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41st International Conference on machine learning, ICML 2024

作者： Shekhar, Shubhanshu Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We consider the problem of sequential change detection under minimal assumptions on the distribution generating the stream of observations. Formally, our goal is to design a scheme for detecting any changes in a parameter or functional θ of the data stream distribution that has small detection delay, but guarantees control on the frequency of false alarms in the absence of changes. We describe a simple reduction from sequential change detection to sequential estimation using confidence sequences (CSs): begin a new level-(1 − α) CS at each time step, and proclaim a change as soon as the intersection of all active CSs becomes empty. We prove that the average run length of our scheme is at least 1/α, resulting in a change detection scheme with minimal structural assumptions (thus allowing for possibly dependent observations, and nonparametric distribution classes), but strong guarantees. We also describe an interesting parallel with Lorden's reduction from change detection to sequential testing and connections to the recent "e-detector" framework. Copyright 2024 by the author(s)

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Optimized Automated Stock Trading using DQN and Double DQN

Optimized Automated Stock Trading using DQN and Double DQN

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Bharadwaj, Gurudutt S Pratap, David Darapaneni, Narayana Pes University Department of Computer Science Bengaluru India Great Learning Department of Data Science and Machine Learning Bengaluru India

ISBN: (纸本)9798350360660

Stock Portfolio management involves managing the buying, holding and selling decisions for the various stocks in the portfolio. There has been work where Reinforcement learning (RL) based actor-critic methods like Deep Direct Policy Gradient (DDPG) have been used for asset allocation problems. Here an attempt has been made to use solely critic-based value function methods like Deep Q-network (DQN) and Double DQN for estimating Q-values of market actions. Then, an optimized portfolio management algorithm designed to balance trades across a basket of stocks is designed. Five stocks are chosen of different price ranges from NYSE and NASDAQ stock exchanges. The average cumulative percentage returns provided by DQN was 55% on testing data with an average Maximum Drawdown (MDD) of 2.5%. The same with Double DQN was 71% on testing data with an average MDD of 2.83%. These results were significantly better than the case when a traditional Buy and Hold strategy was to be employed. © 2024 IEEE.

关键词： Reinforcement learning

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Functional linear non-Gaussian acyclic model for causal discovery

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Behaviormetrika 2024年第2期51卷 567-588页

作者： Yang, Tian-Le Lee, Kuang-Yao Zhang, Kun Suzuki, Joe Graduate School of Engineering Science Osaka University Osaka Japan Department of Statistics Operations and Data Science Temple University Philadelphia United States Machine Learning Department Carnegie Mellon University Pittsburgh United States Machine Learning Department MBZUAI Abu Dhabi United Arab Emirates

In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a linear non-Gaussian acyclic model (LiNGAM), encompassing both the causal ordering of variables and their respective connection strengths. However, LiNGAM can only deal with the finite-dimensional case. To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the functional linear non-Gaussian acyclic model (Func-LiNGAM). Our motivation stems from the desire to identify causal relationships in brain-effective connectivity tasks involving, for example, fMRI and EEG datasets. We demonstrate why the original LiNGAM fails to handle these inherently infinite-dimensional datasets and explain the availability of functional data analysis from both empirical and theoretical perspectives. We establish theoretical guarantees of the identifiability of the causal relationship among non-Gaussian random vectors and even random functions in infinite-dimensional Hilbert spaces. To address the issue of sparsity in discrete time points within intrinsic infinite-dimensional functional data, we propose optimizing the coordinates of the vectors using functional principal component analysis. Experimental results on synthetic data verify the ability of the proposed framework to identify causal relationships among multivariate functions using the observed samples. For real data, we focus on analyzing the brain connectivity patterns derived from fMRI data. © The Behaviormetric Society 2024.

