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arXiv 2021年

作者： Leqi, Liu Kennedy, Edward H. Machine Learning Department Department of Statistics & Data Science Carnegie Mellon University United States

Optimal treatment regimes are personalized policies for making a treatment decision based on subject characteristics, with the policy chosen to maximize some value. It is common to aim to maximize the mean outcome in the population, via a regime assigning treatment only to those whose mean outcome is higher under treatment versus control. However, the mean can be an unstable measure of centrality, resulting in imprecise statistical procedures, as well as unfair decisions that can be overly influenced by a small fraction of subjects. In this work, we propose a new median optimal treatment regime that instead treats individuals whose conditional median is higher under treatment. This ensures that optimal decisions for individuals from the same group are not overly influenced either by (i) a small fraction of the group (unlike the mean criterion), or (ii) unrelated subjects from different groups (unlike marginal median/quantile criteria). We introduce a new measure of value, the Average Conditional Median Effect (ACME), which summarizes across-group median treatment outcomes of a policy, and which the optimal median treatment regime maximizes. After developing key motivating examples that distinguish median optimal treatment regimes from mean and marginal median optimal treatment regimes, we give a nonparametric efficiency bound for estimating the ACME of a policy, and propose a new doubly robust-style estimator that achieves the efficiency bound under weak conditions. Finite-sample properties of the estimator are explored via numerical simulations and the proposed algorithm is illustrated using data from a randomized clinical trial in patients with HIV. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency

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Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques

arXiv

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arXiv 2021年

作者： Oliveira, Natalia L. Lei, Jing Tibshirani, Ryan J. Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States

We develop a new approach for estimating the risk of an arbitrary estimator of the mean vector in the classical normal means problem. The key idea is to generate two auxiliary data vectors, by adding carefully constructed normal noise vectors to the original data. We then train the estimator of interest on the first auxiliary vector and test it on the second. In order to stabilize the risk estimate, we average this procedure over multiple draws of the synthetic noise vector. A key aspect of this coupled bootstrap (CB) approach is that it delivers an unbiased estimate of risk under no assumptions on the estimator of the mean vector, albeit for a modified and slightly "harder" version of the original problem, where the noise variance is elevated. We prove that, under the assumptions required for the validity of Stein’s unbiased risk estimator (SURE), a limiting version of the CB estimator recovers SURE exactly. We then analyze a bias-variance decomposition of the error of the CB estimator, which elucidates the effects of the variance of the auxiliary noise and the number of bootstrap samples on the accuracy of the estimator. Lastly, we demonstrate that the CB estimator performs favorably in various simulated experiments. © 2021, CC BY.

关键词： Vectors

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Monitoring AI-modified content at scale: a case study on the impact of ChatGPT on AI conference peer reviews 24

Monitoring AI-modified content at scale: a case study on the...

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Proceedings of the 41st International Conference on machine learning

作者： Weixin Liang Zachary Izzo Yaohui Zhang Haley Lepp Hancheng Cao Xuandong Zhao Lingjiao Chen Haotian Ye Sheng Liu Zhi Huang Daniel A. McFarland James Y. Zou Department of Computer Science Stanford University Machine Learning Department NEC Labs America Department of Electrical Engineering Stanford University Graduate School of Education Stanford University Department of Computer Science and Department of Management Science and Engineering Stanford University Department of Computer Science UC Santa Barbara Department of Biomedical Data Science Stanford University Graduate School of Education and Department of Sociology and Graduate School of Business Stanford University Department of Computer Science and Department of Electrical Engineering and Department of Biomedical Data Science Stanford University

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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A Comprehensive Review on Deep learning Based Fall Detection in Elderly People

A Comprehensive Review on Deep Learning Based Fall Detection...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： S. Venkata Suryanarayana Lakshmi H.N Posimsetti T Chiranjeevi Swamy D. Bhanu Mahesh Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Swarnandhra College of Engineering and Technology Narsapuram Andhra Pradesh India Department of Computer Science and Engineering KMIT Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Convolutional neural networks Fall detection Older adults Wearable sensors Long short term memory Injuries

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Intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Jay Patil Suvarna Joshi Rahesha Mulla Kushal Yadav Department of Computer Science and Eng. MIT School of Computing MIT ADT University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Data Science Indian Institute of Technology Madras India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods.

关键词： Weight measurement Accuracy Ultrasonic variables measurement Ergonomics Real-time systems Acoustics Intelligent sensors Monitoring Wearable sensors Testing

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Pass: Peer-Agreement Based Sample Selection for Training with Instance-Dependent Noisy Labels

SSRN

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SSRN 2024年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Data Science and AI Monash University Australia

The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples, yet our empirical observations reveal their limitations, especially for labels with instance dependent noise (IDN). An important characteristic of IDN is the difficulty to distinguish the clean-label samples that lie near the decision boundary (i.e., the hard samples) from the noisy-label samples. We, therefore, propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem. Utilising a trio of classifiers, PASS employs consensus-driven peer-based agreement of two models to select the samples to train the remaining model. PASS is easily integrated into existing LNL models, enabling the improvement of the detection accuracy of noisy- and clean-label samples, which increases the classification accuracy across various LNL benchmarks. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A unified framework for bandit multiple testing 21

A unified framework for bandit multiple testing

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ziyu Xu Ruodu Wang Aaditya Ramdas Department of Statistics and Data Science Carnegie Mellon University Department of Statistics and Actuarial Science University of Waterloo Canada Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University

ISBN: (纸本)9781713845393

In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that we wish to test, and the goal is to design adaptive algorithms that correctly identify large set of interesting arms (true discoveries), while only mistakenly identifying a few uninteresting ones (false discoveries). One common metric in non-bandit multiple testing is the false discovery rate (FDR). We propose a unified, modular framework for bandit FDR control that emphasizes the decoupling of exploration and summarization of evidence. We utilize the powerful martingale-based concept of "e-processes" to ensure FDR control for arbitrary composite nulls, exploration rules and stopping times in generic problem settings. In particular, valid FDR control holds even if the reward distributions of the arms could be dependent, multiple arms may be queried simultaneously, and multiple (cooperating or competing) agents may be querying arms, covering combinatorial semi-bandit type settings as well. Prior work has considered in great detail the setting where each arm's reward distribution is independent and sub-Gaussian, and a single arm is queried at each step. Our framework recovers matching sample complexity guarantees in this special case, and performs comparably or better in practice. For other settings, sample complexities will depend on the finer details of the problem (composite nulls being tested, exploration algorithm, data dependence structure, stopping rule) and we do not explore these; our contribution is to show that the FDR guarantee is clean and entirely agnostic to these details.

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Chest X-Ray Analysis Using machine learning for Pneumonia Detection

Chest X-Ray Analysis Using Machine Learning for Pneumonia De...

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data, Computation and Communication (ICDCC), International Conference on

作者： Thenmozhi MI Nithya Sree R S Mohammed Esa Khan J Akshaya R B Barath kumar L Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of CSE (Artificial Intelligence and Machine Learning) Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

A dangerous and potentially fatal respiratory infection is pneumonia., especially for young children and the elderly. With the shortage of radiologists, our automated approach can help fill the gap by providing a quick and reliable diagnosis. By leveraging advanced machine learning techniques, with the ultimate goal of improving patient outcomes by increasing the effectiveness and accuracy of pneumonia detection. Our study proposes an automated approach to detect pneumonia by applying advanced machine learning strategies to chest radiographs. The method is taking relevant information out of the photos and using a robust classification algorithm to distinguish between healthy images and images that show pneumonia. For image processing tasks, the Convolutional Neural Network (CNN) algorithm is selected due to its efficacy. A useful indicator for assessing pneumonia detection systems, the F1 score aids in determining how well machine learning models work. By indicating the model's capacity to precisely detect optimistic cases while reducing false positives, it offers a reasonable valuation of overall performance. The created model is a useful tool to assist healthcare workers in promptly identifying cases of pneumonia, since it demonstrates promising sensitivity and specificity. The findings of this study have implications for respiratory infection treatment that include accelerating diagnosis times, decreasing human error, and eventually increasing patient outcomes.

关键词： Pneumonia Accuracy Computational modeling machine learning Medical services data models Classification algorithms Convolutional neural networks Reliability X-ray imaging

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On the sample complexity and metastability of heavy-tailed policy search in continuous control

The Journal of Machine Learning Research

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The Journal of machine learning Research 2024年第1期25卷 1897-1954页

作者： Amrit Singh Bedi Anjaly Parayil Junyu Zhang Mengdi Wang Alec Koppel Department of Computer Science University of Central FLorida Orlando FL Microsoft India Bengaluru Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical Engineering Center for Statistics and Machine Learning Princeton University/Deepmind Princeton NJ JP Morgan AI Research New York NY

Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to continuous spaces, we focus on policy search where one iteratively improves a parameterized policy with stochastic policy gradient (PG) updates. In tabular Markov Decision Problems (MDPs), under persistent exploration and suitable parameterization, global optimality may be obtained. By contrast, in continuous space, the non-convexity poses a pathological challenge as evidenced by existing convergence results being mostly limited to stationarity or arbitrary local extrema. To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter a, which increases the likelihood of jumping in state space. Doing so invalidates smoothness conditions of the score function common to PG. Thus, we establish how the convergence rate to stationarity depends on the policy's tail index α, a Hölder continuity parameter, integrability conditions, and an exploration tolerance parameter introduced here for the first time. Further, we characterize the dependence of the set of local maxima on the tail index through an exit and transition time analysis of a suitably defined Markov chain, identifying that policies associated with Lévy Processes of a heavier tail converge to wider peaks. This phenomenon yields improved stability to perturbations in supervised learning, which we corroborate also manifests in improved performance of policy search, especially when myopic and farsighted incentives are misaligned.

关键词： policy gradient algorithm heavy-tailed distributions non-convex optimization

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An Enchanced Approach for Early Chronic Kidney Disease Prediction Using Advanced machine learning and data Processing Techniques

An Enchanced Approach for Early Chronic Kidney Disease Predi...

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International Conference on Inventive Computation Technologies (ICICT)

作者： S. Poongodi K. Sasirekha Sheela Rini Jeyapriya. S G.Dona Rashmi Department of Data Science PSGR Krishnammal College for Women Coimbatore India Department of Computer Science(Artificial Intelligence and Data Science) Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Deparrtment of Computer Science(PG) PSGR Krishnammal College for Women Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Artificial Intelligence and Machine Learning Kongunadu Arts and Science College Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic renal disease is a medical disorder in which the kidneys gradually slow down, reducing their ability to filter blood. Diabetes, high blood pressure, heart disease, and a family history of renal failure are the primary risk factors for this condition. The prognosis for patients with chronic kidney disease (CKD) varies according to the stage at which they are diagnosed. Stages 0 and 1 represent the early stages of the disease, whereas stage 4 represents the severe stages, where the kidneys are nearly at the point of failure. Early diagnosis is therefore essential for the patient's survival and the appropriate therapies. Blood tests, biopsies, urine tests, and imaging tests are among the medical diagnostic techniques that can be used to diagnose chronic kidney disease (CKD). However, these medical diagnostic techniques have certain drawbacks and restrictions. They are employed to identify diseases in their later stages and can occasionally yield erroneous results; they are also expensive. Consequently, the researcher has discovered a novel technique for the early identification of CKD that use machine learning algorithms to overcome these complications because machine learning algorithms are becoming increasingly significant in health care datasets. These methods also have issues with overfitting, class imbalance, biased results, problems with dataset interpretability, and high computing costs. Therefore, improved preprocessing techniques and machine learning algorithms are used in this research work to overcome the inadequacies of the existing approaches for CKD prediction. The model is tested using the Kaggle dataset on chronic renal disease. Class imbalance and missing attribute values are issues with this dataset. Additionally, this dataset has several categorical attribute values and additional variances in attribute values. A few preprocessing techniques are used to improve the data quality, including K-Nearest Neighbour for filling in missing attribute v

关键词： Adaptation models Accuracy machine learning algorithms Computational modeling machine learning Predictive models Chronic kidney disease Encoding Optimization Diseases

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