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Found 9 papers, 2 papers with code
Projection-free Adaptive Regret with Membership Oracles
The most common approach is based on the Frank-Wolfe method, that uses linear optimization computation in lieu of projections.
Valid Inference After Causal Discovery
Causal discovery and causal effect estimation are two fundamental tasks in causal inference.
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs.
Online Control of Unknown Time-Varying Dynamical Systems
On the positive side, we give an efficient algorithm that attains a sublinear regret bound against the class of Disturbance Response policies up to the aforementioned system variability term.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite.
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
Non-Stochastic Control with Bandit Feedback
We study the problem of controlling a linear dynamical system with adversarial perturbations where the only feedback available to the controller is the scalar loss, and the loss function itself is unknown.
Adaptive Regret for Control of Time-Varying Dynamics
Our main contribution is a novel efficient meta-algorithm: it converts a controller with sublinear regret bounds into one with sublinear {\it adaptive regret} bounds in the setting of time-varying linear dynamical systems.
