Overview
Syllabus
Intro
Positioning this Tutorial
Working Definitions
Full Information Online Learning
Setup
OCO Problem
Design Principle
Online Gradient Descent (OGD) Algorithm
Online Gradient Descent Result
Proof of OGD regret bound (ctd)
OGD Discussion
From Learning Parameters to Picking Actions
Let's apply what we know
Exponential Weigths / Hedge Algorithm Algorithm: Exponential Weights (EW)
EW Analysis Applying Hoeding's Lemma to the loss of each round gives
Summary so far Balancing act "model complexity vs "overfitting
FTRL/MD "sneak peek"
FTRL/MD sneak peak performance Algorithm: Follow the Regularised Leader (FTRL)
Quadratic Losses
Curvature assumptions
ONS Algorithm
ONS Performance
ONS Discussion
Offline Optimisation
Online to Batch Assumption: stochastic setting
Computing Saddle Points
Application 3: Saddle Point Algorithm Algorithm: approximate saddle point solver
Application 3: Saddle Point Analysis
Conclusion
Taught by
Simons Institute