Bhushan Gopaluni — accepted AI-conference papers

2025

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation

Shuyuan Wang, Philip D Loewen, Michael Forbes, Bhushan Gopaluni, Wei Pan

ICML 2025poster

While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizo…

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2020

Almost Surely Stable Deep Dynamics

Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni

NeurIPS 2020spotlight

We introduce a method for learning provably stable deep neural network based dynamic models from observed data. Specifically, we consider discrete-time stochastic dynamic models, as they are of particular interest in practical applications such as estimation and control. However, these aspects exace…

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