Continuous Upper Confidence Trees with Polynomial Exploration – Consistency

Auger, David; Cou«toux, Adrien; Teytaud, Olivier

doi:10.1007/978-3-642-40988-2_13

Cited by 35 publications

(56 citation statements)

References 12 publications

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“…Given the importance of establishing confidence for an autonomy system in the environmental and earth sciences, the key challenge to overcome is related to addressing tree degeneracy in continuous, partially-observable domains and formulating performance guarantees of the framework. This thesis extends planners for fully-observable continuous-domains [21,22] to partially-observable domains, and demonstrates that theoretical performance guarantees are preserved.…”

Section: Decision-making and Planning In Continuous Domainsmentioning

confidence: 90%

“…However, this assumption can compromise search and has optimality guarantees only in linear-Gaussian systems [192]. Instead, PLUMES uses Monte Carlo Tree Search (MCTS) with progressive widening, referred to as continuous-observation MCTS, to limit the growth of the planning tree [22] and retain optimality [21] in continuous environments.…”

Section: Significance and Role Of Transiencementioning

confidence: 99%

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Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Preston¹

2019

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Thanks first goes to my family and friends for their support and willingness to entertain hours of conversation about robot boats and marine methane. I especially wish to acknowledge my parents for the freedom they provided me throughout my life to explore my interests, ultimately enabling me to become the independent (and quirky) person I am today. I also sincerely thank my partner, Bill, for his enthusiasm about my work, ability to engage in thoughtful conversations about robotics, and his enduring ability to make me laugh when the going gets tough. To my advisers, Anna Michel and Nicholas Roy, I extend sincere gratitude for their mentorship and insight throughout my work. I recognize the fortune I have to work with two incredible professionals in two unique fields, and the opportunities for field work and learning this has afforded. Additionally, working with their labs, WHOI DSL Michel Lab and MIT CSAIL RRG, has been a pleasure. Being surrounded by thoughtful and creative minds everyday is a privilege; and I'm especially thankful to count these individuals among my friends. I particularly thank my research collaborators Dr.Cara Manning, Dr.David Nicholson, Dr.Yogesh Girdhar, Genevieve Flaspohler, and Kevin Manganini for their insight, patience, support, and positive attitudes throughout our work together. They have taught me so much about how to be a good scientist, engineer, and communicator. As I hit this milestone on my way towards earning a PhD, I look forward to what the future has in store. I would like to dedicate this thesis to my parents, Matthew and Elizabeth Preston, to whom I owe my intellectual curiosity, confidence, and perseverance.

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Section: Decision-making and Planning In Continuous Domainsmentioning

confidence: 90%

Section: Significance and Role Of Transiencementioning

confidence: 99%

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Preston¹

2019

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“…Theoretically, PLUMES can be shown to select asymptotically optimal actions. We briefly describe how analysis in Auger et al [17] for PUCT-MCTS with progressive widening in MDPs can be extended to PLUMES. * Refer to Table 1 of Auger et al [17] for parameter settings.…”

Section: B Planning With Continuous-observation Mctsmentioning

confidence: 99%

“…Subsequently, Theorem 1 in Auger et al [17] shows that for an MDP with a continuous state space, like the belief-state MDP representation suggested, the value function estimated by continuous-observation MCTS asymptotically converges to that of the optimal policy:…”

Section: B Planning With Continuous-observation Mctsmentioning

confidence: 99%

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Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

Flaspohler

Preston

Michel

et al. 2019

IEEE Robot. Autom. Lett.

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We present PLUMES, a planner for localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. This "maximum seek-and-sample" (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continuousobservation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.

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Incremental Search Space Construction for Machine Learning Pipeline Synthesis

Zöller

Nguyen

Huber

2021

Advances in Intelligent Data Analysis XIX

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Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. Many studies have investigated efficient methods for algorithm selection and hyperparameter optimization. However, methods for ML pipeline synthesis and optimization considering the impact of complex pipeline structures containing multiple preprocessing and classification algorithms have not been studied thoroughly. In this paper, we propose a data-centric approach based on meta-features for pipeline construction and hyperparameter optimization inspired by human behavior. By expanding the pipeline search space incrementally in combination with meta-features of intermediate data sets, we are able to prune the pipeline structure search space efficiently. Consequently, flexible and data set specific ML pipelines can be constructed. We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.

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Continuous Upper Confidence Trees with Polynomial Exploration – Consistency

Cited by 35 publications

References 12 publications

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

Incremental Search Space Construction for Machine Learning Pipeline Synthesis

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