Publications

• Gaussian universality for approximately polynomial functions of high-dimensional data [arxiv] 

K.Huang, M.Austern and P.Orbanz

• Random Geometric Graph Alignment with Graph Neural Networks[arxiv] [Submitted]

S. Liu and M. Austern

• Statistical Guarantees for Link Prediction using Graph Neural Networks[arxiv ][Submitted]

A.Chung, A. Saberi and M.Austern

• Poisson approximation for stochastic processes summed over amenable groups[arxiv ][Submitted]

H.Ye, P.Orbanz and M.Austern

• Inference on Optimal Dynamic Policies via Softmax Approximation [arxiv] [Submitted]

Q. Chen, M.Austern and V. Syrkganis

• Wassertein-p bounds for the central limit theorem under weak dependence [arxiv] [Submitted]

• Wasserstein-p Bounds in the Central Limit Theorem Under Local Dependence [arxiv] [EJP]

T.Liu, M.Austern

• Efficient Concentration with Gaussian Approximation [arxiv ][In revision at PTRF]

• Debiased Machine learning without sample-splitting for stable estimators [arxiv ] [NeurIPS]

• Quantifying the effect of data augmentation [arxiv ] [In revision at JRSS-B]

• Asymptotics of network embeddings learned via subsampling [arxiv]   [JMLR]

• A free central-limit theorem for dynamical systems[arxiv] [Submitted]

• Asymptotics of Cross Validation [arxiv] [Annals of Institut Henri Poincare]

• Asymptotics of the Bootstrap method beyond asymptotically Gaussian Estimators [arxiv][NeurIPS]

• Limit theorems for invariant distributions  [arxiv][ Annals of Statistics]

• On the Gaussianity of Kolmogorov Complexity of Mixing Sequences [arxiv] [Transactions of Information Theory]

• Compressibility and Generalization in Large-Scale Deep Learning  [arxiv] [ICLR]

• Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data  [arxiv] [AISTATS]