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 is a PhD Student at Computer Science and Engineering department of IIT Delhi. He is advised by Dr. Parag Singla and Dr. 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.

_______________________________________________
Lisa_seminaires mailing list
Lisa_seminaires@iro.umontreal.ca
https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires