by jsendak | Apr 11, 2025 | AI
arXiv:2504.07131v1 Announce Type: new
Abstract: Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
Embedding Reliability Verification Constraints into Generation Expansion Planning
In generation planning, there is a challenge in managing the incompatible mathematical structures between stochastic production simulations and optimization models. This incompatibility creates difficulties in integrating reliability constraints into the planning process. However, an approach using a weighted oblique decision tree (WODT) technique has been proposed to solve this problem.
The proposed approach involves generating a generation mix dataset labeled with reliability assessment simulations for each planning year. This dataset is then used to train a WODT model. Using depth-first search technique, reliability-feasible regions are extracted and transformed into disjunctive constraints. These constraints are further converted into mixed-integer linear form using a convex hull modeling technique. Finally, the transformed constraints are embedded into a unit commitment-integrated generation expansion planning model.
This multi-disciplinary approach combines concepts from mathematical modeling, optimization, and reliability assessment. By leveraging the WODT technique, the proposed approach enables the integration of reliability constraints into generation expansion planning, ultimately leading to reliable and optimal planning solutions.
The effectiveness of this approach is demonstrated through a case study for the Electric Reliability Council of Texas (ERCOT) region. The long-term generation planning study validates the proposed approach, showing that it can achieve both reliability and optimality in the planning solutions.
Overall, this research contributes to the field of generation planning by addressing the challenge of integrating reliability constraints. The approach presented in this study provides a framework for effectively incorporating reliability assessment simulations into the planning process, leading to more robust and reliable generation expansion plans.
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by jsendak | Dec 11, 2024 | AI
arXiv:2412.06810v1 Announce Type: new
Abstract: Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or discrete. Recently, certain works have demonstrated the utility of this rich yet complex treatment information into the estimation process, resulting in better causal effect estimation. However, these works have been demonstrated on either graphs or textual treatments. There is a notable gap in existing literature in addressing higher dimensional data such as images that has a wide variety of applications. In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images. NICE demonstrates an effective way to use the rich multidimensional information present in image treatments that helps in obtaining improved causal effect estimates. To evaluate the performance of NICE, we propose a novel semi-synthetic data simulation framework that generates potential outcomes when images serve as treatments. Empirical results on these datasets, under various setups including the zero-shot case, demonstrate that NICE significantly outperforms existing models that incorporate treatment information for causal effect estimation.
Expert Commentary
Estimating causal effects in observational studies is a challenging task due to the lack of ground truth data and treatment assignment bias. In this article, the authors highlight the limitations of existing methods that consider multi-dimensional treatment information as scalar, and propose a new model called NICE (Network for Image treatments Causal effect Estimation) to address this issue specifically for image treatments.
One of the key contributions of this work is incorporating rich, multidimensional information present in image treatments to improve causal effect estimation. While previous studies have mainly focused on graphs or textual treatments, the authors recognize the wide variety of applications that involve image treatments and aim to bridge the gap in the existing literature.
A multi-disciplinary approach is essential when dealing with image treatments as it requires knowledge from various domains such as computer vision, machine learning, and causal inference. The authors leverage techniques from these fields in developing NICE and demonstrate its effectiveness through empirical results.
Additionally, the authors propose a novel semi-synthetic data simulation framework to evaluate the performance of NICE. This framework generates potential outcomes when images are utilized as treatments, allowing for a comprehensive evaluation of the model under various scenarios, including the challenging zero-shot case.
The results from the experiments show that NICE outperforms existing models that incorporate treatment information for causal effect estimation. This highlights the importance of considering the multidimensional nature of image treatments and the potential improvements that can be achieved by leveraging this information effectively.
In conclusion, the proposed NICE model addresses the limitations of existing methods by incorporating rich multidimensional information in the estimation process for image treatments. The multi-disciplinary nature of this work, combining concepts from computer vision, machine learning, and causal inference, showcases the potential for advancements in causal effect estimation in various domains involving image treatments.
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by jsendak | Dec 3, 2024 | AI
arXiv:2411.18657v1 Announce Type: new
Abstract: Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to score or classify multiple visualizations choices to recommend the most effective ones, as per the statistics. However, state-of-the art models rely on very large number of expensive statistics and therefore using such models on large datasets become infeasible due to prohibitively large computational time, limiting the effectiveness of such techniques to most real world complex and large datasets. In this paper, we propose a novel reinforcement-learning (RL) based framework that takes a given vis-rec model and a time-budget from the user and identifies the best set of input statistics that would be most effective while generating the visual insights within a given time budget, using the given model. Using two state-of-the-art vis-rec models applied on three large real-world datasets, we show the effectiveness of our technique in significantly reducing time-to visualize with very small amount of introduced error. Our approach is about 10X times faster compared to the baseline approaches that introduce similar amounts of error.
