Sotabase

Career

· Software Engineer III, Google2022–
· Intern, Google Research2020–2020
· Intern, Snap Research2018–2018
· M.S., Ph.D. in Machine Learning, Carnegie Mellon University2017–2022
· Intern, University of Southern California2015–2015
· 5-year Bachelors and Masters in Computer Science and Engineering, Indian Institute of Technology Kharagpur2012–2017

Publications (21)

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Journal of machine learning research · 2019
190
cited
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Conference on Uncertainty in Artificial Intelligence · 2018
158
cited
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
Cell Reports Physical Science · 2019
143
cited
Minimizing FLOPs to Learn Efficient Sparse Representations
International Conference on Learning Representations · 2020
80
cited
Gradient-based Constrained Sampling from Language Models
Conference on Empirical Methods in Natural Language Processing · 2022
68
cited
An Experimental Design Perspective on Model-Based Reinforcement Learning
International Conference on Learning Representations · 2021
23
cited
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
arXiv.org · 2018
19
cited
Hierarchically Regularized Deep Forecasting
arXiv.org · 2021
18
cited
Constrained Sampling from Language Models via Langevin Dynamics in Embedding Spaces
arXiv.org · 2022
16
cited
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
arXiv.org · 2016
13
cited
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Conference on Uncertainty in Artificial Intelligence · 2022
8
cited
Forward Stagewise Additive Model for Collaborative Multiview Boosting
IEEE Transactions on Neural Networks and Learning Systems · 2016
6
cited
A Deep Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting
2022
4
cited
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting
arXiv.org · 2022
4
cited
Be Greedy – a Simple Algorithm for Blackbox Optimization using Neural Networks
4
cited
Analytic Connectivity in General Hypergraphs
arXiv.org · 2017
2
cited
Cost-Aware Bayesian Optimization via Information Directed Sampling
2020
2
cited
PolicyGAN: Training generative adversarial networks using policy gradient
International Conference on Advances in Pattern Recognition · 2017
2
cited
Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization
2020
COMA-boost: co-operative multi agent AdaBoost
Indian Conference on Computer Vision, Graphics & Image Processing · 2016
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Biswajit Paria | Researcher Profile | Sotabase | Sotabase