Fanny Yang
Associate Professor
Leads the Statistical Machine Learning group, studying the theory of robust and reliable learning, high-dimensional statistics and overparameterized models.
Fanny Yang's lab at ETH Zurich works on learning theory & optimization and trustworthy & safe ML. 21 papers since 2023, 15 at top venues, mostly ICLR and ICML. 6 PhD students. 8 alumni, most often to AI center and Amazon.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 71%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 2 · collecting
- 5 papers2025 · T1 3
- 9 papers2024 · T1 7
- 4 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, ICML, AISTATS, NeurIPS, ALT
Research focuspaper titles that use each topic's words
- Learning theory & optimization7 papers
- Trustworthy & safe ML0 papers
- overparameterized0 papers
- high-dimensional statistics0 papers
- studying0 papers
Paper list
From the lab homepage.
20263 papers
- ICML (T1)Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm That Provably Exploits Model Similarity
- International Conference on Machine Learning (ICML), SpotlightHedging on the Frontier: Learning New Tasks with Few Samples
- ICLR (T1)ROC-n-reroll: How verifier imperfection affects test-time scaling
20255 papers
- International Conference on Learning Representations (ICLR), OralCopyright-Protected Language Generation via Adaptive Model Fusion
- ICLR (T1)Doubly robust identification of treatment effects from multiple environments
- NeurIPS (T1)Efficient Randomized Experiments Using Foundation Models
- AISTATS (T1)Learning Pareto fronts in high dimensions: How can regularization help?
- Neural Information Processing Systems (NeurIPS), SpotlightOn the sample complexity of semi-supervised multi-objective learning
20249 papers
- NeurIPS (T1)Achievable distributional robustness when the robust risk is only partially identified
- AISTATS (T1)Certified private data release for sparse Lipschitz functions
- UAI (T1)Detecting critical treatment effect bias in small subgroups
- AISTATS (T1)Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
- ICML (T1)Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
- SaTMLPILLAR: How to make semi-private learning more effective
- ICML (T1)Privacy-preserving data release leveraging optimal transport and particle gradient descent
- NeurIPS (T1)Robust Mixture Learning when Outliers Overwhelm Small Groups
- ALTTight bounds for maximum l1-margin classifiers
20234 papers
- Neural Information Processing Systems (NeurIPS), SpotlightCan semi-supervised learning use all the data effectively? A lower bound perspective
- ICML (T1)Margin-based sampling in high dimensions: When being active is less efficient than staying passive
- ICLR (T1)Strong inductive biases provably prevent harmless interpolation
- ICLR (T1)Why adversarial training can hurt robust accuracy
Where alumni went8 listed, 100% to industry
Industry 4Academia 0
Most often to
- AI center
- Amazon
- Google DeepMind
Apply to ETH ZurichRolling
Not announced yet
Computer Science Doctorate (D-INFK)
- Round
- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
- See program page
Confirmed on the program page.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.