14 citations · 41 across the 12 of their papers we have counts for
12 papers
From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
Jessica Dai, Paula Gradu, Inioluwa Deborah Raji +1
When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? We study the rep…
Online Control for Adaptive Tapering of Medications
Paula Gradu, Benjamin Recht
We investigate adaptive protocols for the elimination or reduction of the use of medications or addictive substances. We formalize this problem as online optimization, minimizing t…
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…
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…
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…
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…
Online Control of Unknown Time-Varying Dynamical Systems
Edgar Minasyan, Paula Gradu, Max Simchowitz +1
We study online control of time-varying linear systems with unknown dynamics in the nonstochastic control model. At a high level, we demonstrate that this setting is \emph{qualitat…
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…
Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
Paula Gradu, John Hallman, Daniel Suo +7
We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite. The introdu…
Machine Learning for Mechanical Ventilation Control
Daniel Suo, Naman Agarwal, Wenhan Xia +12
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 acco…
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…
Adaptive Regret for Control of Time-Varying Dynamics
Paula Gradu, Elad Hazan, Edgar Minasyan
We consider the problem of online control of systems with time-varying linear dynamics. This is a general formulation that is motivated by the use of local linearization in control…