270 research outputs found
Efficient Localized Inference for Large Graphical Models
We propose a new localized inference algorithm for answering marginalization
queries in large graphical models with the correlation decay property. Given a
query variable and a large graphical model, we define a much smaller model in a
local region around the query variable in the target model so that the marginal
distribution of the query variable can be accurately approximated. We introduce
two approximation error bounds based on the Dobrushin's comparison theorem and
apply our bounds to derive a greedy expansion algorithm that efficiently guides
the selection of neighbor nodes for localized inference. We verify our
theoretical bounds on various datasets and demonstrate that our localized
inference algorithm can provide fast and accurate approximation for large
graphical models
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps
We consider a challenging theoretical problem in offline reinforcement
learning (RL): obtaining sample-efficiency guarantees with a dataset lacking
sufficient coverage, under only realizability-type assumptions for the function
approximators. While the existing theory has addressed learning under
realizability and under non-exploratory data separately, no work has been able
to address both simultaneously (except for a concurrent work which we compare
in detail). Under an additional gap assumption, we provide guarantees to a
simple pessimistic algorithm based on a version space formed by marginalized
importance sampling, and the guarantee only requires the data to cover the
optimal policy and the function classes to realize the optimal value and
density-ratio functions. While similar gap assumptions have been used in other
areas of RL theory, our work is the first to identify the utility and the novel
mechanism of gap assumptions in offline RL with weak function approximation
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
This paper studies regret minimization with randomized value functions in
reinforcement learning. In tabular finite-horizon Markov Decision Processes, we
introduce a clipping variant of one classical Thompson Sampling (TS)-like
algorithm, randomized least-squares value iteration (RLSVI). Our
high-probability worst-case regret bound
improves the previous sharpest worst-case regret bounds for RLSVI and matches
the existing state-of-the-art worst-case TS-based regret bounds.Comment: Updated version, bug fixe
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book
Heat transfer across a nanoscale pressurized air gap and its application in magnetic recording
In this study, we investigated how a thermally actuated air bearing slider heats up a fast-spinning storage disk through a highly pressurized nanoscale air gap in a magnetic recording system. A Euleriandescription- based computational approach is developed considering heat conduction through a pressurized air film and near-field radiation across the gap. A set of field equations that govern the air bearing dynamics, slider thermo-mechanics and disk heat dissipation are solved simultaneously through an iterative approach. A temperature field on the same order as the hot slider surface itself is found to be established in the disk. The effective local heat transfer coefficient is found to vary substantially with disk materials and linear speeds. This approach quantifies the magnitude of different thermal transport schemes and the accuracy is verified by an excellent agreement with our experiment, which measures the local slider temperature rise with a resistance temperature sensor. It also demonstrates an effective computational approach to treat transient thermal processes in a system of components with fast relative speed and different length scales. Finally, the investigated thermal transport mechanism leads to a substantial spacing change that has a significant impact on the spacing margin of today’s magnetic storage systems
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book
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