Bandit Algorithm (Online Machine Learning)
In many scenarios one faces uncertain environments where a-priori the best action to play is unknown. How to obtain best possible reward/utility in such scenarios. One natural way is to first explore the environment and to identify the `best’ actions and exploit them. However, this give raise to an exploration vs exploitation dilemma, where on hand hand we need to do sufficient explorations to identify the best action so that we are confident about its optimality, and on the other hand, best actions need to exploited more number of times to obtain higher reward. In this course we will study many bandit algorithms that balance exploration and exploitation well in various random environment to accumulate good rewards over the duration of play. Bandit algorithms find applications in online advertising, recommendation systems, auctions, routing, e-commerce or in any filed online scenarios where information can be gather in an increment fashion.
INTENDED AUDIENCE :Computer Sceince, Electrical Engineering, Operations Research, Mathematics and Statistics
PREREQUISITES :Basics of Probability Theory and Optimization
INDUSTRIES SUPPORT :All companies related to Internet Technologies (ex. Google, Microsoft, Flipkart, Ola, Amazon, etc.)
COURSE LAYOUT Week 1:Introduction to Bandit Algorithms. From Batch to Online SettingWeek 2:Adversarial Setting with Full information (Halving, WM Algorithm )Week 3:Adversarial Setting with Bandit InformationWeek 4:Regret lower bounds for adversarial Setting
Week 5:Introduction to Stochastic Setting and various regret notionsWeek 6:A primer on Concentration inequalitiesWeek 7:Stochastic Bandit Algorithms UCB, KL-UCBWeek 8:Lower bounds for stochastic Bandits
Week 9:Introductions to contextual banditsWeek 10:Overview of contextual bandit algorithmsWeek 11:Introduction to pure exploration setups (fixed confidence vs budget)Week 12:Algorithms for pure explorations (LUCB, KL-LUCB, lil’UCB).