[Theory] REMINDER: 3/1 Talks at TTIC: Dylan Foster, MIT

[Mary Marre]

*When:*      Monday, March 1st at* 11:10 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_xtwk0PfYR963X1A2ewhnQw>*)


*Who: *       Dylan Foster, MIT


*Title:*         Bridging Learning and Decision Making

*Abstract: *Machine learning is becoming widely used in decision making, in
domains ranging from personalized medicine and mobile health to online
education and recommendation systems. While (supervised) machine learning
traditionally excels at prediction problems, decision making requires
answering questions that are counterfactual in nature, and ignoring this
mismatch leads to unreliable decisions. As a consequence, our understanding
of the algorithmic foundations for data-driven decision making is limited,
and efficient algorithms are typically developed on an ad hoc basis. Can we
bridge this gap and make decision making as easy as machine learning?

Focusing on the contextual bandit, a core problem in data-driven decision
making, we bridge the gap by providing the first optimal and efficient
reduction to supervised machine learning. The algorithm allows users to
seamlessly apply off-the-shelf supervised learning models and methods to
make decisions on the fly, and has been implemented in widely-used,
industry-standard tools for decision making.

Our results advance a broader program to develop a universal algorithm
design paradigm for data-driven decision making. I will close the talk by
discussing challenges and opportunities in building such a framework,
including efforts to extend our developments to difficult reinforcement
learning problems in large state spaces.

*Bio: *Dylan Foster is a postdoctoral fellow at the MIT Institute for
Foundations of Data Science. He holds a PhD in computer science from
Cornell University, where he was advised by Karthik Sridharan. He has
received several awards, including the best paper award at COLT (2019),
best student paper award at COLT (2018, 2019), Facebook PhD fellowship, and
NDSEG PhD fellowship.

His research focuses on problems at the intersection of learning and
decision making.


*Host:* Avrim Blum <avrim at ttic.edu>


Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Mon, Feb 22, 2021 at 8:03 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Monday, March 1st at* 11:10 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_xtwk0PfYR963X1A2ewhnQw>*
> )
>
>
> *Who: *       Dylan Foster, MIT
>
>
> *Title:*         Bridging Learning and Decision Making
>
> *Abstract: *Machine learning is becoming widely used in decision making,
> in domains ranging from personalized medicine and mobile health to online
> education and recommendation systems. While (supervised) machine learning
> traditionally excels at prediction problems, decision making requires
> answering questions that are counterfactual in nature, and ignoring this
> mismatch leads to unreliable decisions. As a consequence, our understanding
> of the algorithmic foundations for data-driven decision making is limited,
> and efficient algorithms are typically developed on an ad hoc basis. Can we
> bridge this gap and make decision making as easy as machine learning?
>
> Focusing on the contextual bandit, a core problem in data-driven decision
> making, we bridge the gap by providing the first optimal and efficient
> reduction to supervised machine learning. The algorithm allows users to
> seamlessly apply off-the-shelf supervised learning models and methods to
> make decisions on the fly, and has been implemented in widely-used,
> industry-standard tools for decision making.
>
> Our results advance a broader program to develop a universal algorithm
> design paradigm for data-driven decision making. I will close the talk by
> discussing challenges and opportunities in building such a framework,
> including efforts to extend our developments to difficult reinforcement
> learning problems in large state spaces.
>
> *Bio: *Dylan Foster is a postdoctoral fellow at the MIT Institute for
> Foundations of Data Science. He holds a PhD in computer science from
> Cornell University, where he was advised by Karthik Sridharan. He has
> received several awards, including the best paper award at COLT (2019),
> best student paper award at COLT (2018, 2019), Facebook PhD fellowship, and
> NDSEG PhD fellowship.
>
> His research focuses on problems at the intersection of learning and
> decision making.
>
>
> *Host:* Avrim Blum <avrim at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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