[Colloquium] REMINDER: 2/20 Talks at TTIC: Adith Swaminathan, Microsoft Research AI

[Mary Marre via Colloquium]

 When:     Tuesday, February 20th at *11:00am*

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Adith Swaminathan, Microsoft Research AI


Title:       Learning from Logged Bandit Feedback

Abstract: Many impactful applications of machine learning are not just
about prediction, but are about putting learning systems in control of
selecting the right action at the right time (e.g., search engines,
recommender systems or automated trading platforms). These systems are both
producers and users of data -- the logs of the selected actions and their
outcomes (e.g., derived from clicks, ratings or revenue) can provide
valuable training data for learning the next generation of the system,
giving rise to some of the biggest datasets we have collected. Machine
learning in these settings is challenging since the system in operation
biases the log data through the actions it selects and outcomes remain
unknown for the actions not taken. Learning methods must, hence, reason
about how changes to the system will affect future outcomes. We will
summarize recent advances in these counterfactual learning techniques, and
demonstrate how deep neural networks can be trained in these settings
(ICLR’18).

Joint work with Thorsten Joachims and Maarten de Rijke.

Bio: Adith Swaminathan is a researcher in the Reinforcement Learning group
at Microsoft Research AI (Redmond). He studies techniques for
counterfactual reasoning in learning systems -- applications include
off-policy reinforcement learning, contextual bandits and understanding the
biases that confound user interaction data. He completed his PhD at Cornell
University (2017) and received a BTech from IIT Bombay (2010).


Host: Shubhendu Trivedi <shubhendu at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Wed, Feb 14, 2018 at 11:26 AM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Tuesday, February 20th at *11:00am*
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Adith Swaminathan, Microsoft Research AI
>
>
> Title:       Learning from Logged Bandit Feedback
>
> Abstract: Many impactful applications of machine learning are not just
> about prediction, but are about putting learning systems in control of
> selecting the right action at the right time (e.g., search engines,
> recommender systems or automated trading platforms). These systems are both
> producers and users of data -- the logs of the selected actions and their
> outcomes (e.g., derived from clicks, ratings or revenue) can provide
> valuable training data for learning the next generation of the system,
> giving rise to some of the biggest datasets we have collected. Machine
> learning in these settings is challenging since the system in operation
> biases the log data through the actions it selects and outcomes remain
> unknown for the actions not taken. Learning methods must, hence, reason
> about how changes to the system will affect future outcomes. We will
> summarize recent advances in these counterfactual learning techniques, and
> demonstrate how deep neural networks can be trained in these settings
> (ICLR’18).
>
> Joint work with Thorsten Joachims and Maarten de Rijke.
>
> Bio: Adith Swaminathan is a researcher in the Reinforcement Learning group
> at Microsoft Research AI (Redmond). He studies techniques for
> counterfactual reasoning in learning systems -- applications include
> off-policy reinforcement learning, contextual bandits and understanding the
> biases that confound user interaction data. He completed his PhD at Cornell
> University (2017) and received a BTech from IIT Bombay (2010).
>
>
> Host: Shubhendu Trivedi <shubhendu at ttic.edu>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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