Morgane Austern

21 papers A* 3A 2Journal 15Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Graph Attention Network for Node Regression on Random Geometric Graphs with Erdős-Rényi contamination. CoRR Somak Laha, Suqi Liu, Morgane Austern
2025	J	`jnl`	Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing. CoRR Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis
2025	A	`conf`	Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks. AISTATS Suqi Liu, Morgane Austern
2025	J	`jnl`	Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models. CoRR Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke, Tianle Liu
2025	A*	`conf`	Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation. COLT Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern
2024	J	`jnl`	Random Geometric Graph Alignment with Graph Neural Networks. CoRR Suqi Liu, Morgane Austern
2024	J	`jnl`	Statistical Guarantees for Link Prediction using Graph Neural Networks. CoRR Alan Chung, Amin Saberi, Morgane Austern
2023	J	`jnl`	Asymptotics of Network Embeddings Learned via Subsampling. J. Mach. Learn. Res. Andrew Davison, Morgane Austern
2023	J	`jnl`	Inference on Optimal Dynamic Policies via Softmax Approximation. CoRR Qizhao Chen, Morgane Austern, Vasilis Syrgkanis
2022	A*	`conf`	Debiased Machine Learning without Sample-Splitting for Stable Estimators. NeurIPS Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
2022	J	`jnl`	Debiased Machine Learning without Sample-Splitting for Stable Estimators. CoRR Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
2022	J	`jnl`	Quantifying the Effects of Data Augmentation. CoRR Kevin Han Huang, Peter Orbanz, Morgane Austern
2021	J	`jnl`	Asymptotics of Network Embeddings Learned via Subsampling. CoRR Andrew Davison, Morgane Austern
2021	A*	`conf`	Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection. NeurIPS Morgane Austern, Vasilis Syrgkanis
2020	J	`jnl`	Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality. CoRR Morgane Austern, Vasilis Syrgkanis
2020	J	`jnl`	On the Gaussianity of Kolmogorov Complexity of Mixing Sequences. IEEE Trans. Inf. Theory Morgane Austern, Arian Maleki
2019	A	`conf`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2019	—	`conf`	Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Compressibility and Generalization in Large-Scale Deep Learning. CoRR Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2017	J	`jnl`	On the Gaussianity of Kolmogorov Complexity of Mixing Sequences. CoRR Morgane Austern, Arian Maleki