39 citations · 90 across the 11 of their papers we have counts for
15 papers
Private Synthetic Data Meets Ensemble Learning
Haoyuan Sun, Navid Azizan, Akash Srivastava +1
When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and rea…
Automatic Gradient Descent: Deep Learning without Hyperparameters
Jeremy Bernstein, Chris Mingard, Kevin Huang +2
The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation fr…
Data-Driven Control with Inherent Lyapunov Stability
Youngjae Min, Spencer M. Richards, Navid Azizan
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time. However,…
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance
Shervin Ardeshir, Navid Azizan
The superior performance of some of today's state-of-the-art deep learning models is to some extent owed to extensive (self-)supervised contrastive pretraining on large-scale datas…
Control-oriented meta-learning
Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine +1
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory…
Online Learning for Traffic Routing under Unknown Preferences
Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan +2
In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results…