Search Results for author:
Found 18 papers, 2 papers with code
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification.
Forecasting subcritical cylinder wakes with Fourier Neural Operators
The learned FNO solution operator can be evaluated in milliseconds, potentially enabling faster-than-real-time modeling for predictive flow control in physical systems.
Thompson Sampling Achieves $\tilde O(\sqrt{T})$ Regret in Linear Quadratic Control
By carefully prescribing an early exploration strategy and a policy update rule, we show that TS achieves order-optimal regret in adaptive control of multidimensional stabilizable LQRs.
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits their applicability for the control of safety-critical systems.
Optimal Competitive-Ratio Control
The key techniques that underpin our explicit solution is a reduction of the control problem to a Nehari problem, along with a novel factorization of the clairvoyant controller's cost.
Explicit Regularization via Regularizer Mirror Descent
RMD starts with a standard cost which is the sum of the training loss and a convex regularizer of the weights.
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems
Using these guarantees, we design adaptive control algorithms for unknown ARX systems with arbitrary strongly convex or convex quadratic regulating costs.
Regret-Optimal LQR Control
Motivated by competitive analysis in online learning, as a criterion for controller design we introduce the dynamic regret, defined as the difference between the LQR cost of a causal controller (that has only access to past disturbances) and the LQR cost of the \emph{unique} clairvoyant one (that has also access to future disturbances) that is known to dominate all other controllers.
Stable Online Control of Linear Time-Varying Systems
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems
In this model, each state variable is updated randomly and asynchronously with some probability according to the underlying system dynamics.
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems.
Model-based Reinforcement Learning
reinforcement-learning
+1
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of system identification and adaptive control in partially observable linear dynamical systems.
Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting
We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori.
Regret Minimization in Partially Observable Linear Quadratic Control
We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control.
Stochastic Mirror Descent on Overparameterized Nonlinear Models
On the theory side, we show that in the overparameterized nonlinear setting, if the initialization is close enough to the manifold of global optima, SMD with sufficiently small step size converges to a global minimum that is approximately the closest global minimum in Bregman divergence, thus attaining approximate implicit regularization.
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (parameter vectors that perfectly interpolate the training data).
Stochastic Linear Bandits with Hidden Low Rank Structure
We modify the image classification task into the SLB setting and empirically show that, when a pre-trained DNN provides the high dimensional feature representations, deploying PSLB results in significant reduction of regret and faster convergence to an accurate model compared to state-of-art algorithm.
