Fanny Yang

According to our database1, Fanny Yang authored at least 37 papers between 2012 and 2024.

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Bibliography

2024
Privacy-preserving data release leveraging optimal transport and particle gradient descent.
CoRR, 2024

2023
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials.
CoRR, 2023

How robust accuracy suffers from certified training with convex relaxations.
CoRR, 2023

PILLAR: How to make semi-private learning more effective.
CoRR, 2023

Sample-efficient private data release for Lipschitz functions under sparsity assumptions.
CoRR, 2023

Can semi-supervised learning use all the data effectively? A lower bound perspective.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Margin-based sampling in high dimensions: When being active is less efficient than staying passive.
Proceedings of the International Conference on Machine Learning, 2023

Why adversarial training can hurt robust accuracy.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Strong inductive biases provably prevent harmless interpolation.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Tight bounds for maximum 𝓁<sub>1</sub>-margin classifiers.
CoRR, 2022

Uniform versus uncertainty sampling: When being active is less efficient than staying passive.
CoRR, 2022

Provable concept learning for interpretable predictions using variational inference.
CoRR, 2022

Fast rates for noisy interpolation require rethinking the effects of inductive bias.
CoRR, 2022

Semi-supervised novelty detection using ensembles with regularized disagreement.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

How unfair is private learning?
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Fast rates for noisy interpolation require rethinking the effect of inductive bias.
Proceedings of the International Conference on Machine Learning, 2022

Tight bounds for minimum ℓ<sub>1</sub>-norm interpolation of noisy data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Tight bounds for minimum l1-norm interpolation of noisy data.
CoRR, 2021

Interpolation can hurt robust generalization even when there is no noise.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Novel Disease Detection Using Ensembles with Regularized Disagreement.
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis, 2021

How rotational invariance of common kernels prevents generalization in high dimensions.
Proceedings of the 38th International Conference on Machine Learning, 2021

Self-supervised Reinforcement Learning with Independently Controllable Subgoals.
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020
Learn what you can't learn: Regularized Ensembles for Transductive Out-of-distribution Detection.
CoRR, 2020

Understanding and Mitigating the Tradeoff between Robustness and Accuracy.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Early Stopping for Kernel Boosting Algorithms: A General Analysis With Localized Complexities.
IEEE Trans. Inf. Theory, 2019

Adversarial Training Can Hurt Generalization.
CoRR, 2019

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Regularized Learning for Domain Adaptation under Label Shifts.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Statistics meets Optimization: Computational guarantees for statistical learning algorithms.
PhD thesis, 2018

2017
Statistical and Computational Guarantees for the Baum-Welch Algorithm.
J. Mach. Learn. Res., 2017

A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Online control of the false discovery rate with decaying memory.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2014
A phase retrieval method for signals in modulation-invariant spaces.
Proceedings of the IEEE International Conference on Acoustics, 2014

2013
Phaseless Signal Recovery in Infinite Dimensional Spaces using Structured Modulations
CoRR, 2013

Phase Retrieval via Structured Modulations in Paley-Wiener Spaces
CoRR, 2013

Phase retrieval from low rate samples.
CoRR, 2013

2012
Causal reconstruction kernels for consistent signal recovery.
Proceedings of the 20th European Signal Processing Conference, 2012


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