[Colloquium] TTIC Talk: Ambuj Tewari

[Julia MacGlashan]

When:             *Thursday, Mar 18 @ 11:00am *

Where:           * TTIC Conference Room #526*, 6045 S Kenwood Ave


Who:               *Ambuj Tewari*, TTIC


Title:          *      **Risk, Regularization and Regret: A View through the
Lens of Strong Convexity*****

 There are two related and complementary approaches for studying the
theoretical foundations of learning. One is probabilistic in nature and the
other game-theoretic and adversarial. The first approach attempts to find a
predictor with low expected loss, or risk, while the other attempts to
minimize the regret on each individual sequences of loss functions.
Regularization plays a central role in both approaches. Starting from a high
level overview of the two approaches and definitions of risk and regret, I
will present examples of regularizers commonly used in single task,
multi-task & multi-class learning. Then I will try to show how the concept
of strong convexity allows us to obtain risk bounds, derive new algorithms
and regret bounds, and understand the relationship between probabilistic and
adversarial models of learning.
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