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09/15/2023
A Bayesian Approach to Robust Inverse Reinforcement Learning
We consider a Bayesian approach to offline model-based inverse reinforce...
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03/27/2023
An active inference model of car following: Advantages and applications
Driver process models play a central role in the testing, verification, ...
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02/15/2023
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the...
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10/04/2022
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function...
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10/04/2022
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
We consider the task of estimating a structural model of dynamic decisio...
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09/07/2022
On Plane Subgraphs of Complete Topological Drawings
Topological drawings are representations of graphs in the plane, where v...
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09/02/2022
Shooting Stars in Simple Drawings of K_m,n
Simple drawings are drawings of graphs in which two edges have at most o...
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08/11/2022
Empty Triangles in Generalized Twisted Drawings of K_n
Simple drawings are drawings of graphs in the plane or on the sphere suc...
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03/11/2022
Twisted Ways to Find Plane Structures in Simple Drawings of Complete Graphs
Simple drawings are drawings of graphs in which the edges are Jordan arc...
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02/28/2022
A Market Mechanism for Trading Flexibility Between Interconnected Electricity Markets
Electricity markets differ in their ability to meet power imbalances in ...
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10/11/2021
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees
Multi-agent reinforcement learning (MARL) has attracted much research at...
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02/14/2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
We consider a distributed non-convex optimization where a network of age...
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01/22/2021
On the Local Linear Rate of Consensus on the Stiefel Manifold
We study the convergence properties of Riemannian gradient method for so...
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08/02/2020
Dynamic Discrete Choice Estimation with Partially Observable States and Hidden Dynamics
Dynamic discrete choice models are used to estimate the intertemporal pr...
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04/28/2020
Distributed Projected Subgradient Method for Weakly Convex Optimization
The stochastic subgradient method is a widely-used algorithm for solving...
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10/28/2019
Distributed Estimation via Network Regularization
We propose a new method for distributed estimation of a linear model by ...
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06/11/2018
Swarming for Faster Convergence in Stochastic Optimization
We study a distributed framework for stochastic optimization which is in...
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03/06/2018
Packing Plane Spanning Trees into a Point Set
Let P be a set of n points in the plane in general position. We show tha...
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02/09/2018
Colored ray configurations
We study the cyclic color sequences induced at infinity by colored rays ...
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10/27/2017