabilistic matrix Factorization algorithm.

    Jupyter Notebook 23 11

  4. GenerativeNetworksScoringRulesProbabilisticForecasting GenerativeNetworksScoringRulesProbabilisticForecasting Public

    Code for the paper "Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization"

    Python 8 2

  5. ReferenceInstancesPredictability ReferenceInstancesPredictability Public

    Code for the paper "100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances"

    Jupyter Notebook 1

  6. Kinds-of-Intelligence-CFI/KindsOfReasoning Kinds-of-Intelligence-CFI/KindsOfReasoning Public

    KindsOfReasoning collection of datasets and results

    Jupyter Notebook 1 1