You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 6 Nov 2021 • Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar

The Physics-Informed Neural Network (PINN) is an example of the former while the Fourier neural operator (FNO) is an example of the latter.

no code implementations • 3 Sep 2021 • Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson

Data-driven machine learning methods provide faster alternatives to traditional simulators by training neural network models with numerical simulation data mappings.

no code implementations • 26 Aug 2021 • Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

Using these guarantees, we design adaptive control algorithms for unknown ARX systems with arbitrary strongly convex or convex quadratic regulating costs.

no code implementations • 19 Aug 2021 • Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

We propose a generalization of neural networks tailored to learn operators mapping between infinite dimensional function spaces.

no code implementations • 11 Aug 2021 • Yan Yang, Angela F. Gao, Jorge C. Castellanos, Zachary E. Ross, Kamyar Azizzadenesheli, Robert W. Clayton

We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations.

no code implementations • 13 Jun 2021 • Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

Experiments show neural operators are more accurate and stable compared to previous methods on chaotic systems such as the Kuramoto-Sivashinsky and Navier-Stokes equations.

1 code implementation • NeurIPS 2021 • Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue

We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.

no code implementations • 31 May 2021 • Jafar Abbaszadeh Chekan, Kamyar Azizzadenesheli, Cedric Langbort

We propose an optimism-based algorithm that utilizes the actuator redundancy and the possibility of switching between actuating modes to guarantee the boundedness of the state.

no code implementations • NeurIPS 2021 • Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli

Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data.

no code implementations • 4 Mar 2021 • Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli

Because optimizing the coherent risk is difficult in Markov decision processes, recent work tends to focus on the Markov coherent risk (MCR), a time-consistent surrogate.

no code implementations • 10 Feb 2021 • Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli

With our algorithm, LCC-UCB, each agent enjoys a regret of $\tilde{O}\left(\sqrt{({K/N}+ N)T}\right)$, communicates for $O(\log T)$ steps and broadcasts $O(\log K)$ bits in each communication step.

1 code implementation • 9 Jan 2021 • Jonathan D. Smith, Zachary E. Ross, Kamyar Azizzadenesheli, Jack B. Muir

We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference.

1 code implementation • 29 Nov 2020 • Kamyar Azizzadenesheli

We deploy these estimators and provide generalization bounds in the unlabeled target domain.

1 code implementation • ICLR 2021 • Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.

no code implementations • 23 Jul 2020 • Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

In the absence of such a stabilizing controller, at the early stages, the lack of reasonable model estimates needed for (i) strategic exploration and (ii) design of controllers that stabilize the system, results in regret that scales exponentially in the problem dimensions.

1 code implementation • 28 Jun 2020 • Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue

On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity.

2 code implementations • 18 Jun 2020 • Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar

A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties.

2 code implementations • NeurIPS 2020 • Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

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 in the desired structure for neural networks.

1 code implementation • 1 May 2020 • Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs.

1 code implementation • 25 Mar 2020 • Jonathan D. Smith, Kamyar Azizzadenesheli, Zachary E. Ross

Here, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3D velocity structures.

no code implementations • NeurIPS 2020 • Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

We study the problem of system identification and adaptive control in partially observable linear dynamical systems.

no code implementations • 12 Mar 2020 • Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori.

3 code implementations • ICLR Workshop DeepDiffEq 2019 • Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces.

no code implementations • 31 Jan 2020 • Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control.

no code implementations • 30 Jun 2019 • Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar

A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially-compact cluster.

no code implementations • 25 Jun 2019 • Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).

2 code implementations • ICLR 2019 • Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar

We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class.

no code implementations • 28 Jan 2019 • Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi

We modify the image classification task into the SLB setting and empirically show that, when a pre-trained DNN provides the high dimensional feature representations, deploying PSLB results in significant reduction of regret and faster convergence to an accurate model compared to state-of-art algorithm.

no code implementations • 19 Nov 2018 • Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung

To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.

no code implementations • 18 Oct 2018 • Kamyar Azizzadenesheli, Yisong Yue, Animashree Anandkumar

Deploying these tools, we generalize a variety of existing theoretical guarantees, such as policy gradient and convergence theorems, to partially observable domains, those which also could be carried to more settings of interest.

2 code implementations • ICLR 2019 • Jeremy Bernstein, Jia-Wei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar

Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote.

3 code implementations • ICLR 2019 • Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C. Lipton, Animashree Anandkumar

We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning.

no code implementations • ICLR 2018 • Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar

Neural networks are known to be vulnerable to adversarial examples.

2 code implementations • ICML 2018 • Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar

Using a theorem by Gauss we prove that majority vote can achieve the same reduction in variance as full precision distributed SGD.

1 code implementation • ICLR 2018 • Kamyar Azizzadenesheli, Animashree Anandkumar

This allows us to directly incorporate the uncertainty over the Q-function and deploy Thompson sampling on the learned posterior distribution resulting in efficient exploration/exploitation trade-off.

no code implementations • ICLR 2018 • Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, Li Deng

Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never.

no code implementations • ICLR 2018 • Jeremy Bernstein, Kamyar Azizzadenesheli, Yu-Xiang Wang, Anima Anandkumar

The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates.

no code implementations • 7 May 2017 • Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.

no code implementations • 11 Nov 2016 • Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

We derive finite-time regret bounds for our algorithm with a weak dependence on the dimensionality of the observed space.

no code implementations • 3 Nov 2016 • Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, Li Deng

We introduce intrinsic fear (IF), a learned reward shaping that guards DRL agents against periodic catastrophes.

no code implementations • 17 Aug 2016 • Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

Generally in RL, one can assume a generative model, e. g. graphical models, for the environment, and then the task for the RL agent is to learn the model parameters and find the optimal strategy based on these learnt parameters.

no code implementations • 25 Feb 2016 • Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.