Statistical Learning
The Impact of Regularization on High-dimensional Logistic Regression
Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
Neural Information Processing Systems http://nips.cc/
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Neural Information Processing Systems http://nips.cc/
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen, Umut Simsekli, Mert Gurbuzbalaban, Gaël RICHARD
In this study, we provide formal theoretical analysis where we derive explicit conditions for the step-size such that the metastability behavior of the discrete-time system is similar to its continuous-time limit.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Existing regression-based models can only utilize the sufficiently fine-grained auxiliary data sets on the same domain (e.g., a city).
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Wei Qian, Yuqian Zhang, Yudong Chen
Understanding the convergence property of EM is highly nontrivial due to the non-convexity of the negative log-likelihood function.
Neural Similarity Learning
Weiyang Liu, Zhen Liu, James M. Rehg, Le Song
Neural Information Processing Systems http://nips.cc/
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh Kai-Siang Ang Hubert H. K. T eo
Removing such large groups can result in significant changes to the model.
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Neural Information Processing Systems http://nips.cc/
Generalization Bounds in the Predict-then-Optimize Framework
Othman El Balghiti, Adam Elmachtoub, Paul Grigas, Ambuj Tewari
The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters.