Morgane Austern

According to our database, Morgane Austern authored at least 17 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

Graph Attention Network for Node Regression on Random Geometric Graphs with Erdős-Rényi contamination.

Somak Laha, Suqi Liu, Morgane Austern

CoRR, January, 2026

2025

Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing.

Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis

CoRR, July, 2025

Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models.

Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke, Tianle Liu

CoRR, March, 2025

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation.

Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks.

Suqi Liu, Morgane Austern

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Random Geometric Graph Alignment with Graph Neural Networks.

Suqi Liu, Morgane Austern

CoRR, 2024

Statistical Guarantees for Link Prediction using Graph Neural Networks.

Alan Chung, Amin Saberi, Morgane Austern

CoRR, 2024

2023

Asymptotics of Network Embeddings Learned via Subsampling.

Andrew Davison, Morgane Austern

J. Mach. Learn. Res., 2023

Inference on Optimal Dynamic Policies via Softmax Approximation.

Qizhao Chen, Morgane Austern, Vasilis Syrgkanis

CoRR, 2023

2022

Quantifying the Effects of Data Augmentation.

Kevin Han Huang, Peter Orbanz, Morgane Austern

CoRR, 2022

Debiased Machine Learning without Sample-Splitting for Stable Estimators.

Qizhao Chen, Vasilis Syrgkanis, Morgane Austern

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection.

Morgane Austern, Vasilis Syrgkanis

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

On the Gaussianity of Kolmogorov Complexity of Mixing Sequences.

Morgane Austern, Arian Maleki

IEEE Trans. Inf. Theory, 2020

Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality.

Morgane Austern, Vasilis Syrgkanis

CoRR, 2020

2019

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Proceedings of the 7th International Conference on Learning Representations, 2019

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data.

Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Compressibility and Generalization in Large-Scale Deep Learning.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

CoRR, 2018