I am Yash Sirvi, a final-year undergraduate in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur. I worked with Prof. Partha Pratim Chakrabarti on scalable learning-based solutions for NP-complete problems, focusing on the Travelling Salesman Problem. Under the guidance of Prof. Debashish Chakravarty, I developed game-theoretic planners for autonomous racing systems. I am also doing my Bachelor's Thesis under the supervision of Prof. Aritra Hazra to improve MARL techniques, addressing scalability and stability in multi-agent settings.
I have also interned at Quadeye Securities LLP as a Systems Engineer. There, I worked on optimizing the compiler pipeline for efficient regression test selection, designing algorithms to detect test and compilation failures early.
Research
I'm interested in Algorithmic Optimization, Multi-Agent Reinforcement Learning (MARL), Planning, and Robotics, with an emphasis on scalability, efficiency, and real-world applications. I aim to develop innovative algorithms to tackle challenges in autonomous systems and multi-agent learning.
Publications
Decoupling Vertical Federated Learning
using Local Self-Supervision
Avi Amalanshu, Yash Sirvi, et al. Presented at
NeurIPS 2024 Workshop: Self-Supervised
Learning, focusing on fault-tolerant federated learning on
vertically partitioned data.
Entity Augmentation for Efficient
Classification of Vertically Partitioned
Data
Yash Sirvi, et al. Published at
IJCAI 2024 GLOW Workshop, introducing novel
techniques to handle limited data overlap in
vertical federated learning.
DiffClone: Enhanced Behaviour Cloning in
Robotics
Sabariswaran Mani, Yash Sirvi, et al. A technical
report for
NeurIPS 2023 TOTO Benchmark Challenge
Proposing a diffusion-driven approach for policy
learning.
GitHub Code
Projects
Learning-Based Solutions for the Travelling
Salesman Problem
Developed scalable algorithms combining heuristics
and graph sparsification to achieve 40x speedup and
near-optimal results on large problem instances.
Project guide:
Prof. Partha Pratim Chakrabarti (Professor, IITKGP)
With:
Maitreyi Swaroop
(PhD, MLD CMU) and
Sourodeep Datta (CS Undergrad, IITKGP)
Novel Techniques for Scalable Multi-Agent
Reinforcement Learning (MARL)
Enhanced SAPG methods for multi-agent environments,
integrating centralized training with decentralized
execution for scalable solutions.
Project guide:
Prof. Aritra Hazra
(Assistant Professor, IITKGP)
With:
Ananye Agarwal (Phd,
MLD CMU).
The Chandrayaan Moon Mapping Challenge –
ISRO
Designed deep learning models for lunar surface
image super-resolution, enhancing resolution by 16x
with cutting-edge techniques.
GitHub Code