Navid Azizan

Orcid: 0000-0002-4299-2963

Affiliations:
  • Massachusetts Institute of Technology (MIT), MA, USA
  • California Institute of Technology, CA, USA (former)


According to our database1, Navid Azizan authored at least 40 papers between 2015 and 2024.

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Bibliography

2024
Learning Chaotic Dynamics with Embedded Dissipativity.
CoRR, 2024

Meta-Learning for Adaptive Control with Automated Mirror Descent.
CoRR, 2024

Adapting Differentially Private Synthetic Data to Relational Databases.
CoRR, 2024

Π-ORFit: One-Pass Learning with Bregman Projection.
Proceedings of the American Control Conference, 2024

2023
Control-oriented meta-learning.
Int. J. Robotics Res., September, 2023

A Unified Approach to Controlling Implicit Regularization via Mirror Descent.
J. Mach. Learn. Res., 2023

Private Synthetic Data Meets Ensemble Learning.
CoRR, 2023

Representation Reliability and Its Impact on Downstream Tasks.
CoRR, 2023

SketchOGD: Memory-Efficient Continual Learning.
CoRR, 2023

Automatic Gradient Descent: Deep Learning without Hyperparameters.
CoRR, 2023

Learning Control-Oriented Dynamical Structure from Data.
Proceedings of the International Conference on Machine Learning, 2023

On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Data-Driven Control with Inherent Lyapunov Stability.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Online Learning for Equilibrium Pricing in Markets under Incomplete Information.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Online Learning for Traffic Routing under Unknown Preferences.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Stochastic Mirror Descent on Overparameterized Nonlinear Models.
IEEE Trans. Neural Networks Learn. Syst., 2022

Uncertainty-Aware Meta-Learning for Multimodal Task Distributions.
CoRR, 2022

Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance.
CoRR, 2022

Explicit Regularization via Regularizer Mirror Descent.
CoRR, 2022

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

A Unified View of SDP-based Neural Network Verification through Completely Positive Programming.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Sketching curvature for efficient out-of-distribution detection for deep neural networks.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems.
Proceedings of the Robotics: Science and Systems XVII, Virtual Event, July 12-16, 2021., 2021

2020
Optimal Pricing in Markets with Nonconvex Costs.
Oper. Res., 2020

A Study of Generalization of Stochastic Mirror Descent Algorithms on Overparameterized Nonlinear Models.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Orthogonal Gradient Descent for Continual Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Distributed Solution of Large-Scale Linear Systems via Accelerated Projection-Based Consensus.
IEEE Trans. Signal Process., 2019

Optimization Algorithms for Large-Scale Systems: From Deep Learning to Energy Markets.
SIGMETRICS Perform. Evaluation Rev., 2019

Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization.
CoRR, 2019

Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization.
Proceedings of the 7th International Conference on Learning Representations, 2019

A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties.
Proceedings of the IEEE International Conference on Acoustics, 2019

Optimal Pricing in Markets with Non-Convex Costs.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018
Improving Distributed Gradient Descent Using Reed-Solomon Codes.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2016
Opportunities for Price Manipulation by Aggregators in Electricity Markets.
SIGMETRICS Perform. Evaluation Rev., 2016

Analysis of Exact and Approximated Epidemic Models over Complex Networks.
CoRR, 2016

Improved bounds on the epidemic threshold of exact SIS models on complex networks.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015
Multiscale modeling of biological communication.
Proceedings of the 2015 IEEE International Conference on Communications, 2015

SIRS epidemics on complex networks: Concurrence of exact Markov chain and approximated models.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015


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