1 citations · 1 across the 9 of their papers we have counts for
10 papers
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning
Anutam Srinivasan, Siddhant Panse, Glen Chou
Robust adaptive control is vital for keeping robots safe while actively reducing uncertainty in unfamiliar settings. However, traditional methods are often conservative since they…
GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics
Jeffrey Fang, Keyi Shen, Anutam Srinivasan +1
This paper studies real-time robust optimal control for uncertain nonlinear systems, where linear time-varying (LTV) approximations make planning tractable but require sound linear…
Towards Auditing AI Systems in the Wild
Aditya T. Vadlamani, Anutam Srinivasan, Srinivasan Parthasarathy
AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users…
Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
Devesh Nath, Anutam Srinivasan, Haoran Yin +3
We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an actio…
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
Anutam Srinivasan, Antoine Leeman, Glen Chou
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensur…
Extending Temporal Disturbance Estimations For Magnetic Anomaly Navigation and Mapping
Anutam Srinivasan, Aaron Nielsen
Slow-moving vehicles relying on crustal magnetic anomaly navigation (MagNav) or vehicles revisiting the same location in a short time - such as those used for surveys in magnetic a…
FedCF: Fair Federated Conformal Prediction
Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi +1
Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabilistic guarantees on the coverag…
A Generic Framework for Conformal Fairness
Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker +2
Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding th…
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
Yuntian He, Pranav Maneriker, Anutam Srinivasan +2
Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural n…
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan +3
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has bu…