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DTEND:20221117T120000Z
UID:9947b9637c81b3af3f08a11097d97539-358
DTSTAMP:19700101T120011Z
DESCRIPTION:The Role of Adaptivity in Learning and Decision-Making
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/358/the-role-of-adaptivity-in-learning-and-decision-making/
SUMMARY:In many machine learning applications the learner is faced with a *stochastic environment* and it (sequentially) probes or influences the environment so as to optimize a given objective function. Examples of such applications include recommendation systems, web advertising, viral marketing, clinical trials, search ranking etc. For instance, in recommendation systems, the learner attempts to identify good recommendations by probing the stochastic preferences of users. Similarly, in viral marketing, the learner attempts to spread information through a social network using marketing campaigns that influence (stochastic) subsets of the network. 

Most existing learning algorithms for these applications operate in one of two settings: (1) non-adaptive, and (2) fully adaptive. In the non-adaptive setting, all the selections/probes are completely determined ahead of time. However, these a priori selections might be inefficient as some of them might be unnecessary in hindsight. In the fully adaptive setting, the selection policy is updated after each observation from the environment. However, this fully adaptive setting might not be practical in many applications due to delays in receiving observations from many parallel sources. In this talk we introduce a semi-adaptive setting that interpolates between these two extreme settings for a wide range of learning and decision-making problems such as best arm identification in multi-armed bandits, ranking from pairwise comparisons, dueling bandits, and stochastic submodular maximization. We show that semi-adaptive policies enjoy the power of fully adaptive policies while requiring very few updates to the selection/probing rules. We also identify the trade-offs between rounds of adaptivity and performance.
DTSTART:20221117T120000Z
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