13 citations · 29 across the 13 of their papers we have counts for
7 papers · 1 filter
ATLAS-AL: Adaptive Trust-Region for Latent Adversarial Searches via Active Learning
Marsalis Gibson, Claire Tomlin, Shankar Sastry
Security evaluation of learning-based systems requires more than just testing the system against a fixed collection of attacks. It requires adaptive mechanisms that can efficiently…
Competing Bandits in Time Varying Matching Markets
Deepan Muthirayan, Chinmay Maheshwari, Pramod P. Khargonekar +1
We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting w…
Independent and Decentralized Learning in Markov Potential Games
Chinmay Maheshwari, Manxi Wu, Druv Pai +1
We study a multi-agent reinforcement learning dynamics, and analyze its asymptotic behavior in infinite-horizon discounted Markov potential games. We focus on the independent and d…
Who Leads and Who Follows in Strategic Classification?
Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry +1
As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this proble…
Persistency of Excitation for Robustness of Neural Networks
Kamil Nar, S. Shankar Sastry
When an online learning algorithm is used to estimate the unknown parameters of a model, the signals interacting with the parameter estimates should not decay too quickly for the o…
Step Size Matters in Deep Learning
Kamil Nar, S. Shankar Sastry
Training a neural network with the gradient descent algorithm gives rise to a discrete-time nonlinear dynamical system. Consequently, behaviors that are typically observed in these…
Residual Networks: Lyapunov Stability and Convex Decomposition
Kamil Nar, Shankar Sastry
While training error of most deep neural networks degrades as the depth of the network increases, residual networks appear to be an exception. We show that the main reason for this…