Nino Scherrer

<firstname>.<lastname>@gmail.com

I am a visiting research scientist at FAR AI, working with Claudia Shi (Columbia) on a causal perspective for reward learning from human feedback. Previously, I was a research intern at the Vector Institute (hosted by Prof. Animesh Garg) and at Mila (hosted by Prof. Yoshua Bengio). During this research visits, I have been fortunate to work closely with Nan Rosemary Ke, Anirudh Goyal and Prof. Stefan Bauer.

My research interest centers around the causal perspective on machine learning to build robust and adaptive models that can handle various sorts of distribution shifts. I did my masters and bachelors in computer science at ETH Zürich and collaborated with Mila and MPI for Intelligent Systems, Tübingen during my master thesis on active neural causal discovery. Previously, I did a diploma (vocational education) in computer science and worked multiple years as a systems engineer at SFS AG.

Info: I'm currently on the job market for PhD positions and research scientist positions.

Fields of Interest

Causal Inference X Machine Learning Representation Learning Meta Learning Reinforcement Learning AI for Healthcare AI for Sciences

Research

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
Preprint (arXiv) & NeurIPS 2022 Workshop on Causality for Real-world Impact ,
[Paper]

Causal Discovery

Experimental Design
On the Generalization and Adaption Performance of Causal Models
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, & Nan Rosemary Ke,
Preprint (arXiv) & ICML 2022 - SCIS and BeyondBayes Workshop,
[Paper]

Causal Models

Zero- and 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,
Preprint (arXiv) & NeurIPS WHY-21 Workshop, 2021,
[Paper] [Blog]

Causal Discovery

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,
Preprint (arXiv) & KDD - BCIRWIS Workshop, 2021,
[Paper] [Code]

Causal Discovery

Variational Inference
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, 2020,
[Paper]

Medical Image Analysis

Semantic Segmentation

Talks

December 2022: "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