Rajat Sen

I am a Research Scientist at Google. My areas of interest are bandit algorithms, black box optimization and time-series forecasting. I obtained my PhD. at UT Austin, where I was lucky to be advised by Dr. Sanjay Shakkottai.

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    Google
A decoder-only foundation model for time-series forecasting
Rajat Sen
Weihao Kong
Yichen Zhou
Abhimanyu Das
2024
Black-Box Optimization of Unimodal Functions
Abhimanyu Das
Ashok Cutkosky
Chansoo Lee
Rajat Sen
Weihao Kong
UAI 2023 (2023)
Efficient List-Decodable Regression using Batches
Abhimanyu Das
Ayush Jain
Rajat Sen
Weihao Kong
Efficient List-Decodable Regression using Batches, ICML (2023)
Long Horizon Forecasting with TiDE: Time-series Dense Encoder
Abhimanyu Das
Andrew Leach
Rajat Sen
Rose Yu
Weihao Kong
Transactions on Machine Learning Research (2023)
A top-down approach to hierarchically coherent probabilistic forecasting
Abhimanyu Das
Biswajit Paria
Rajat Sen
Weihao Kong
UAI 2023 (2023)
On Learning Mixtures of Linear Regressions in a Non-Realizable Setting
Arya Mazumdar
Avishek Ghosh
Rajat Sen
Soumyabrata Pal
On Learning Mixture of Linear Regressions in the Non-Realizable Setting (2022)
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
Abhimanyu Das
Pranjal Awasthi
Rajat Sen
Weihao Kong
NeurIPS 2022 (2022)
On the benefits of Maximum Likelihood Estimation for Regression and Forecasting
Abhimanyu Das
Ananda Theertha Suresh
Pranjal Awasthi
Rajat Sen
ICLR 2022 (2022)
Top-k eXtreme Contextual Bandits with Arm Hierarchy
Rajat Sen
Alexander Rakhlin
Lexing Ying
Rahul Kidambi
Daniel Hill
Dean Foster
Inderjit Dhillon
Top-k eXtreme Contextual Bandits with Arm Hierarchy, International Conference on Machine Learning (2021) (to appear)
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