Automated Visualization Recommendations in Data Analysis
Automated visualization recommendations have become indispensable tools in data analysis, helping users to extract crucial insights from complex datasets. These recommendations are generated by models that calculate numerous statistics from the dataset and then employ machine learning algorithms to score and classify various visualization options, suggesting the most effective ones based on the statistics. However, existing models heavily rely on a large number of computationally expensive statistics, making them impractical for analyzing large datasets. As a result, these techniques often fail to provide efficient and effective visualization recommendations for real-world complex datasets.
To overcome this limitation, the authors propose a novel framework based on reinforcement learning (RL) to optimize visualization recommendations within a given time budget. The user provides a vis-rec model and a predefined time budget, and the RL algorithm identifies the most effective set of input statistics for generating visual insights within the given time constraints.
The multi-disciplinary nature of this research is evident in the integration of machine learning, data analysis, and reinforcement learning techniques. By combining these different fields, the authors aim to improve the efficiency and effectiveness of automated visualization recommendations.
Experimental Results
In order to evaluate their proposed framework, the authors conducted experiments using two state-of-the-art vis-rec models on three large real-world datasets. The results demonstrated the effectiveness of their technique in significantly reducing the time required to generate visualizations, while introducing only a small amount of error.
Compared to baseline approaches that introduce similar amounts of error, the proposed RL-based framework was found to be approximately 10 times faster. This substantial reduction in computational time makes it feasible to apply automated visualization recommendations on large and complex datasets, thus enhancing the usefulness of these techniques in real-world scenarios.
Future Directions
This research opens up several avenues for further exploration. Firstly, there is scope to investigate different reinforcement learning algorithms and their impact on the optimization of visualization recommendations. Additionally, examining the applicability of the proposed framework to different types of datasets and vis-rec models could provide valuable insights.
Furthermore, exploring the potential of incorporating domain knowledge and user preferences into the RL framework could lead to more personalized and context-aware visualization recommendations. By considering the unique characteristics of each dataset and the specific needs of users, the framework can generate recommendations that align with domain-specific requirements.
Overall, this research sheds light on the importance of efficient visualization recommendation techniques and introduces a promising approach using reinforcement learning. By addressing the computational challenges associated with large datasets, this framework paves the way for more effective and scalable automated visualization recommendations in diverse domains.
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by jsendak | Jul 26, 2024 | AI
arXiv:2407.17518v1 Announce Type: new
Abstract: Current approaches to identifying driving heterogeneity face challenges in comprehending fundamental patterns from the perspective of underlying driving behavior mechanisms. The concept of Action phases was proposed in our previous work, capturing the diversity of driving characteristics with physical meanings. This study presents a novel framework to further interpret driving patterns by classifying Action phases in an unsupervised manner. In this framework, a Resampling and Downsampling Method (RDM) is first applied to standardize the length of Action phases. Then the clustering calibration procedure including ”Feature Selection”, ”Clustering Analysis”, ”Difference/Similarity Evaluation”, and ”Action phases Re-extraction” is iteratively applied until all differences among clusters and similarities within clusters reach the pre-determined criteria. Application of the framework using real-world datasets revealed six driving patterns in the I80 dataset, labeled as ”Catch up”, ”Keep away”, and ”Maintain distance”, with both ”Stable” and ”Unstable” states. Notably, Unstable patterns are more numerous than Stable ones. ”Maintain distance” is the most common among Stable patterns. These observations align with the dynamic nature of driving. Two patterns ”Stable keep away” and ”Unstable catch up” are missing in the US101 dataset, which is in line with our expectations as this dataset was previously shown to have less heterogeneity. This demonstrates the potential of driving patterns in describing driving heterogeneity. The proposed framework promises advantages in addressing label scarcity in supervised learning and enhancing tasks such as driving behavior modeling and driving trajectory prediction.
Analysis of the Content
The content of this article highlights the challenges in identifying driving heterogeneity and proposes a novel framework for interpreting driving patterns. It introduces the concept of Action phases, which capture the diversity of driving characteristics with physical meanings. The framework involves a Resampling and Downsampling Method (RDM) to standardize the length of Action phases, followed by a clustering calibration procedure to classify the patterns.
