39 citations · 175 across the 48 of their papers we have counts for
7 papers · 1 filter
Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
Young-Jin Park, Carson Sobolewski, Navid Azizan
DETR and its variants have emerged as promising architectures for object detection, offering an end-to-end prediction pipeline. In practice, however, DETRs generate hundreds of pre…
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Zeyang Li, Navid Azizan
Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in…
Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection
Addison Kristanto Julistiono, Davoud Ataee Tarzanagh, Navid Azizan
Attention mechanisms have revolutionized several domains of artificial intelligence, such as natural language processing and computer vision, by enabling models to selectively focu…
HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees
Youngjae Min, Navid Azizan
Incorporating prior knowledge or specifications of input-output relationships into machine learning models has attracted significant attention, as it enhances generalization from l…
Learning Dissipative Chaotic Dynamics with Boundedness Guarantees
Sunbochen Tang, Themistoklis Sapsis, Navid Azizan
Chaotic dynamics, commonly seen in weather systems and fluid turbulence, are characterized by their sensitivity to initial conditions, which makes accurate prediction challenging.…
Meta-Learning for Adaptive Control with Automated Mirror Descent
Sunbochen Tang, Haoyuan Sun, Navid Azizan
Adaptive control achieves concurrent parameter learning and stable control under uncertainties that are linearly parameterized with known nonlinear features. Nonetheless, it is oft…
Differentially Private Synthetic Data Generation for Relational Databases
Kaveh Alimohammadi, Hao Wang, Ojas Gulati +2
Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with…