128 citations · 366 across the 32 of their papers we have counts for
6 papers · 1 filter
LagNetViP: A Lagrangian Neural Network for Video Prediction
Christine Allen-Blanchette, Sushant Veer, Anirudha Majumdar +1
The dominant paradigms for video prediction rely on opaque transition models where neither the equations of motion nor the underlying physical quantities of the system are easily i…
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Yaofeng Desmond Zhong, Naomi Ehrich Leonard
Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, whe…
Distributed Learning: Sequential Decision Making in Resource-Constrained Environments
Udari Madhushani, Naomi Ehrich Leonard
We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed l…
Asymptotic Allocation Rules for a Class of Dynamic Multi-armed Bandit Problems
T. W. U. Madhushani, D. H. S. Maithripala, N. E. Leonard
This paper presents a class of Dynamic Multi-Armed Bandit problems where the reward can be modeled as the noisy output of a time varying linear stochastic dynamic system that satis…
Satisficing in multi-armed bandit problems
Paul Reverdy, Vaibhav Srivastava, Naomi Ehrich Leonard
Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty. We propose two sets of satisficing objectives for the multi-armed ba…
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits
Paul Reverdy, Vaibhav Srivastava, Naomi E. Leonard
We present a formal model of human decision-making in explore-exploit tasks using the context of multi-armed bandit problems, where the decision-maker must choose among multiple op…