I am an Assistant Professor in the Computer Science Department (D-INFK) at ETH Zurich. Previously I was a postdoctoral Scholar at Stanford University working with John Duchi and Percy Liang and a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich working with Nicolai Meinshausen. Before that, I was a PhD student at the EECS department of UC Berkeley advised by Martin Wainwright.

#### Research interests

I’m generally interested in theoretically understanding and developing tools in machine learning and statistics that work well. Currently I am particularly curious about gaining theoretical understanding for the generalization properties of overparameterized models for high-dimensional data (motivated by neural networks), as well as a plethora of questions related to obtaining more trustworthy ML models, specifically distributional robustness, domain generalization and interpretability.

For the latter branch of questions I’m particularly excited about problems in the medical domain - hence if you’re facing concrete reliability issues when using ML for medical diagnostics or treatment, please don’t hesitate to ping me.

#### Recent talk slides

• (2022) At MSRI workshop Foundations of Stable, Generalizable and Transferable Statistical Learning on fast rates for interpolation for min-lp-norm interpolation (for p in [1,2]) and issues of interpolating models for robust evaluation slides
• (2021) At ELLIS Doctoral symposium on limits of rotationally invariant kernels in high dimensions (such as NTK) and semi-supervised novelty detection slides

#### Recent papers

1. Provable concept learning for interpretable predictions using variational inference
arXiv preprint, 2022
2. Why adversarial training can hurt robust accuracy
arXiv preprint, 2022
1. How unfair is private learning?
Conference on Uncertainty in Artificial Intelligence (UAI), Oral, 2022
2. Semi-supervised novelty detection using ensembles with regularized disagreement
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
3. Fast rates for noisy interpolation require rethinking the effects of inductive bias
International Conference on Machine Learning (ICML), 2022
4. Tight bounds for minimum l1-norm interpolation of noisy data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
1. Self-supervised Reinforcement Learning with Independently Controllable Subgoals
Conference on Robot Learning (CoRL), 2021
2. How rotational invariance of common kernels prevents generalization in high dimensions
International Conference on Machine Learning (ICML), 2021
3. Interpolation can hurt robust generalization even when there is no noise
Neural Information Processing Systems (NeurIPS), 2021
1. Understanding and Mitigating the Tradeoff between Robustness and Accuracy
International Conference on Machine Learning (ICML), 2020
1. Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Neural Information Processing Systems (NeurIPS), 2019

#### Selected older publications

1. Early stopping for kernel boosting algorithms: A general analysis with localized complexities
IEEE Transactions on Information Theory (IEEE IT), 2019
Neural Information Processing Systems (NeurIPS), Spotlight, 2017
2. A framework for multi-A(rmed)/B(andit) testing with online fdr control
Neural Information Processing Systems (NeurIPS), Spotlight, 2017
3. Statistical and computational guarantees for the Baum-Welch algorithm
Journal for Marchine Learning Research (JMLR), 2017
4. Phase Retrieval via Structured Modulation in Paley-Wiener Spaces
International Conference on Sampling Theory and Applications 2013

#### Short C.V.

 01/2020 - present Assistant Professor, ETH Zurich 04/2019 - 12/2019 Postdoctoral Fellow, Stanford University 09/2018 - 09/2019 Junior Fellow (Postdoc), ETH Zurich 06/2017 - 02/2018 Applied Scientist Intern, Amazon AWS, Palo Alto 08/2013 - 08/2018 PhD, UC Berkeley 10/2010 - 08/2013 M. Sc., Technical University Munich (TUM) 10/2007 - 10/2010 B.Sc., Karlsruhe Institute of Technology (KIT)

#### Contact

The best way to reach me is via e-mail at fan.yang (at) inf.ethz.ch. however note that I cannot respond to most requests although I try to answer all research-related messages.

Meetings take place in my office: CAB G68 map
Note: Enter the south side of the CAB building and walk up to G floor
The G floor is not connected, hence it’d be a pity if you reach it in the wrong section

Personal information about me can be found on my website