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Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift
Kamyar Azizzadenesheli
We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source t…
Fourier Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli +4
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural o…
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi +1
In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems. When learning a dynamical system, one needs to stabilize the unknow…
Competitive Policy Optimization
Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger +2
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties…
Deep Bayesian Quadrature Policy Optimization
Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh +2
We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimati…
Multipole Graph Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli +4
One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data…