Kamyar Azizzadenesheli

research

∙ 05/13/2022

Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds

Executing safe and precise flight maneuvers in dynamic high-speed winds ...

Michael O'Connell, et al. ∙

research

∙ 05/06/2022

Generative Adversarial Neural Operators

We propose the generative adversarial neural operator (GANO), a generati...

Md Ashiqur Rahman, et al. ∙

research

∙ 04/23/2022

U-NO: U-shaped Neural Operators

Neural operators generalize classical neural networks to maps between in...

Md Ashiqur Rahman, et al. ∙

research

∙ 02/22/2022

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

FourCastNet, short for Fourier Forecasting Neural Network, is a global d...

Jaideep Pathak, et al. ∙

research

∙ 11/06/2021

Physics-Informed Neural Operator for Learning Partial Differential Equations

Machine learning methods have recently shown promise in solving partial ...

Zongyi Li, et al. ∙

research

∙ 09/03/2021

U-FNO – an enhanced Fourier neural operator based-deep learning model for multiphase flow

Numerical simulation of multiphase flow in porous media is essential for...

Gege Wen, et al. ∙

research

∙ 08/26/2021

Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems

Autoregressive exogenous (ARX) systems are the general class of input-ou...

Sahin Lale, et al. ∙

research

∙ 08/19/2021

Neural Operator: Learning Maps Between Function Spaces

The classical development of neural networks has primarily focused on le...

Nikola Kovachki, et al. ∙

research

∙ 08/11/2021

Seismic wave propagation and inversion with Neural Operators

Seismic wave propagation forms the basis for most aspects of seismologic...

Yan Yang, et al. ∙

research

∙ 06/13/2021

Markov Neural Operators for Learning Chaotic Systems

Chaotic systems are notoriously challenging to predict because of their ...

Zongyi Li, et al. ∙

research

∙ 06/11/2021

Meta-Adaptive Nonlinear Control: Theory and Algorithms

We present an online multi-task learning approach for adaptive nonlinear...

Guanya Shi, et al. ∙

research

∙ 04/18/2021

Off-Policy Risk Assessment in Contextual Bandits

To evaluate prospective contextual bandit policies when experimentation ...

Audrey Huang, et al. ∙

research

∙ 03/04/2021

On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk

In order to model risk aversion in reinforcement learning, an emerging l...

Audrey Huang, et al. ∙

research

∙ 02/10/2021

Multi-Agent Multi-Armed Bandits with Limited Communication

We consider the problem where N agents collaboratively interact with an ...

Mridul Agarwal, et al. ∙

research

∙ 01/09/2021

HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks

We introduce a scheme for probabilistic hypocenter inversion with Stein ...

Jonathan D. Smith, et al. ∙

research

∙ 11/29/2020

Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift

We study generalization under label shift in domain adaptation where the...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 10/18/2020

Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on le...

Zongyi Li, et al. ∙

research

∙ 07/23/2020

Explore More and Improve Regret in Linear Quadratic Regulators

Stabilizing the unknown dynamics of a control system and minimizing regr...

Sahin Lale, et al. ∙

research

∙ 06/28/2020

Deep Bayesian Quadrature Policy Optimization

We study the problem of obtaining accurate policy gradient estimates. Th...

Akella Ravi Tej, et al. ∙

research

∙ 06/18/2020

Competitive Policy Optimization

A core challenge in policy optimization in competitive Markov decision p...

Manish Prajapat, et al. ∙

research

∙ 06/16/2020

Multipole Graph Neural Operator for Parametric Partial Differential Equations

One of the main challenges in using deep learning-based methods for simu...

Zongyi Li, et al. ∙

research

∙ 05/01/2020

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...

Chiyu "Max" Jiang, et al. ∙

research

∙ 03/25/2020

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems

We study the problem of adaptive control in partially observable linear ...

Sahin Lale, et al. ∙

research

∙ 03/25/2020

EikoNet: Solving the Eikonal equation with Deep Neural Networks

The recent deep learning revolution has created an enormous opportunity ...

Jonathan D. Smith, et al. ∙

research

∙ 03/12/2020

Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems

We study the problem of adaptive control in partially observable linear ...

Sahin Lale, et al. ∙

research

∙ 03/07/2020

Neural Operator: Graph Kernel Network for Partial Differential Equations

The classical development of neural networks has been primarily for mapp...

Zongyi Li, et al. ∙

research

∙ 01/31/2020

Regret Minimization in Partially Observable Linear Quadratic Control

We study the problem of regret minimization in partially observable line...

Sahin Lale, et al. ∙

research

∙ 06/30/2019

Directivity Modes of Earthquake Populations with Unsupervised Learning

We present a novel approach for resolving modes of rupture directivity i...

Zachary E. Ross, et al. ∙

research

∙ 06/25/2019

Learning Causal State Representations of Partially Observable Environments

Intelligent agents can cope with sensory-rich environments by learning t...

Amy Zhang, et al. ∙

research

∙ 03/22/2019

Regularized Learning for Domain Adaptation under Label Shifts

We propose Regularized Learning under Label shifts (RLLS), a principled ...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 01/28/2019

Stochastic Linear Bandits with Hidden Low Rank Structure

High-dimensional representations often have a lower dimensional underlyi...

Sahin Lale, et al. ∙

research

∙ 11/19/2018

Neural Lander: Stable Drone Landing Control using Learned Dynamics

Precise trajectory control near ground is difficult for multi-rotor dron...

Guanya Shi, et al. ∙

research

∙ 10/18/2018

Trust Region Policy Optimization of POMDPs

We propose Generalized Trust Region Policy Optimization (GTRPO), a Reinf...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 10/11/2018

signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant

Training neural networks on large datasets can be accelerated by distrib...

Jeremy Bernstein, et al. ∙

research

∙ 06/15/2018

Sample-Efficient Deep RL with Generative Adversarial Tree Search

We propose Generative Adversarial Tree Search (GATS), a sample-efficient...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 03/05/2018

Stochastic Activation Pruning for Robust Adversarial Defense

Neural networks are known to be vulnerable to adversarial examples. Care...

Guneet S. Dhillon, et al. ∙

research

∙ 02/13/2018

signSGD: compressed optimisation for non-convex problems

Training large neural networks requires distributing learning across mul...

Jeremy Bernstein, et al. ∙

research

∙ 02/13/2018

Efficient Exploration through Bayesian Deep Q-Networks

We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 05/07/2017

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods

We propose a new reinforcement learning algorithm for partially observab...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 11/11/2016

Reinforcement Learning in Rich-Observation MDPs using Spectral Methods

Designing effective exploration-exploitation algorithms in Markov decisi...

Kamyar Azizzadenesheli, et al. ∙

research

∙ 02/25/2016

Reinforcement Learning of POMDPs using Spectral Methods

We propose a new reinforcement learning algorithm for partially observab...

Kamyar Azizzadenesheli, et al. ∙

Kamyar Azizzadenesheli

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