The CVX Users’ Guide Release 2.1 Michael C. Grant, Stephen P. Boyd CVX Research, Inc.

Source URL: cvxr.com

• Numerical analysis
• Mathematical optimization
• Mathematical software
• Software
• Numerical software
• Computer file formats
• Convex optimization
• Operations research
• MOSEK
• Nl
• Quadratic programming
• AMPL

MetaGrad: Faster Convergence Without Curvature in Online Convex Optimization Wouter M. Koolen Tim van Erven

Source URL: wouterkoolen.info

• Convex analysis
• Mathematical analysis
• Mathematical optimization
• Analysis
• Operations research
• Convex optimization
• Convex function
• Machine learning
• Cognition

CSStat 260, Fall 2014: Learning in Sequential Decision Problems Lectures: Evans 334. Tuesday/Thursday 2:00-3:30. Instructor: Peter Bartlett http://www.stat.berkeley.edu/∼bartlett

Source URL: www.stat.berkeley.edu

• Mathematical optimization
• Operations research
• Mathematical analysis
• Numerical analysis
• Convex optimization
• Stochastic optimization
• Multi-armed bandit
• Game theory
• Linear programming
• AMPL

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes Harikrishna Narasimhan∗ Indian Institute of Science, Bangalore, INDIA HARIKRISHNA @ CSA . IISC . ERNET. IN

Source URL: jmlr.org

• Mathematical analysis
• Machine learning
• Learning
• Statistics
• Operations research
• Statistical classification
• Stochastic optimization
• Convex optimization
• Mathematical optimization
• Support vector machine
• Distribution
• Stochastic gradient descent

Lecture Notes on Online Learning DRAFT Alexander Rakhlin —– These lecture notes contain material presented in the Statistical Learning Theory course at UC Berkeley, Spring’08.

Source URL: stat.wharton.upenn.edu

• Machine learning
• Learning
• Analysis
• Artificial intelligence
• Mathematical analysis
• Inverse problems
• Statistical classification
• Convex optimization
• Support vector machine
• Mathematical optimization
• Regularization
• Statistical learning theory

VolumeDeform: Real-time Volumetric Non-rigid Reconstruction Matthias Innmann1 Michael Zollh¨ofer2 Matthias Nießner3 Christian Theobalt2 Marc Stamminger1

Source URL: arxiv.org

• Convex optimization
• Linear programming
• Operations research
• Plasticity
• 3D reconstruction

Surrogate loss functions, divergences and decentralized detection XuanLong Nguyen Department of Electrical Engineering and Computer Science U.C. Berkeley Advisors: Michael Jordan & Martin Wainwright

Source URL: dept.stat.lsa.umich.edu

• Mathematical optimization
• Statistics
• Analysis
• Mathematical analysis
• Statistical distance
• Operations research
• KullbackLeibler divergence
• Convex analysis
• Divergence
• Convex optimization

Analysis of Newton-Raphson Consensus for multi-agent convex optimization under asynchronous and lossy communications Ruggero Carli Giuseppe Notarstefano

Source URL: automatica.dei.unipd.it

A Lower Bound for the Optimization of Finite Sums Alekh Agarwal Microsoft Research NYC, New York, NY. ALEKHA @ MICROSOFT. COM

Source URL: jmlr.org

• Mathematical optimization
• Operations research
• Convex analysis
• Convex optimization
• Gradient descent
• Big O notation
• Regularized least squares
• Random coordinate descent
• Online machine learning

ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models Sanjay Krishnan, Jiannan Wang, Eugene Wu † , Michael J. Franklin, Ken Goldberg UC Berkeley, † Columbia University {sanjaykrishnan, jnwang, fran

Source URL: sampleclean.org

• Statistics
• Statistical theory
• Estimator
• Statistical inference
• M-estimators
• Convex optimization
• Stochastic gradient descent
• Bias of an estimator
• Loss function
• Data analysis
• Linear regression
• Generalised Hough transform