关键词： Causal discovery Darmois–Skitovich theorem Functional data Gaussian process LiNGAM Non-Gaussian

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Foundations of Testing for Finite-Sample Causal Discovery 41

Foundations of Testing for Finite-Sample Causal Discovery

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41st International Conference on machine learning, ICML 2024

作者： Yan, Tom Xu, Ziyu Lipton, Zachary Machine Learning Department Carnegie Mellon University Pittsburgh United States Department of Statistics and Data Science Carnegie Mellon University Pittsburgh United States

Discovery of causal relationships is a fundamental goal of science and vital for sound decision making. As such, there has been considerable interest in causal discovery methods with provable guarantees. Existing works have thus far largely focused on discovery under hard intervention and infinite-samples, in which intervening on a node readily reveals the orientation of every edge incident to the node. This setup however overlooks the stochasticity inherent in real-world, finite-sample settings. Our work takes a step towards studying finite-sample causal discovery, wherein multiple interventions on a node are now needed for edge orientation. In this work, we study the canonical setup in theoretical causal discovery literature, where one assumes causal sufficiency and access to the graph skeleton. Our key observation is that discovery may be viewed as structured, multiple testing, and we develop a novel testing framework to this end. Crucially, our framework allows for anytime valid testing as multiple tests are needed to conclude an edge orientation. It also allows for flexible combination of structured test-statistics (enabling one to use Meek rules to propagate edge orientation) as well as robust testing. Through empirical simulations, we confirm the usefulness of our framework. In closing, using this testing framework, we show how one may efficiently verify graph structure by drawing a connection to multi-constraint bandits and designing a novel algorithm to this end. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Image Recognition for Wildlife Conservation

Image Recognition for Wildlife Conservation

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Sridharan, S. Yuvan Shankar, S. Sriram, M. Yashvanth, K.P. Department of Artificial Intelligence and Machine Learning Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natural ecosystem, including images captured through camera traps, drones, and other monitoring devices. This comprehensive and diverse dataset is then processed using advanced Image Processing Tools. A key strength of this methodology lies in its use of machine learning algorithms, particularly YOLO v8 (You Only Look Once) and Support Vector machines (SVMs). These techniques are suitable for wildlife conservation because of their capacity to analyze complex visual data and detect patterns indicative of species presence, behavior, and habitat conditions. The paper outlines the application of YOLO v8 and SVMs to the collected image data, enabling accurate identification, real-time detection, and classification of wildlife species. This capability is essential for monitoring populations, tracking movements, and identifying potential threats, thus facilitating timely conservation actions. Furthermore, the research extends its evaluation by comparing the results of the machine learning approach with traditional ecological survey methods, offering a comprehensive assessment of the effectiveness of image processing in wildlife conservation. © 2025 IEEE.

关键词： Invertebrates

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An Efficient Doubly-Robust Test for the Kernel Treatment Effect 37

An Efficient Doubly-Robust Test for the Kernel Treatment Eff...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Martinez-Taboada, Diego Ramdas, Aaditya Kennedy, Edward H. Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781713899921

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treatment. It is, to the best of our knowledge, the first kernel-based, doubly-robust test with provably valid type-I error. Furthermore, our proposed algorithm is computationally efficient, avoiding the use of permutations. © 2023 Neural information processing systems foundation. All rights reserved.

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Hybrid speech enhancement in modulation domain

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Multimedia Tools and Applications 2024年 1-31页

作者： P S, Praveen Kumar H S, Jayanna Technical Lead-Machine Learning Merit Data and Technology Tamil Nadu Chennai India Department of Information Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur India

This paper presents a comprehensive study on speech enhancement (SE) techniques, particularly focusing on the utilization of the discrete cosine transform (DCT) in the modulation domain (MD) in combination with the minimum mean square error (MMSE) and Kalman filter (KF) algorithms. The research investigates the efficacy of these techniques in improving speech quality and intelligibility under various noisy conditions, such as babble, drilling, horn, train, and traffic noises, across different signal-to-noise ratio (SNR) levels. Additionally, the study evaluates the performance of recurrent neural network (RNN) algorithms alongside traditional SE approaches. The study highlights the advantages of employing DCT in the modulation domain over the traditional discrete Fourier transform (DFT) in SE applications. Experimental results demonstrate significant improvements in objective quality metrics, including Perceptual Evaluation of Speech Quality (PESQ), composite measures, and short-time objective intelligibility (STOI), when implementing SE algorithms using discrete cosine transform in modulation domain. The proposed hybrid speech enhancement techniques, leveraging minimum mean square error and Kalman filter in combination with discrete cosine transform in modulation domain, outperform individual speech enhancement algorithms, showcasing superior noise reduction capabilities. This paper contributes to the advancement of speech enhancement methodologies, particularly in real-world noisy environments, and underscores the effectiveness of discrete cosine transform-based approaches in enhancing speech quality and intelligibility. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

