SIRS epidemics on complex networks: Concurrence of exact Markov chain and approximated models. - Navid Azizan Ruhi; Babak Hassibi (CDC, 2015)

Multiscale modeling of biological communication. - Navid Azizan Ruhi; Paul Bogdan (ICC, 2015)

Improved bounds on the epidemic threshold of exact SIS models on complex networks. - Navid Azizan Ruhi; Christos Thrampoulidis; Babak Hassibi (CDC, 2016)

Analysis of Exact and Approximated Epidemic Models over Complex Networks. - Navid Azizan Ruhi; Hyoung Jun Ahn; Babak Hassibi (CoRR, 2016)

Opportunities for Price Manipulation by Aggregators in Electricity Markets. - Navid Azizan Ruhi; Niangjun Chen; Krishnamurthy Dvijotham; Adam Wierman (SIGMETRICS Perform. Evaluation Rev., 2016)

Distributed Solution of Large-Scale Linear Systems Via Accelerated Projection-Based Consensus. - Navid Azizan Ruhi; Farshad Lahouti; Salman Avestimehr; Babak Hassibi (ICASSP, 2018)

Improving Distributed Gradient Descent Using Reed-Solomon Codes. - Wael Halbawi; Navid Azizan Ruhi; Fariborz Salehi; Babak Hassibi (ISIT, 2018)

A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality. - Navid Azizan; Babak Hassibi (CDC, 2019)

Optimal Pricing in Markets with Non-Convex Costs. - Navid Azizan Ruhi; Yu Su 013; Krishnamurthy Dvijotham; Adam Wierman (EC, 2019)

A Characterization of Stochastic Mirror Descent Algorithms and Their Convergence Properties. - Navid Azizan Ruhi; Babak Hassibi (ICASSP, 2019)

Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization. - Navid Azizan Ruhi; Babak Hassibi (ICLR (Poster), 2019)

Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization. - Navid Azizan Ruhi; Sahin Lale; Babak Hassibi (CoRR, 2019)

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

Orthogonal Gradient Descent for Continual Learning. - Mehrdad Farajtabar; Navid Azizan; Alex Mott; Ang Li (AISTATS, 2020)

A Study of Generalization of Stochastic Mirror Descent Algorithms on Overparameterized Nonlinear Models. - Navid Azizan; Sahin Lale; Babak Hassibi (ICASSP, 2020)

Optimal Pricing in Markets with Nonconvex Costs. - Navid Azizan; Yu Su 013; Krishnamurthy Dvijotham; Adam Wierman (Oper. Res., 2020)

Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems. - Spencer M. Richards; Navid Azizan; Jean-Jacques E. Slotine; Marco Pavone 001 (Robotics: Science and Systems, 2021)

Sketching curvature for efficient out-of-distribution detection for deep neural networks. - Apoorva Sharma; Navid Azizan; Marco Pavone 001 (UAI, 2021)

A Unified View of SDP-based Neural Network Verification through Completely Positive Programming. - Robin A. Brown; Edward Schmerling; Navid Azizan; Marco Pavone 001 (AISTATS, 2022)

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. - Youngjae Min; Kwangjun Ahn; Navid Azizan (CDC, 2022)

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently. - Haoyuan Sun; Kwangjun Ahn; Christos Thrampoulidis; Navid Azizan (NeurIPS, 2022)

Explicit Regularization via Regularizer Mirror Descent. - Navid Azizan; Sahin Lale; Babak Hassibi (CoRR, 2022)

Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance. - Shervin Ardeshir; Navid Azizan (CoRR, 2022)

Uncertainty-Aware Meta-Learning for Multimodal Task Distributions. - Cesar Almecija; Apoorva Sharma; Navid Azizan (CoRR, 2022)

Stochastic Mirror Descent on Overparameterized Nonlinear Models. - Navid Azizan; Sahin Lale; Babak Hassibi (IEEE Trans. Neural Networks Learn. Syst., 2022)

Online Learning for Traffic Routing under Unknown Preferences. - Devansh Jalota; Karthik Gopalakrishnan 002; Navid Azizan; Ramesh Johari; Marco Pavone 001 (AISTATS, 2023)

Online Learning for Equilibrium Pricing in Markets under Incomplete Information. - Devansh Jalota; Haoyuan Sun; Navid Azizan (CDC, 2023)

Data-Driven Control with Inherent Lyapunov Stability. - Youngjae Min; Spencer M. Richards; Navid Azizan (CDC, 2023)

