Causal reconstruction kernels for consistent signal recovery. - Volker Pohl; Fanny Yang; Holger Boche (EUSIPCO, 2012)

Phase retrieval from low rate samples. - Volker Pohl; Fanny Yang; Holger Boche (CoRR, 2013)

Phase Retrieval via Structured Modulations in Paley-Wiener Spaces - Fanny Yang; Volker Pohl; Holger Boche (CoRR, 2013)

Phaseless Signal Recovery in Infinite Dimensional Spaces using Structured Modulations - Volker Pohl; Fanny Yang; Holger Boche (CoRR, 2013)

A phase retrieval method for signals in modulation-invariant spaces. - Volker Pohl; Çagkan Yapar; Holger Boche; Fanny Yang (ICASSP, 2014)

Online control of the false discovery rate with decaying memory. - Aaditya Ramdas; Fanny Yang; Martin J. Wainwright; Michael I. Jordan (NIPS, 2017)

A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control. - Fanny Yang; Aaditya Ramdas; Kevin G. Jamieson; Martin J. Wainwright (NIPS, 2017)

Statistical and Computational Guarantees for the Baum-Welch Algorithm. - Fanny Yang; Sivaraman Balakrishnan; Martin J. Wainwright (J. Mach. Learn. Res., 2017)

Statistics meets Optimization: Computational guarantees for statistical learning algorithms. - Fanny Yang (2018)

Regularized Learning for Domain Adaptation under Label Shifts. - Kamyar Azizzadenesheli; Anqi Liu; Fanny Yang; Animashree Anandkumar (ICLR (Poster), 2019)

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness. - Fanny Yang; Zuowen Wang; Christina Heinze-Deml (NeurIPS, 2019)

Adversarial Training Can Hurt Generalization. - Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang (CoRR, 2019)

Early Stopping for Kernel Boosting Algorithms: A General Analysis With Localized Complexities. - Yuting Wei 001; Fanny Yang; Martin J. Wainwright (IEEE Trans. Inf. Theory, 2019)

Understanding and Mitigating the Tradeoff between Robustness and Accuracy. - Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang (ICML, 2020)

Learn what you can't learn: Regularized Ensembles for Transductive Out-of-distribution Detection. - Alexandru Tifrea; Eric Stavarache; Fanny Yang (CoRR, 2020)

Self-supervised Reinforcement Learning with Independently Controllable Subgoals. - Andrii Zadaianchuk; Georg Martius; Fanny Yang (CoRL, 2021)

How rotational invariance of common kernels prevents generalization in high dimensions. - Konstantin Donhauser; Mingqi Wu; Fanny Yang (ICML, 2021)

Novel Disease Detection Using Ensembles with Regularized Disagreement. - Alexandru Tifrea; Eric Stavarache; Fanny Yang (UNSURE/PIPPI@MICCAI, 2021)

Interpolation can hurt robust generalization even when there is no noise. - Konstantin Donhauser; Alexandru Tifrea; Michael Aerni; Reinhard Heckel; Fanny Yang (NeurIPS, 2021)

Tight bounds for minimum l1-norm interpolation of noisy data. - Guillaume Wang; Konstantin Donhauser; Fanny Yang (CoRR, 2021)

Tight bounds for minimum ℓ1-norm interpolation of noisy data. - Guillaume Wang; Konstantin Donhauser; Fanny Yang (AISTATS, 2022)

Fast rates for noisy interpolation require rethinking the effect of inductive bias. - Konstantin Donhauser; Nicolò Ruggeri; Stefan Stojanovic; Fanny Yang (ICML, 2022)

How unfair is private learning? - Amartya Sanyal; Yaxi Hu; Fanny Yang (UAI, 2022)

Semi-supervised novelty detection using ensembles with regularized disagreement. - Alexandru Tifrea; Eric Stavarache; Fanny Yang (UAI, 2022)

Fast rates for noisy interpolation require rethinking the effects of inductive bias. - Konstantin Donhauser; Nicolò Ruggeri; Stefan Stojanovic; Fanny Yang (CoRR, 2022)

Provable concept learning for interpretable predictions using variational inference. - Armeen Taeb; Nicolò Ruggeri; Carina Schnuck; Fanny Yang (CoRR, 2022)

