3 citations · 11 across the 7 of their papers we have counts for
8 papers
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…
Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection
Oguzhan Akcin, Po-han Li, Shubhankar Agarwal +1
Fleets of networked autonomous vehicles (AVs) collect terabytes of sensory data, which is often transmitted to central servers (the ''cloud'') for training machine learning (ML) mo…
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
Shubhankar Agarwal, David Fridovich-Keil, Sandeep P. Chinchali
Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to pl…
Synthesizing Adversarial Visual Scenarios for Model-Based Robotic Control
Shubhankar Agarwal, Sandeep P. Chinchali
Today's robots often interface with data-driven perception and planning models with classical model-predictive controllers (MPC). Often, such learned perception/planning models pro…
Imitative Planning using Conditional Normalizing Flow
Shubhankar Agarwal, Harshit Sikchi, Cole Gulino +2
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampli…