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09/07/2023
Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation
We present a data-driven model for ground-motion synthesis using a Gener...
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09/01/2023
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
We propose the geometry-informed neural operator (GINO), a highly effici...
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08/17/2023
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces
Tipping points are abrupt, drastic, and often irreversible changes in th...
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07/27/2023
Speeding up Fourier Neural Operators via Mixed Precision
The Fourier neural operator (FNO) is a powerful technique for learning s...
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07/12/2023
Reward Selection with Noisy Observations
We study a fundamental problem in optimization under uncertainty. There ...
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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...
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02/14/2023
Score-based Diffusion Models in Function Space
Diffusion models have recently emerged as a powerful framework for gener...
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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...
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11/24/2022
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas. Howeve...
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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...
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09/21/2022
Off-Policy Risk Assessment in Markov Decision Processes
Addressing such diverse ends as safety alignment with human preferences,...
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07/12/2022
Compactly Restrictable Metric Policy Optimization Problems
We study policy optimization problems for deterministic Markov decision ...
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06/27/2022
Supervised Learning with General Risk Functionals
Standard uniform convergence results bound the generalization gap of the...
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06/22/2022
Langevin Monte Carlo for Contextual Bandits
We study the efficiency of Thompson sampling for contextual bandits. Exi...
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06/17/2022
Thompson Sampling Achieves Õ(√(T)) Regret in Linear Quadratic Control
Thompson Sampling (TS) is an efficient method for decision-making under ...
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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...
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05/28/2022
Functional Linear Regression of CDFs
The estimation of cumulative distribution functions (CDF) is an importan...
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05/27/2022
Competitive Gradient Optimization
We study the problem of convergence to a stationary point in zero-sum ga...
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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 ...
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05/06/2022
Generative Adversarial Neural Operators
We propose the generative adversarial neural operator (GANO), a generati...
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04/23/2022
U-NO: U-shaped Neural Operators
Neural operators generalize classical neural networks to maps between in...
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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...
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11/06/2021
Physics-Informed Neural Operator for Learning Partial Differential Equations
Machine learning methods have recently shown promise in solving partial ...
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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...
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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...
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08/19/2021
Neural Operator: Learning Maps Between Function Spaces
The classical development of neural networks has primarily focused on le...
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08/11/2021
Seismic wave propagation and inversion with Neural Operators
Seismic wave propagation forms the basis for most aspects of seismologic...
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06/13/2021
Markov Neural Operators for Learning Chaotic Systems
Chaotic systems are notoriously challenging to predict because of their ...
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06/11/2021
Meta-Adaptive Nonlinear Control: Theory and Algorithms
We present an online multi-task learning approach for adaptive nonlinear...
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04/18/2021
Off-Policy Risk Assessment in Contextual Bandits
To evaluate prospective contextual bandit policies when experimentation ...
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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...
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02/10/2021
Multi-Agent Multi-Armed Bandits with Limited Communication
We consider the problem where N agents collaboratively interact with an ...
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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 ...
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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...
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10/18/2020
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on le...
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07/23/2020
Explore More and Improve Regret in Linear Quadratic Regulators
Stabilizing the unknown dynamics of a control system and minimizing regr...
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06/28/2020
Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
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06/18/2020
Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision p...
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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...
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05/01/2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...
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03/25/2020
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of adaptive control in partially observable linear ...
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03/25/2020
EikoNet: Solving the Eikonal equation with Deep Neural Networks
The recent deep learning revolution has created an enormous opportunity ...
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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 ...
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03/07/2020
Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mapp...
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01/31/2020
Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
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06/30/2019
Directivity Modes of Earthquake Populations with Unsupervised Learning
We present a novel approach for resolving modes of rupture directivity i...
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06/25/2019
Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensory-rich environments by learning t...
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03/22/2019
Regularized Learning for Domain Adaptation under Label Shifts
We propose Regularized Learning under Label shifts (RLLS), a principled ...
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01/28/2019
Stochastic Linear Bandits with Hidden Low Rank Structure
High-dimensional representations often have a lower dimensional underlyi...
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11/19/2018