Uniform versus uncertainty sampling: When being active is less efficient than staying passive. - Alexandru Tifrea; Jacob Clarysse; Fanny Yang (CoRR, 2022)

Strong inductive biases provably prevent harmless interpolation. - Michael Aerni; Marco Milanta; Konstantin Donhauser; Fanny Yang (ICLR, 2023)

Why adversarial training can hurt robust accuracy. - Jacob Clarysse; Julia Hörrmann; Fanny Yang (ICLR, 2023)

Margin-based sampling in high dimensions: When being active is less efficient than staying passive. - Alexandru Tifrea; Jacob Clarysse; Fanny Yang (ICML, 2023)

Can semi-supervised learning use all the data effectively? A lower bound perspective. - Alexandru Tifrea; Gizem Yüce; Amartya Sanyal; Fanny Yang (NeurIPS, 2023)

Sample-efficient private data release for Lipschitz functions under sparsity assumptions. - Konstantin Donhauser; Johan Lokna; Amartya Sanyal; March Boedihardjo; Robert Hönig; Fanny Yang (CoRR, 2023)

How robust accuracy suffers from certified training with convex relaxations. - Piersilvio De Bartolomeis; Jacob Clarysse; Amartya Sanyal; Fanny Yang (CoRR, 2023)

Hidden yet quantifiable: A lower bound for confounding strength using randomized trials. - Piersilvio De Bartolomeis; Javier Abad Martinez; Konstantin Donhauser; Fanny Yang (AISTATS, 2024)

Certified private data release for sparse Lipschitz functions. - Konstantin Donhauser; Johan Lokna; Amartya Sanyal; March Boedihardjo; Robert Hönig; Fanny Yang (AISTATS, 2024)

Tight bounds for maximum ℓ1-margin classifiers. - Stefan Stojanovic; Konstantin Donhauser; Fanny Yang (ALT, 2024)

Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent. - Konstantin Donhauser; Javier Abad Martinez; Neha Hulkund; Fanny Yang (ICML, 2024)

Minimum Norm Interpolation Meets The Local Theory of Banach Spaces. - Gil Kur; Pedro Abdalla; Pierre Bizeul; Fanny Yang (ICML, 2024)

Robust Mixture Learning when Outliers Overwhelm Small Groups. - Daniil Dmitriev; Rares-Darius Buhai; Stefan Tiegel; Alexander Wolters; Gleb Novikov; Amartya Sanyal; David Steurer; Fanny Yang (NeurIPS, 2024)

Achievable distributional robustness when the robust risk is only partially identified. - Julia Kostin; Nicola Gnecco; Fanny Yang (NeurIPS, 2024)

PILLAR: How to make semi-private learning more effective. - Francesco Pinto; Yaxi Hu; Fanny Yang; Amartya Sanyal (SaTML, 2024)

Detecting critical treatment effect bias in small subgroups. - Piersilvio De Bartolomeis; Javier Abad; Konstantin Donhauser; Fanny Yang (UAI, 2024)

Strong Copyright Protection for Language Models via Adaptive Model Fusion. - Javier Abad; Konstantin Donhauser; Francesco Pinto; Fanny Yang (CoRR, 2024)

Atmospheric Transport Modeling of CO2 with Neural Networks. - Vitus Benson; Ana Bastos; Christian Reimers; Alexander J. Winkler; Fanny Yang; Markus Reichstein (CoRR, 2024)

Learning Pareto manifolds in high dimensions: How can regularization help? - Tobias Wegel; Filip Kovacevic; Alexandru Tifrea; Fanny Yang (AISTATS, 2025)

Copyright-Protected Language Generation via Adaptive Model Fusion. - Javier Abad; Konstantin Donhauser; Francesco Pinto; Fanny Yang (ICLR, 2025)

Doubly robust identification of treatment effects from multiple environments. - Piersilvio De Bartolomeis; Julia Kostin; Javier Abad; Yixin Wang; Fanny Yang (ICLR, 2025)

Efficient Randomized Experiments Using Foundation Models. - Piersilvio De Bartolomeis; Javier Abad; Guanbo Wang; Konstantin Donhauser; Raymond M. Duch; Fanny Yang; Issa J. Dahabreh (CoRR, 2025)

ROC-n-reroll: How verifier imperfection affects test-time scaling. - Florian E. Dorner; Yatong Chen; André F. Cruz; Fanny Yang (CoRR, 2025)