Papers
NEVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana et al.
Neyman-Pearson classification: parametrics and sample size requirement
Xin Tong, Lucy Xia, Jiacheng Wang et al.
Noise Accumulation in High Dimensional Classification and Total Signal Index
Miriam R. Elman, Jessica Minnier, Xiaohui Chang et al.
Nonparametric graphical model for counts
Arkaprava Roy, David B Dunson
On Efficient Adjustment in Causal Graphs
Janine Witte, Leonard Henckel, Marloes H. Maathuis et al.
Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Hanbaek Lyu, Deanna Needell, Laura Balzano
Online Sufficient Dimension Reduction Through Sliced Inverse Regression
Zhanrui Cai, Runze Li, Liping Zhu
On lp-Support Vector Machines and Multidimensional Kernels
Victor Blanco, Justo Puerto, Antonio M. Rodriguez-Chia
On Mahalanobis Distance in Functional Settings
José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Xi Chen, Simon S. Du, Xin T. Tong
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Nicolas Garcia Trillos, Zachary Kaplan, Thabo Samakhoana et al.
On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach
Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang
Optimal Bipartite Network Clustering
Zhixin Zhou, Arash A. Amini
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Junhong Lin, Volkan Cevher
Optimal Estimation of Sparse Topic Models
Xin Bing, Florentina Bunea, Marten Wegkamp
Orlicz Random Fourier Features
Linda Chamakh, Emmanuel Gobet, Zoltán Szabó
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
Anna Little, Mauro Maggioni, James M. Murphy
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Ery Arias-Castro, Adel Javanmard, Bruno Pelletier
Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications
Frederik Heber, Žofia Trst’anová, Benedict Leimkuhler
Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer et al.
Prediction regions through Inverse Regression
Emilie Devijver, Emeline Perthame