Statistical Learning
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
Neural Information Processing Systems http://nips.cc/
Temporal alignment and latent Gaussian process factor inference in population spike trains
Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories.
KONG: Kernels for ordered-neighborhood graphs
Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
Neural Information Processing Systems http://nips.cc/
Variance-Reduced Stochastic Gradient Descent on Streaming Data
Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip Gibbons, Srikanta Tirthapura
Such a model is never "complete" but instead needs to be continuously updated as newer training data points arrive.
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
A contextual bandit is an online learning framework for modeling sequential decision-making problems.
Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro
Neural Information Processing Systems http://nips.cc/
High Dimensional Linear Regression using Lattice Basis Reduction
We propose a new polynomial-time algorithm for this task which is based on the seminal Lenstra-Lenstra-Lovasz (LLL) lattice basis reduction algorithm.
Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
This is made possible by learning a projection between a feature space and a semantic space (e.g.
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions.
Sketching Method for Large Scale Combinatorial Inference
We present computationally efficient algorithms to test various combinatorial structures of large-scale graphical models. In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.