13 citations · 29 across the 13 of their papers we have counts for
16 papers
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Zhefei Gong, Hefeifei Jiang, Zehao Wang +1
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and i…
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
Yousef Liang, Ryan Ardalan, Yarden Goraly +6
Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless navigation approaches predom…
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…
Dynamic Tolling in Arc-based Traffic Assignment Models
Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su +1
Tolling in traffic networks offers a popular measure to minimize overall congestion. Existing toll designs primarily focus on congestion in route-based traffic assignment models (T…
Markov -Potential Games
Xin Guo, Xinyu Li, Chinmay Maheshwari +2
We propose a new framework of Markov -potential games to study Markov games. We show that any Markov game with finite-state and finite-action is a Markov -potential game, and…
Arc-based Traffic Assignment: Equilibrium Characterization and Learning
Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su +1
Arc-based traffic assignment models (TAMs) are a popular framework for modeling traffic network congestion generated by self-interested travelers who sequentially select arcs based…
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…
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry
We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about th…
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…
Inducing Social Optimality in Games via Adaptive Incentive Design
Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu +1
How can a social planner adaptively incentivize selfish agents who are learning in a strategic environment to induce a socially optimal outcome in the long run? We propose a two-ti…
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…
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar +2
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. We propose a random reshuffling-based gr…
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…
On the Lagrangian Biduality of Sparsity Minimization Problems
Dheeraj Singaraju, Ehsan Elhamifar, Roberto Tron +2
Recent results in Compressive Sensing have shown that, under certain conditions, the solution to an underdetermined system of linear equations with sparsity-based regularization ca…