Statistical Learning
Efficient characterization of electrically evoked responses for neural interfaces
Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky
Neural Information Processing Systems http://nips.cc/
OnlineForecastingofTotal-Variation-bounded Sequences
We consider the problem of online forecasting of sequences of lengthn with total-variation at mostCn using observations contaminated by independentฯsubgaussian noise. We design anO(nlogn)-time algorithm that achieves a cu-mulativesquare error of O(n1/3C2/3n ฯ4/3+C2n)with high probability.
Chefs'RandomTables: Non-TrigonometricRandom Features
We introduce chefs' random tables(CRTs), a new class of non-trigonometric random features (RFs) toapproximate Gaussian andsoftmax-kernels.
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang
The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn?
Gradient-basedAdaptiveMarkovChainMonteCarlo
The basic set up of an MCMC algorithm inanyprobabilistic (e.g.