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Privacy Preserving data Imputation via Multi-party Computation for Medical Applications

Privacy Preserving Data Imputation via Multi-party Computati...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Jentsch, Julia Ünal, Ali Burak Mağara, Şeyma Selcan Akgün, Mete Department of Computer Science Medical Data Privacy and Privacy Preserving Machine Learning Tübingen Germany

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350548

Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whole dataset, which contradicts the privacy regulations when the data is distributed among multiple sources. Especially in the medical and healthcare domain, such access reveals sensitive information about patients. This study addresses privacy-preserving imputation methods for sensitive data using secure multi-party computation, enabling secure computations without revealing any party’s sensitive information. In this study, we realized the mean, median, regression, and kNN imputation methods in a privacy-preserving way. We specifically target the medical and healthcare domains considering the significance of protection of the patient data, showcasing our methods on a diabetes dataset. Experiments on the diabetes dataset validated the correctness of our privacy-preserving imputation methods, yielding the largest error around 3 × 10-3, closely matching plaintext methods. We also analyzed the scalability of our methods to varying numbers of samples, showing their applicability to real-world healthcare problems. Our analysis demonstrated that all our methods scale linearly with the number of samples. Except for kNN, the runtime of all our methods indicates that they can be utilized for large datasets. © 2024 IEEE.

关键词： Differential privacy

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Using Generative Models to Improve Fire Detection Efficiency 10

Using Generative Models to Improve Fire Detection Efficiency

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Andriyanov, Nikita Kim, Alexandr Fao, Xenin Data Analysis And Machine Learning Department Financial University Under The Government Of The Russian Federation Moscow Russia Data Science Department Huazhong University Of Science And Technology Huazhong China

ISBN: (纸本)9798350372939

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1-2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models. © 2024 IEEE.

关键词： Generative adversarial networks

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Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling 38

Low-Rank Optimal Transport through Factor Relaxation with La...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Halmos, Peter Liu, Xinhao Gold, Julian Raphael, Benjamin J. Department of Computer Science Princeton University United States Center for Statistics and Machine Learning Princeton University United States

Optimal transport (OT) is a general framework for finding a minimum-cost transport plan, or coupling, between probability distributions, and has many applications in machine learning. A key challenge in applying OT to massive datasets is the quadratic scaling of the coupling matrix with the size of the dataset. Forrow et al. (2019) introduced a factored coupling for the k-Wasserstein barycenter problem, which Scetbon et al. (2021) adapted to solve the primal low-rank OT problem. We derive an alternative parameterization of the low-rank problem based on the latent coupling (LC) factorization previously introduced by Lin et al. (2021) generalizing Forrow et al. (2019). The LC factorization has multiple advantages for low-rank OT including decoupling the problem into three OT problems and greater flexibility and interpretability. We leverage these advantages to derive a new algorithm Factor Relaxation with Latent Coupling (FRLC), which uses coordinate mirror descent to compute the LC factorization. FRLC handles multiple OT objectives (Wasserstein, Gromov-Wasserstein, Fused Gromov-Wasserstein), and marginal constraints (balanced, unbalanced, and semi-relaxed) with linear space complexity. We provide theoretical results on FRLC, and demonstrate superior performance on diverse applications - including graph clustering and spatial transcriptomics - while demonstrating its interpretability. © 2024 Neural information processing systems foundation. All rights reserved.

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