Publications (12)
Projection-free Adaptive Regret with Membership Oracles
Zhou Lu, Nataly Brukhim, Paula Gradu +1
In the framework of online convex optimization, most iterative algorithms require the computation of projections onto convex sets, which can be computationally expensive. To tackle…
Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
Jessica Dai, Paula Gradu, Christopher Harshaw
From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as the…
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Daniel Suo, Naman Agarwal, Wenhan Xia +12
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challe…
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason Choi +3
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-di…
Valid Inference After Causal Discovery
Paula Gradu, Tijana Zrnic, Yixin Wang +1
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challeng…
Non-Stochastic Control with Bandit Feedback
Paula Gradu, John Hallman, Elad Hazan
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 fu…