[Lisa_seminaires] [Tea Talk] Ankit Anand (IIT Delhi) Fri Apr 20 10:30AM AA1360

Yoshua Bengio yoshua.umontreal at gmail.com
Ven 20 Avr 08:20:52 EDT 2018


I am considering to recruit Ankit for a post-doc in the area of 'AI for
good' applications, especially humanitarian applications.

Feedback from people who would meet him would be greatly appreciated.

If you'd like to meet with Ankit, please sign up here:
> https://doodle.com/poll/98taifuumd5wmzan


-- Yoshua

2018-04-17 13:58 GMT-04:00 Michael Noukhovitch <mnoukhov at gmail.com>:

> If you'd like to meet with Ankit, please sign up here:
> https://doodle.com/poll/98taifuumd5wmzan
>
> Cheers,
> Michael
>
> On Mon, Apr 16, 2018 at 6:28 PM Michael Noukhovitch <mnoukhov at gmail.com>
> wrote:
>
>> This week we have *Ankit Anand* from *IIT Delhi* giving a talk on *Friday
>> April 20* at *10:30AM* in room *AA1360*.
>>
>> ¡ʞlɐʇ ǝɥʇ oʇ ǝɯoɔ
>> Michael
>>
>> *TITLE* Symmetry Aware Decision Making and Inference
>>
>> *KEYWORDS *AI Planning, Graphical Models, Image Segmentation/Stereovision
>>
>> *ABSTRACT*
>> Many traditional AI algorithms fail to scale as the problem size
>> increases exponentially with the number of features. This work focuses on
>> using symmetries and abstractions to reduce computation and memory overhead
>> in state-of-the-art AI and ML problems. Specifically, we exploit symmetries
>> in sequential decision making under uncertainty (Monte Carlo Tree Search
>> algorithms like UCT) and probabilistic inference. Abstractions and
>> symmetries though well studied in the past have mostly been used as a
>> preprocessing step. Our work focuses to learn symmetries and abstractions
>> from scratch within the algorithm itself and use the learned abstractions
>> and symmetries for further speeding up the state-of-the-art algorithms.
>> Specifically,
>>
>> a) We propose ASAP-UCT and OGA-UCT which uses novel notion of
>> Abstractions in Monte Carlo Tree Search Algorithms like UCT
>>
>> b) We apply symmetry aware graphical model inference in close to
>> state-of-the-art Stereovision and Image Segmentation algorithms to gain
>> impressive speed-ups without loss in quality
>>
>> c) We propose novel notion of symmetries -"Contextual Symmetries" and
>> "Non-Count Symmetries" and use those for faster mixing in MCMC algorithms
>>
>> Moreover, our strategies are computationally efficient and provide gains
>> up to 25% in International Probabilistic Planning Competition(IPPC) domains
>> while speeding up MAP inference in Graphical Models by 10x for Stereovision
>> and Segmentation without much loss in quality.
>>
>> *BIO*
>> Ankit Anand <http://www.cse.iitd.ernet.in/~ankitanand/> is a PhD Student
>> at Computer Science and Engineering department of IIT Delhi. He is advised
>> by Dr. Parag Singla <http://www.cse.iitd.ac.in/~parags/> and Dr. Mausam
>> <http://homes.cs.washington.edu/~mausam/>. His primary research
>> interests are Artificial Intelligence Planning, Reinforcement Learning and
>> Probabilistic Graphical Models. He has worked extensively on applications
>> of Image Segmentation, Stereovision and Sequential Decision Making tasks.
>>
>> He is a recipient of Tata Consultancy Services Ph.D Fellowship from
>> 2013-2016 and has published in top tier conferences like IJCAI, AAMAS,
>> ICAPS and AISTATS. He is also a winner of best paper award at StarAI
>> workshop at IJCAI'16.
>>
>
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