Nino Scherrer

Fields of Interest

Research

> Language Model Evaluations

• Evaluating the Moral Beliefs Encoded in LLMs
  Nino Scherrer*, Claudia Shi*, Amir Feder, and David Blei
  NeurIPS 2023 (Spotlight)
  
  Paper Poster Code Dataset

  LLM Evaluations

  AI Safety / AI Ethics

  Consistency / Uncertainty

  
• SimpleSafetyTests: A Test Suite for Identifying Critical Safety Risks in Large Language Models
  Bertie Vidgen, Nino Scherrer, Hannah Rose Kirk, Rebecca Qian, Anand Kannappan, Scott A Hale and Paul Röttger
  Arxiv Preprint, 2023
  
  Paper Dataset Press

  LLM Evaluations

  AI Safety / AI Ethics

  

> Mechanistic Interpretability

• Uncovering Mesa-Optimization Algorithms in Transformers
  Johannes von Oswald*, Eyvind Niklasson*, Maximilian Schlegel*, Seijin Kobayashi, Nicolas Zuccet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Razvan Pascanu and João Sacramento
  Arxiv Preprint, 2023
  
  Paper

  Mechanistic Interpretability

  Mesa-Optimization

  

> Causality / Machine Learning

• Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
  Mateusz Olko*, Michał Zając*, Aleksandra Nowak*, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś
  NeurIPS 2023
  
  Paper

  Causal Structure Learning

  Experimental Design

  
• On the Generalization and Adaption Performance of Causal Models
  Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, & Nan Rosemary Ke,
  ICML 2022, SCIS and BeyondBayes Workshop
  
  Paper

  Causal Models

  Generalization / Robustness

  Few-Shot Adaptation

  
• Learning Neural Causal Models with Active Interventions
  Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer & Nan Rosemary Ke,
  NeurIPS 2021, WHY Workshop
  
  Paper Web Version

  Causal Structure Learning

  Experimental Design

  
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
  Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio & Stefan Bauer,
  KDD 2021, BCIRWIS Workshop
  
  Paper Code

  Causal Structure Learning

  Variational Inference

  

> Medical Image Analysis

• Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning
  Christian Federau, Soren Christensen, Nino Scherrer, Johanna M. Ospel, Victor Schulze-Zachau, Noemi Schmidt, Hannes-Christian Breit, Julian Macclaren, Maarten Lansberg & Sebastian Kozerke,
  Radiology: Artificial Intelligence, (Volume 2, 2020)
  
  Paper

  Medical Image Analysis

  Semantic Segmentation

  

> Biology / Tissue Engineering

• Improved Radial Matrix Constraint Influences Tissue Contraction and Promotes Maturation of Bi-Layered Skin Equivalents
  Jessica Polak, David Sachs, Nino Scherrer, Adrian Süess, Huan Liu, Mitchell Levesque, Sabine Werner, Edoardo Mazza, Gaetana Restivo, Mirko Meboldt and Costanza Giampietro,
  Biomaterials Advances, (Volume 156, 2024)
  
  Paper

  Tissue Engineering

  Human Skin Equivalents

  

Tutorial "Causality and Deep Learning"

Talks

• ...

• September 2023: "Evaluating the Moral Beliefs Encoded in LLMs", ChatGPT Zurich Group (AI Meetup), Zurich
• February 2023: "Deep Learning for Causality: Neural Causal Structure Learning", AI for Actionable Impact (AI4AI) Lab , Imperial College London
• January 2023: "On the Synergies of Causality and DeepLearning", Neuroscience in ML Seminar, ETH Zurich
• April 2022: "Learning Neural Causal Models with Active Interventions", XAI seminar series, Imperial College London
• October 2021: "Learning Neural Causal Models with Active Interventions", Causality Reading Group, TU Darmstadt

Nino's Log

• Surveying Large Language Models / AI Psychiatry
  ... Coming soon ...