39 citations · 175 across the 48 of their papers we have counts for
8 papers · 1 filter
Tractable Uncertainty-Aware Meta-Learning
Young-Jin Park, Cesar Almecija, Apoorva Sharma +1
Meta-learning is a popular approach for learning new tasks with limited data by leveraging the commonalities among different tasks. However, meta-learned models can perform poorly…
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…
ORFit: One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares
Youngjae Min, Namhoon Cho, Navid Azizan
While large machine learning models have shown remarkable performance in various domains, their training typically requires iterating for many passes over the training data. Howeve…
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently
Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis +1
Driven by the empirical success and wide use of deep neural networks, understanding the generalization performance of overparameterized models has become an increasingly popular qu…
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…
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
Robin Brown, Edward Schmerling, Navid Azizan +1
Verifying that input-output relationships of a neural network conform to prescribed operational specifications is a key enabler towards deploying these networks in safety-critical…
Explicit Regularization via Regularizer Mirror Descent
Navid Azizan, Sahin Lale, Babak Hassibi
Despite perfectly interpolating the training data, deep neural networks (DNNs) can often generalize fairly well, in part due to the "implicit regularization" induced by the learnin…