12 papers
An Optimization Framework for Certain Separable Problems using Neural Networks
Rohit Negi, Soummya Kar
This paper studies a class of parametric constrained optimization problems that are motivated by applications in real time applications. Under a parameter-separable problem structu…
Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs
Dragana Bajovic, Dusan Jakovetic, Soummya Kar +1
We present an algorithm for distributed estimation of an unknown vector parameter in the presence of heavy-tailed observation and communicati…
Multi-Period Sparse Optimization for Proactive Grid Blackout Diagnosis
Qinghua Ma, Reetam Sen Biswas, Denis Osipov +3
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses…
Stability Analysis of a B-Spline Deep Neural Operator for Nonlinear Systems
Raffaele Romagnoli, Soummya Kar
This paper investigates the stability properties of neural operators through the structured representation offered by the Hybrid B-spline Deep Neural Operator (HBDNO). While existi…
Cyber-Resilient System Identification for Power Grid through Bayesian Integration
Shimiao Li, Guannan Qu, Bryan Hooi +3
Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape. Advances in snapshot-based system identification approaches have enable…
Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts
Jihoon Lee, Hoyeon Moon, Kevin Zhai +6
Diffusion-based large language models (dLLMs) are trained flexibly to model extreme dependence in the data distribution; however, how to best utilize this information at inference…