Vedant Shah

I am a Visiting Researcher at Mila - Quebec AI Institute where I work closely with Anirudh Goyal and others in Prof. Yoshua Bengio's group.

I completed my undergraduate studies in Electronics and Communications Engineering from BITS Pilani, Goa Campus where I was advised by Prof. Ashwin Srinivasan and Prof. Tanmay Verlekar at APPCAIR. I also spent a summer interning with Dr. Gautam Shroff at TCS Research and was selected for Google Summer of Code 2021.

At BITS, I was the Vice President of the Society for Artificial Intelligence and Deep Learning and Teaching Head of the Electronics and Robotics Club, BITS Goa.

Email  /  CV  /  Scholar  /  LinkedIn  /  Twitter  /  Github

profile photo
Recent News

Broadly, I am interested in improving the and sample efficiency and out of distribution generalization abilities of Reinforcement Learning and Deep Learning agents. To this end, I am interested in looking into approaches based on human cognitive inductive biases, meta learning and neurosymbolic methods.

Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
Dianbo Liu*, Vedant Shah*, Oussama Boussif*, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio,
ICLR 2023
arXiv / Talk

Investigating the problem of environmental heterogeneity and using behvioural priors to tackle the problems of coordination and heterogeneity in MARL

Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss
Vedant Shah*, Aditya Agarwal*, Lovekesh Vig , Ashwin Srinivasan, Gautam Shroff, Tanmay Verlekar,
Under Review

Using corrupted pretraining and semantic loss for obtaining explainable predictions in terms of human intelligible concepts by composing a symbolic and a neural module together.

Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!
Vedant Shah, Gautam Shroff
I (Still) Can't Believe It's Not Better! Workshop, NeurIPS 2021
arXiv / Poster

We train state of the art deep learning models on financial data using a data augmentation with synthetic data and meta learning.
ARIMA still beats the deep learning models in forecasting market data.

Adapting Deep Neural Networks for Pedestrian-Detection to Low-Light Conditions without Re-training
Vedant Shah, Anmol Agarwal Tanmay Tulsidas Verlekar Raghavendra Singh
Workshop on Traditional Computer Vision in the Age of Deep Learning, ICCV 2021
PDF / Slides

We develop a real time pre-processing pipeline for helping deep neural networks in pedestrian detection in low-light conditions.

Sep 2021 - Present
Research Intern
Advisors - Dr. Anirudh Goyal and Prof. Yoshua Bengio
Jan 2022 - Aug 2022
Undergraduate Researcher
Advisor - Prof. Ashwin Srinivasan

Jan 2021 - Aug 2021
Undergraduate Researcher
Advisor - Prof. Tanmay Verlekar
May 2021 - Sep 2021
Research Intern
Advisor - Dr. Gautam Shroff
May 2021 - Aug 2021
Student Developer
Organisation - GFOSS (Suborganisation - OpenDR)
Aug. 2020 - Nov. 2020
Research Intern
Advisor - Prof. GC Nandi

Original Templates: 1,2