Statistical Learning
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling
While ridge regression provides shrinkage for the regression coefficients, manyofthecoefficients remain smallbutnon-zero. Performing ridgeregression with the matrix sketch returned by our algorithm and a particular regularization parameter forces coefficients to zero and has a provable(1+) bound on the statisticalrisk.
STREETS: A Novel Camera Network Dataset for Traffic Flow
We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Muhammad Osama, Dave Zachariah, Peter Stoica
Neural Information Processing Systems http://nips.cc/
Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
A Little Is Enough: Circumventing Defenses For Distributed Learning
Gilad Baruch, Moran Baruch, Yoav Goldberg
Neural Information Processing Systems http://nips.cc/
Towards Robust Detection of Adversarial Examples
Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
Neural Information Processing Systems http://nips.cc/
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
Neural Information Processing Systems http://nips.cc/
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi, Nisheeth Vishnoi
Neural Information Processing Systems http://nips.cc/
