Fast Prediction of New Feature Utility Hoyt Koepke Department of Statistics University of Washington Seattle, WAMikhail Bilenko Microsoft Research, Redmond, WA 12345

Source URL: arxiv.org

• Mathematical analysis
• Statistics
• Gradient descent
• Loss function
• Analysis
• Spectral theory
• Operator theory
• Ordinary differential equations
• Lambda calculus

Stability and optimality in stochastic gradient descent arXiv:1505.02417v1 [stat.ME] 10 May 2015 Panos Toulis, Dustin Tran, and Edoardo M. Airoldi Harvard University

Source URL: arxiv.org

Stochastic Gradient Descent with Importance Sampling Rachel Ward UT Austin Joint work with Deanna Needell (Claremont McKenna College)

Source URL: www.ma.utexas.edu

Gossip Learning with Linear Models on Fully Distributed Data∗ Róbert Ormándi, István Heged˝us, Márk Jelasity University of Szeged and Hungarian Academy of Sciences {ormandi,ihegedus,jelasity}@inf.u-szeged.hu

Source URL: www.inf.u-szeged.hu

• Statistics
• Machine learning
• Computer programming
• Distributed computing
• File sharing
• Artificial neural networks
• Computational statistics
• Peer-to-peer
• Expectationmaximization algorithm
• Perceptron
• Supervised learning
• Stochastic gradient descent

c Springer Verlag. The copyright for this contribution is held by Springer Verlag. The original publication is available at www.springerlink.com. Active Contours Methods with Respect to Vickers Indentations

Source URL: wavelab.at

• Image processing
• Computer vision
• Vision
• Image segmentation
• Artificial intelligence
• Active contour model
• Computer graphics
• Edge detection
• Gradient descent
• Image gradient
• Thresholding
• Graph cuts in computer vision

Sparse Online Learning via Truncated Gradient John Langford Yahoo! Research

Source URL: hunch.net

• Machine learning
• Computational statistics
• Convex optimization
• Mathematical optimization
• Statistical classification
• Support vector machine
• Stochastic gradient descent
• Lasso
• Vowpal Wabbit
• Least squares
• Artificial neural network
• Gradient descent

PCA-enhanced stochastic optimization methods Alina Kuznetsova, Gerard Pons-Moll, and Bodo Rosenhahn⋆ Institute for Information Processing (TNT), Leibniz University Hanover, Germany {kuznetso,pons,rosenhahn}@tnt.uni-han

Source URL: www.tnt.uni-hannover.de

• Statistics
• Applied mathematics
• Numerical analysis
• Metaheuristics
• Computational statistics
• Monte Carlo methods
• Estimation theory
• Statistical mechanics
• Mathematical optimization
• Simulated annealing
• Stochastic optimization
• Gradient descent

CS168: The Modern Algorithmic Toolbox Lecture #15: Gradient Descent Basics Tim Roughgarden & Gregory Valiant May 18,

Source URL: web.stanford.edu

• Numerical analysis
• Mathematical analysis
• Mathematical optimization
• Convex analysis
• Operations research
• Convex optimization
• Gradient descent
• Derivative
• Convex function
• Linear programming
• Gradient boosting
• FrankWolfe algorithm

One coordinate at a time • Adaboost performs gradient descent on exponential loss • Adds one coordinate (“weak learner”) at each iteration. • Weak learning in binary classification = slightly better than random

Source URL: seed.ucsd.edu

Smoothed Gradients for Stochastic Variational Inference David Blei Department of Computer Science Department of Statistics Columbia University

Source URL: papers.nips.cc

• Statistics
• Mathematical analysis
• Statistical theory
• Stochastic optimization
• Computational statistics
• Estimation theory
• M-estimators
• Stochastic gradient descent
• Bias of an estimator
• Mathematical optimization
• Variational Bayesian methods
• Biasvariance tradeoff