Search Results for author:
Found 9 papers, 2 papers with code
Non-convex online learning via algorithmic equivalence
We prove an $O(T^{\frac{2}{3}})$ regret bound for non-convex online gradient descent in this setting, answering this open problem.
A Regret Minimization Approach to Multi-Agent Control
We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
Robust Online Control with Model Misspecification
We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification.
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.
Generating Adversarial Disturbances for Controller Verification
We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it.
No-Regret Prediction in Marginally Stable Systems
This requires a refined regret analysis, including a structural lemma showing the current state of the system to be a small linear combination of past states, even if the state grows polynomially.
Exponentiated Gradient Meets Gradient Descent
The hypentropy has a natural spectral counterpart which we use to derive a family of matrix-based updates that bridge gradient methods and the multiplicative method for matrices.
