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John Sipple

John A. Sipple

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2020 – today

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2024
[c4]
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  - conf/buildsys/GoldfederS24
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Judah Goldfeder, John Sipple:
Reducing Carbon Emissions at Scale: Interpretable and Efficient to Implement Reinforcement Learning via Policy Extraction. BuildSys 2024
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  - journals/corr/abs-2410-03756
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Judah Goldfeder, John Sipple:
Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability. CoRR abs/2410.03756 (2024)
2023
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  - conf/sensys/GoldfederS23
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Judah A. Goldfeder, John A. Sipple:
A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings. BuildSys 2023: 352-356
[i3]
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  - journals/corr/abs-2310-08569
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Judah Goldfeder, John Sipple:
A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings. CoRR abs/2310.08569 (2023)
2022
[c2]
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  - conf/ismis/SippleY22
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John Sipple, Abdou Youssef:
A General-Purpose Method for Applying Explainable AI for Anomaly Detection. ISMIS 2022: 162-174
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  - journals/corr/abs-2207-11564
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John Sipple, Abdou Youssef:
A general-purpose method for applying Explainable AI for Anomaly Detection. CoRR abs/2207.11564 (2022)
2020
[c1]
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John Sipple:
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. ICML 2020: 9016-9025
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  - journals/corr/abs-2007-10088
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John Sipple:
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. CoRR abs/2007.10088 (2020)

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