6 papers
Discovering mathematical concepts through a multi-agent system
Daattavya Aggarwal, Oisin Kim, Carl Henrik Ek +1
Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model…
Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm
Carl Henrik Ek, Oisin Kim, Challenger Mishra
Motivated by recent progress in the problem of numerical Kähler metrics, we survey machine learning techniques in this area, discussing both advantages and drawbacks. We then revi…
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti, Sattar Vakili, Amanda Prorok +1
Thompson sampling (TS) is a powerful and widely used strategy for sequential decision-making, with applications ranging from Bayesian optimization to reinforcement learning (RL). D…
Bayesian Nonparametric Dynamical Clustering of Time Series
Adrián Pérez-Herrero, Paulo Félix, Jesús Presedo +1
We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Baye…
Learning from Preferences and Mixed Demonstrations in General Settings
Jason R Brown, Carl Henrik Ek, Robert D Mullins
Reinforcement learning is a general method for learning in sequential settings, but it can often be difficult to specify a good reward function when the task is complex. In these c…
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state…