
Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
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Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision p...
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Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learningbased methods for simu...
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MeshfreeFlowNet: A PhysicsConstrained Deep Continuous SpaceTime SuperResolution Framework
We propose MeshfreeFlowNet, a novel deep learningbased superresolution...
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Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of adaptive control in partially observable linear ...
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EikoNet: Solving the Eikonal equation with Deep Neural Networks
The recent deep learning revolution has created an enormous opportunity ...
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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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Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mapp...
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Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
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Directivity Modes of Earthquake Populations with Unsupervised Learning
We present a novel approach for resolving modes of rupture directivity i...
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Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensoryrich environments by learning t...
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Regularized Learning for Domain Adaptation under Label Shifts
We propose Regularized Learning under Label shifts (RLLS), a principled ...
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Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
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Neural Lander: Stable Drone Landing Control using Learned Dynamics
Precise trajectory control near ground is difficult for multirotor dron...
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Trust Region Policy Optimization of POMDPs
We propose Generalized Trust Region Policy Optimization (GTRPO), a Reinf...
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signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant
Training neural networks on large datasets can be accelerated by distrib...
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SampleEfficient Deep RL with Generative Adversarial Tree Search
We propose Generative Adversarial Tree Search (GATS), a sampleefficient...
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Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples. Care...
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signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
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Efficient Exploration through Bayesian Deep QNetworks
We propose Bayesian Deep QNetwork (BDQN), a practical Thompson sampling...
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Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
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Reinforcement Learning in RichObservation MDPs using Spectral Methods
Designing effective explorationexploitation algorithms in Markov decisi...
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Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
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Kamyar Azizzadenesheli
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