3 citations · 11 across the 7 of their papers we have counts for
5 papers · 1 filter
TSFM Meets LLM: Context as Covariates
Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal +1
Textual information is ubiquitous in real-world time series datasets, appearing as news, reports, and annotations that hold key predictive signals not deducible from numerical hist…
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
Shubhankar Agarwal, Hamzah I. Khan, Sandeep P. Chinchali +1
Saddle point optimization is a critical problem employed in numerous real-world applications, including portfolio optimization, generative adversarial networks, and robotics. It ha…
Constrained Posterior Sampling: Time Series Generation with Hard Constraints
Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout +2
Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications,…
Time Weaver: A Conditional Time Series Generation Model
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin +2
Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winte…
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
Junette Hsin, Shubhankar Agarwal, Adam Thorpe +2
In this paper, we address the challenge of deriving dynamical models from sparse and noisy data. High-quality data is crucial for symbolic regression algorithms; limited and noisy…