On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers. - Boris Velasevic; Rohit Parasnis; Christopher G. Brinton; Navid Azizan (CDC, 2023)

Learning Control-Oriented Dynamical Structure from Data. - Spencer M. Richards; Jean-Jacques E. Slotine; Navid Azizan; Marco Pavone 001 (ICML, 2023)

Automatic Gradient Descent: Deep Learning without Hyperparameters. - Jeremy Bernstein; Chris Mingard; Kevin Huang; Navid Azizan; Yisong Yue (CoRR, 2023)

SketchOGD: Memory-Efficient Continual Learning. - Benjamin Wright; Youngjae Min; Jeremy Bernstein; Navid Azizan (CoRR, 2023)

Representation Reliability and Its Impact on Downstream Tasks. - Young-Jin Park; Hao Wang; Shervin Ardeshir; Navid Azizan (CoRR, 2023)

Private Synthetic Data Meets Ensemble Learning. - Haoyuan Sun; Navid Azizan; Akash Srivastava; Hao Wang (CoRR, 2023)

A Unified Approach to Controlling Implicit Regularization via Mirror Descent. - Haoyuan Sun; Khashayar Gatmiry; Kwangjun Ahn; Navid Azizan (J. Mach. Learn. Res., 2023)

Control-oriented meta-learning. - Spencer M. Richards; Navid Azizan; Jean-Jacques E. Slotine; Marco Pavone 001 (Int. J. Robotics Res., 2023)

Π-ORFit: One-Pass Learning with Bregman Projection. - Namhoon Cho; Youngjae Min; Hyo-Sang Shin; Navid Azizan (ACC, 2024)

Quantifying Representation Reliability in Self-Supervised Learning Models. - Young-Jin Park; Hao Wang; Shervin Ardeshir; Navid Azizan (UAI, 2024)

Adapting Differentially Private Synthetic Data to Relational Databases. - Kaveh Alimohammadi; Hao Wang; Ojas Gulati; Akash Srivastava; Navid Azizan (CoRR, 2024)

Learning Chaotic Dynamics with Embedded Dissipativity. - Sunbochen Tang; Themistoklis Sapsis; Navid Azizan (CoRR, 2024)

Hard-Constrained Neural Networks with Universal Approximation Guarantees. - Youngjae Min; Anoopkumar Sonar; Navid Azizan (CoRR, 2024)

Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection. - Aaron Alvarado Kristanto Julistiono; Davoud Ataee Tarzanagh; Navid Azizan (CoRR, 2024)

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium. - Zeyang Li; Navid Azizan (CoRR, 2024)

Identifying Reliable Predictions in Detection Transformers. - Young-Jin Park; Carson Sobolewski; Navid Azizan (CoRR, 2024)

Meta-Learning for Adaptive Control with Automated Mirror Descent. - Sunbochen Tang; Haoyuan Sun; Navid Azizan (L4DC, 2025)

In-Context Learning of Polynomial Kernel Regression in Transformers with GLU Layers. - Haoyuan Sun; Ali Jadbabaie; Navid Azizan (CoRR, 2025)

Activation-Informed Merging of Large Language Models. - Amin Heyrani Nobari; Kaveh Alimohammadi; Ali ArjomandBigdeli; Akash Srivastava; Faez Ahmed; Navid Azizan (CoRR, 2025)

Constrained Online Decision-Making: A Unified Framework. - Haichen Hu; David Simchi-Levi; Navid Azizan (CoRR, 2025)

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models. - Young Jin Park; François G. Germain; Jing Liu 009; Ye Wang 001; Toshiaki Koike-Akino; Gordon Wichern; Navid Azizan; Christopher R. Laughman; Ankush Chakrabarty (CoRR, 2025)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models. - Young-Jin Park; Kristjan H. Greenewald; Kaveh Alim; Hao Wang; Navid Azizan (CoRR, 2025)

Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning. - Ji Young Byun; Young-Jin Park; Navid Azizan; Rama Chellappa (CoRR, 2025)

Hierarchical Vision-Language Planning for Multi-Step Humanoid Manipulation. - André Schakkal; Ben Zandonati; Zhutian Yang; Navid Azizan (CoRR, 2025)

Personalized Collaborative Learning with Affinity-Based Variance Reduction. - Chenyu Zhang; Navid Azizan (CoRR, 2025)