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Found 21 papers, 2 papers with code
Privacy Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility
To ensure the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of "air-credits" and anonymously charges prices for traversing the edges of the time-extended graph.
Parameter Estimation in Optimal Tolling for Traffic Networks Under the Markovian Traffic Equilibrium
Tolling, or congestion pricing, has emerged as an effective tool for preventing gridlock in traffic systems.
Convergence of Decentralized Actor-Critic Algorithm in General-sum Markov Games
Markov games provide a powerful framework for modeling strategic multi-agent interactions in dynamic environments.
Incentive-Compatible Vertiport Reservation in Advanced Air Mobility: An Auction-Based Approach
The rise of advanced air mobility (AAM) is expected to become a multibillion-dollar industry in the near future.
Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security
Adversarial attacks on learning-based multi-modal trajectory predictors have already been demonstrated.
Dynamic Tolling in Arc-based Traffic Assignment Models
We prove that our adaptive learning and marginal pricing dynamics converge to a neighborhood of the socially optimal loads and tolls.
Markov $α$-Potential Games
We study two important classes of practically significant Markov games, Markov congestion games and the perturbed Markov team games, via the framework of Markov $\alpha$-potential games, with explicit characterization of an upper bound for $\mnpg$ and its relation to game parameters.
Representation Learning via Manifold Flattening and Reconstruction
This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold.
Arc-based Traffic Assignment: Equilibrium Characterization and Learning
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 on their perceived latency on the network.
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory.
Competing Bandits in Time Varying Matching Markets
We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match.
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
We study the problem of online learning in competitive settings in the context of two-sided matching markets.
Independent and Decentralized Learning in Markov Potential Games
We study a multi-agent reinforcement learning dynamics, and analyze its convergence in infinite-horizon discounted Markov potential games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization
This work presents an optimization-based framework that unifies these approaches, and allows users to flexibly implement different design choices, e. g., the number and types of variables maintained in the algorithm at each time.
Dynamic Tolling for Inducing Socially Optimal Traffic Loads
How to design tolls that induce socially optimal traffic loads with dynamically arriving travelers who make selfish routing decisions?
Residual Networks: Lyapunov Stability and Convex Decomposition
While training error of most deep neural networks degrades as the depth of the network increases, residual networks appear to be an exception.
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards.
Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained $\ell_1$-Minimization
High-dimensional data often lie in low-dimensional subspaces corresponding to different classes they belong to.
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem
This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal.
Markov Chain Monte Carlo Data Association for Multiple-Target Tracking
This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multiple-target tracking in a cluttered environment.
Geometric Models of Rolling-Shutter Cameras
We develop a general projection equation for a rolling shutter camera and show how it is affected by different types of camera motion.
