25 citations · 36 across the 8 of their papers we have counts for
13 papers
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Reese Pathak, Rajat Sen, Nikhil Rao +3
We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these para…
On the benefits of maximum likelihood estimation for Regression and Forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen +1
We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach…
Hierarchically Regularized Deep Forecasting
Biswajit Paria, Rajat Sen, Amr Ahmed +1
Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series t…
Top- eXtreme Contextual Bandits with Arm Hierarchy
Rajat Sen, Alexander Rakhlin, Lexing Ying +4
Motivated by modern applications, such as online advertisement and recommender systems, we study the top- extreme contextual bandits problem, where the total number of arms can…
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Nishant Yadav, Rajat Sen, Daniel N. Hill +2
Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar. Previous queries in t…
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw, Rajat Sen, Karthikeyan Shanmugam +2
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs fr…