I am a fourth year PhD student in the Machine Learning Department at
Carnegie Mellon University. I am fortunate to be advised by
Barnabás Póczos and
Jeff Schneider. I am
broadly interested in statistical machine learning and some aspects
of deep learning. More specifically, I am interested in Bayesian optimization,
bandits, and sequential decision making.
I graduated from the Indian Institute of Technology Kharagpur
with a combined bachelors and masters in Computer Science and Engineering.
I was advised by Pabitra Mitra
for my undergraduate thesis.
Outside of work, I am a regular boulderer (≤ V4),
and sometimes paint on my iPad.
Cost-Aware Bayesian Optimization via Information Directed Sampling
Biswajit Paria, Willie Neiswanger, Ramina Ghods, Jeff Schneider, Barnabás Póczos
ICML Workshop on Real World Experiment Design and Active Learning, 2020
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E.H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
International Conference on Learning Representations, 2020
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria,
Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing
Journal of Machine Learning Research, 2020
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
Uncertainty in Artificial Intelligence, 2019 (oral)
Forward Stagewise Additive Model for Collaborative Multiview Boosting
Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas
IEEE Transactions on Neural Networks and Learning Systems, 2016
Analytic Connectivity in General Hypergraphs
Ashwin Guha, Muni Sreenivas Pydi, Biswajit Paria, Ambedkar Dukkipati
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
Biswajit Paria, K. M. Annervaz, Ambedkar Dukkipati, Ankush Chatterjee, Sanjay Podder