
OffPolicy Risk Assessment in Contextual Bandits
To evaluate prospective contextual bandit policies when experimentation ...
read it

On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk
In order to model risk aversion in reinforcement learning, an emerging l...
read it

MultiAgent MultiArmed Bandits with Limited Communication
We consider the problem where N agents collaboratively interact with an ...
read it

HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
We introduce a scheme for probabilistic hypocenter inversion with Stein ...
read it

Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift
We study generalization under label shift in domain adaptation where the...
read it

Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on le...
read it

Explore More and Improve Regret in Linear Quadratic Regulators
Stabilizing the unknown dynamics of a control system and minimizing regr...
read it

Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
read it

Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision p...
read it

Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learningbased methods for simu...
read it

MeshfreeFlowNet: A PhysicsConstrained Deep Continuous SpaceTime SuperResolution Framework
We propose MeshfreeFlowNet, a novel deep learningbased superresolution...
read it

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of adaptive control in partially observable linear ...
read it

EikoNet: Solving the Eikonal equation with Deep Neural Networks
The recent deep learning revolution has created an enormous opportunity ...
read it

Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems
We study the problem of adaptive control in partially observable linear ...
read it

Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mapp...
read it

Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
read it

Directivity Modes of Earthquake Populations with Unsupervised Learning
We present a novel approach for resolving modes of rupture directivity i...
read it

Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensoryrich environments by learning t...
read it

Regularized Learning for Domain Adaptation under Label Shifts
We propose Regularized Learning under Label shifts (RLLS), a principled ...
read it

Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
read it

Neural Lander: Stable Drone Landing Control using Learned Dynamics
Precise trajectory control near ground is difficult for multirotor dron...
read it

Trust Region Policy Optimization of POMDPs
We propose Generalized Trust Region Policy Optimization (GTRPO), a Reinf...
read it

signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant
Training neural networks on large datasets can be accelerated by distrib...
read it

SampleEfficient Deep RL with Generative Adversarial Tree Search
We propose Generative Adversarial Tree Search (GATS), a sampleefficient...
read it

Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples. Care...
read it

signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
read it

Efficient Exploration through Bayesian Deep QNetworks
We propose Bayesian Deep QNetwork (BDQN), a practical Thompson sampling...
read it

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
read it

Reinforcement Learning in RichObservation MDPs using Spectral Methods
Designing effective explorationexploitation algorithms in Markov decisi...
read it

Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
read it
Kamyar Azizzadenesheli
verfied profile