Somjit Nath

I am a PhD Student at McGill University/ Mila where I am advised by Professor Derek Nowrouzezahrai and Professor Samira Ebrahimi Kahou. I am currently working on representation learning for reinforcement learning. Previously, I was a Researcher at TCS Research, working under the supervision of Harshad Khadilkar, where I worked on applying Reinforcement Learning to Supply Chains and addressing the challenges that come with it.

I did my Masters from the University of Alberta where I was advised by Martha White. My thesis formulates a new approach for training Recurrent Neural Networks. I also worked on non-linear value function approximation for Reinforcement Learning. My undergrad was in Electrical Engineering from Jadavpur University, during which I worked mostly in the Internet of Things (IOT) domain along with medical imaging.

Email / CV / Twitter / Google Scholar / LinkedIn / Github

Latest News

Our work on Spectral Temporal Contrastive Learning has been accepted at NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice.
Our work on Prioritizing Samples in Reinforcement Learning with Reducible Loss has been accepted to NeurIPS 2023.
Excited to announce I joined Borealis AI as a Machine Learning Research Intern for Summer 2023.
Our work on Learning object-centric representations with General Value Functions has been accepted at ICML 2023.
Our paper on Exploration with General Value Functions has been accepted at AAMAS Main Track 2023.
Locally Constrained Representations in Reinforcement Learning (joint work with Prof. Samira Ebrahimi Kahou) accepted to Deep RL Workshop, Neurips 2022.
Prioritizing Samples in Reinforcement Learning with Reducible Loss (joint work with Shivakanth Sujit, Pedro Braga and Prof. Samira Ebrahimi Kahou) accepted to Deep RL Workshop, Neurips 2022.
Our work on Handling Uncertain Lead Times in Supply Chains has been accepted at the 15th European Workshop on Reinforcement Learning (EWRL 2022).

Research

My main interests lie in the field of reinforcement learning. My fascination for RL developed after taking Rich Sutton's course during my Masters. In the context of RL, I am also interested in representation learning, model based RL and exploration.

	Prioritizing Samples in Reinforcement Learning with Reducible Loss Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga, Samira Ebrahimi Kahou Neural Information Processing Systems (NeurIPS), 2023 project / paper / code / slides
	Discovering Object-Centric Generalized Value Functions From Pixels Somjit Nath, Gopeshh Raaj Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou International Conference on Machine Learning (ICML), 2023 paper / code
	Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning Durgesh Kalwar, Omkar Shelke, Somjit Nath, Hardik Meisheri, Harshad Khadilkar International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023 paper / poster
	A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management Hardik Meisheri, Somjit Nath, Mayank Baranwal, Harshad Khadilkar European Workshop on Reinforcement Learning (EWRL), 2022 paper
	Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays Somjit Nath, Mayank Baranwal, Harshad Khadilkar Conference on Information and Knowledge Management (CIKM), 2021 paper / code / slides / poster
	Scalable multi-product inventory control with lead time constraints using reinforcement learning Hardik Meisheri, Nazneen N Sultana, Mayank Baranwal, Vinita Baniwal, Somjit Nath, Satyam Verma, Balaraman Ravindran, Harshad Khadilkar Neural Computing and Applications (NCA) , 2021. paper
	SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning Somjit Nath, Richa Verma, Abhik Ray, Harshad Khadilkar International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021. paper
	Training Recurrent Neural Networks Online by Learning Explicit State Variables Somjit Nath, Vincent Liu, Alan Chan, Xin Li, Adam White, Martha White International Conference on Learning Representations(ICLR), 2020. paper / slides / code
	Two-Timescale Networks for Nonlinear Value Function Approximation Wesley Chung, Somjit Nath, Ajin Joseph, Martha White International Conference on Learning Representations(ICLR), 2019. paper
	A Fixed-Point Formulation for Recurrent Neural Networks Somjit Nath, Taher Jafferjee and Martha White Continual Learning Workshop, Neural Information Processing Systems(NeurIPS) , 2018. paper
	Rejection Sampling for Off-Policy Learning Wesley Chung, Sina Ghiassian, Somjit Nath, and Martha White Continual Learning Workshop, Neural Information Processing Systems(NeurIPS) , 2018. paper
	Smartphone Camera Based Analysis of ELISA using Artificial Neural Network Somjit Nath, Subhannita Sarcar, Biswendu Chatterjee, Nabendu Chatterjee, Rhishita Chourashi IET Computer Vision , 2018. paper
	Arduino Based Door Unlocking System with Real Time Control Somjit Nath, Paramita Banerjee, Rathindra Nath Biswas, Swarup Kumar Mitra and Mrinal Kanti Naskar International Conference on Contemporary Computing and Informatics (IC3I) , 2016. paper

Source code and style from Jon Barron's website.