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:
-- Yoshua
2018-04-17 13:58 GMT-04:00 Michael Noukhovitch mnoukhov@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@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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