Kamyar Azizzadenesheli

research

∙ 05/29/2023

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

We present a scalable and effective exploration strategy based on Thomps...

3 Haque Ishfaq, et al. ∙

research

∙ 02/14/2023

Score-based Diffusion Models in Function Space

Diffusion models have recently emerged as a powerful framework for gener...

0 Jae Hyun Lim, et al. ∙

research

∙ 11/25/2022

PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators

We address the problem of generating 3D human motions in dyadic activiti...

0 Md Ashiqur Rahman, et al. ∙

research

∙ 11/24/2022

Fast Sampling of Diffusion Models via Operator Learning

Diffusion models have found widespread adoption in various areas. Howeve...

17 Hongkai Zheng, et al. ∙

research

∙ 10/31/2022

Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators

Carbon capture and storage (CCS) is an important strategy for reducing c...

17 Gege Wen, et al. ∙

research

∙ 09/21/2022

Off-Policy Risk Assessment in Markov Decision Processes

Addressing such diverse ends as safety alignment with human preferences,...

0 Audrey Huang, et al. ∙

research

∙ 07/12/2022

Compactly Restrictable Metric Policy Optimization Problems

We study policy optimization problems for deterministic Markov decision ...

13 Victor D. Dorobantu, et al. ∙

research

∙ 06/27/2022

Supervised Learning with General Risk Functionals

Standard uniform convergence results bound the generalization gap of the...

0 Liu Leqi, et al. ∙

research

∙ 06/22/2022

Langevin Monte Carlo for Contextual Bandits

We study the efficiency of Thompson sampling for contextual bandits. Exi...

23 Pan Xu, et al. ∙

research

∙ 06/17/2022

Thompson Sampling Achieves Õ(√(T)) Regret in Linear Quadratic Control

Thompson Sampling (TS) is an efficient method for decision-making under ...

31 Taylan Kargin, et al. ∙

research

∙ 06/03/2022

KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems

Learning a dynamical system requires stabilizing the unknown dynamics to...

17 Sahin Lale, et al. ∙

research

∙ 05/28/2022

Functional Linear Regression of CDFs

The estimation of cumulative distribution functions (CDF) is an importan...

3 Qian Zhang, et al. ∙

research

∙ 05/27/2022

Competitive Gradient Optimization

We study the problem of convergence to a stationary point in zero-sum ga...

0 Abhijeet Vyas, et al. ∙

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 ...

12 Michael O'Connell, et al. ∙

research

∙ 05/06/2022

Generative Adversarial Neural Operators

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

21 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...

0 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...

64 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 ...

172 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...

43 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...

7 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...

17 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...

0 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 ...

16 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...

14 Guanya Shi, et al. ∙

research

∙ 04/18/2021

Off-Policy Risk Assessment in Contextual Bandits

To evaluate prospective contextual bandit policies when experimentation ...

0 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...

0 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 ...

0 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 ...

7 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...

0 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...

170 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...

27 Sahin Lale, et al. ∙

research

∙ 06/28/2020

Deep Bayesian Quadrature Policy Optimization

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

6 Akella Ravi Tej, et al. ∙

research

∙ 06/18/2020

Competitive Policy Optimization

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

16 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...

5 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...

3 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 ...

4 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 ...

31 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 ...

4 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...

22 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...

13 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...

0 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...

68 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 ...

0 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...

18 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...

0 Guanya Shi, et al. ∙

research

∙ 10/18/2018

Trust Region Policy Optimization of POMDPs

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

0 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...

0 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...

4 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...

0 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...

0 Jeremy Bernstein, et al. ∙

Kamyar Azizzadenesheli

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