Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Gradu, Paula"'
Autor:
Gradu, Paula, Recht, Benjamin
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 the cumulative dose subject to constraints on well-being. We adapt a mo
Externí odkaz:
http://arxiv.org/abs/2309.11629
From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as they offer the possibility of improved precision over their non-adaptive
Externí odkaz:
http://arxiv.org/abs/2305.17187
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 this problem HK12 proposed the study of projection-free methods that
Externí odkaz:
http://arxiv.org/abs/2211.12638
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effect
Externí odkaz:
http://arxiv.org/abs/2208.05949
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. In order to avoid distribution shift when deploying
Externí odkaz:
http://arxiv.org/abs/2206.10524
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{qualitatively harder} than that of either unknown time-invariant or known time
Externí odkaz:
http://arxiv.org/abs/2202.07890
Autor:
Suo, Daniel, Agarwal, Naman, Xia, Wenhan, Chen, Xinyi, Ghai, Udaya, Yu, Alexander, Gradu, Paula, Singh, Karan, Zhang, Cyril, Minasyan, Edgar, LaChance, Julienne, Zajdel, Tom, Schottdorf, Manuel, Cohen, Daniel, Hazan, Elad
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 challenges remain. We frame these as a control problem: ventilators must let
Externí odkaz:
http://arxiv.org/abs/2111.10434
Autor:
Gradu, Paula, Hallman, John, Suo, Daniel, Yu, Alex, Agarwal, Naman, Ghai, Udaya, Singh, Karan, Zhang, Cyril, Majumdar, Anirudha, Hazan, Elad
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 introduced environments allow auto-differentiation through the simulation dyn
Externí odkaz:
http://arxiv.org/abs/2102.09968
Autor:
Suo, Daniel, Agarwal, Naman, Xia, Wenhan, Chen, Xinyi, Ghai, Udaya, Yu, Alexander, Gradu, Paula, Singh, Karan, Zhang, Cyril, Minasyan, Edgar, LaChance, Julienne, Zajdel, Tom, Schottdorf, Manuel, Cohen, Daniel, Hazan, Elad
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. Ha
Externí odkaz:
http://arxiv.org/abs/2102.06779
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. For this problem, with either a known or unk
Externí odkaz:
http://arxiv.org/abs/2008.05523