One of the significant aspects of this study is its multi-disciplinary nature, combining concepts from physics, data analysis, and machine learning. By leveraging the physical meanings of Action phases, the framework aims to provide a deeper understanding of driving behavior mechanisms. The use of unsupervised learning techniques in the clustering calibration procedure allows for the identification of patterns without relying on labeled data.
The application of the framework to real-world datasets revealed six driving patterns in the I80 dataset, with both stable and unstable states. The observation that unstable patterns are more numerous than stable ones aligns with the dynamic nature of driving. The study also compares the results of the I80 dataset with the US101 dataset, demonstrating that the proposed framework can capture the variations in driving heterogeneity.
From an expert standpoint, this research has several implications for the field of driving behavior modeling and prediction. The framework addresses the challenge of label scarcity in supervised learning by utilizing unsupervised methods. This is especially valuable in contexts where obtaining labeled data is difficult or expensive. The insights gained from understanding driving patterns can contribute to the development of more accurate driving behavior models and trajectory predictions.
Conclusion
This article presents a novel framework for interpreting driving patterns based on the concept of Action phases. The framework combines concepts from physics, data analysis, and machine learning to capture the diversity of driving characteristics and address the challenges in identifying driving heterogeneity. The application of the framework to real-world datasets demonstrates its potential in describing driving patterns and providing insights into driving behavior mechanisms. This research opens new avenues for improving driving behavior modeling and prediction tasks, particularly in scenarios where labeled data is scarce.
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by jsendak | Jul 24, 2024 | AI
arXiv:2407.16062v1 Announce Type: new
Abstract: Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice.
This invited article has undergone anonymous review and is intended as a book chapter for the volume “Frontiers of Statistics and Data Science” edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled “Artificial Intelligence in Precision and Digital Health” taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.
The Intersection of Artificial Intelligence and Precision Health
Precision health, supported by digital technologies, is a rapidly evolving field that aims to revolutionize healthcare by individualizing treatment and preventive strategies. This paradigm shift is made possible by the advances in artificial intelligence (AI) and machine learning, which have the potential to transform the way we approach healthcare.
Artificial intelligence, particularly reinforcement learning (RL), has shown immense promise in solving complex and dynamic problems in the healthcare domain. RL is a subfield of machine learning that focuses on teaching an agent to make a sequence of decisions to maximize long-term rewards. In the context of healthcare, RL can be used to develop adaptive interventions and just-in-time interventions, making it a valuable tool in precision and digital health.
This article, authored by Bibhas Chakraborty, provides a comprehensive overview of the application of RL in the context of precision and digital health. It delves into the methodological aspects of RL and explores its potential in solving real-world healthcare problems. The article also includes case studies that highlight the successful implementation of RL in healthcare practice.
One of the key strengths of this article is its interdisciplinary nature. The intersection of AI, statistics, and healthcare is a multi-disciplinary field that requires expertise in various domains. The author, with his background in artificial intelligence and extensive experience in teaching and research, is well-equipped to bridge these domains and provide valuable insights.
By focusing on adaptive interventions, the article sheds light on the potential of RL in dynamically adjusting treatment strategies based on individual patient responses. This personalized approach has the potential to greatly improve patient outcomes and reduce healthcare costs. Moreover, the article goes beyond theoretical discussions by presenting real-world applications of RL in healthcare, offering tangible evidence of its efficacy.
The Implications for Future Research
The integration of AI and precision health is still in its early stages, and there is much scope for further research and development. One area that warrants further exploration is the ethical implications of using AI in healthcare. As AI algorithms make decisions that impact human lives, ensuring fairness, transparency, and accountability becomes paramount.
Additionally, the scalability and generalizability of RL algorithms need to be addressed. While RL has shown promise in small-scale studies and specific domains, its application in larger healthcare systems still poses challenges. Developing robust and scalable RL algorithms that can be readily deployed in diverse healthcare settings is an important avenue for future research.
Furthermore, the integration of RL with other emerging technologies such as wearable devices and electronic health records holds immense potential. By leveraging data from these sources, RL algorithms can gain a more comprehensive understanding of individual patient’s needs and preferences, leading to more effective interventions.
In conclusion, this article serves as a valuable resource for researchers, practitioners, and policymakers in the field of precision and digital health. It highlights the potential of RL in transforming healthcare delivery and provides a roadmap for future research. As the field continues to evolve, it is crucial to adopt a multi-disciplinary approach that brings together expertise from AI, statistics, and healthcare to harness the full potential of AI in precision health.
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