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arXiv 2023年

作者： Chugg, Ben Wang, Hongjian Ramdas, Aaditya Machine Learning Department United States Department of Statistics and Data Science Carnegie Mellon University United States

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike most previous literature on this topic, our bounds are anytime-valid (i.e., time-uniform), meaning that they hold at all stopping times, not only for a fixed sample size. Our approach combines four tools in the following order: (a) nonnegative supermartingales or reverse submartingales, (b) the method of mixtures, (c) the Donsker-Varadhan formula (or other convex duality principles), and (d) Ville's inequality. Our main result is a PAC-Bayes theorem which holds for a wide class of discrete stochastic processes. We show how this result implies time-uniform versions of wellknown classical PAC-Bayes bounds, such as those of Seeger, McAllester, Maurer, and Catoni, in addition to many recent bounds. We also present several novel bounds. Our framework also enables us to relax traditional assumptions;in particular, we consider nonstationary loss functions and non-i.i.d. data. In sum, we unify the derivation of past bounds and ease the search for future bounds: one may simply check if our supermartingale or submartingale conditions are met and, if so, be guaranteed a (time-uniform) PAC-Bayes bound. Copyright © 2023, The Authors. All rights reserved.

关键词： Random processes

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Time-Uniform Confidence Spheres for Means of Random Vectors

arXiv

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arXiv 2023年

作者： Chugg, Ben Wang, Hongjian Ramdas, Aaditya Machine Learning Department United States Department of Statistics and Data Science Carnegie Mellon University United States

We study sequential mean estimation in d. In particular, we derive time-uniform confidence spheres—confidence sphere sequences (CSSs)—which contain the mean of random vectors with high probability simultaneously across all sample sizes. Our results include a dimension-free CSS for log-concave random vectors, a dimension-free CSS for sub-Gaussian random vectors, and CSSs for sub-ψ random vectors (which includes sub-gamma, sub-Poisson, and sub-exponential distributions). Many of our results are optimal. For sub-Gaussian distributions we also provide a CSS which tracks a time-varying mean, generalizing Robbins’ mixture approach to the multivariate setting. Finally, we provide several CSSs for heavy-tailed random vectors (two moments only). Our bounds hold under a martingale assumption on the mean and do not require that the observations be iid. Our work is based on PAC-Bayesian theory and inspired by an approach of Catoni and Giulini. © 2023, CC BY.

关键词： Spheres

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed for Secure Sub-6 GHz 5G Applications

A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed fo...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Aser M. Matarneh Omar R. Daoud Electrical and Computer Engineering American International University Aljahra Kuwait Data Science and Machine Learning Department Pioneers International Academy Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This work presents a broadband and low-profile printed Inverted-F antenna (PIFA). The design deploys a 1.5 mm thick FR-4 substrate with dimensions ( $\mathbf{4 0} \times \mathbf{2 5} \mathbf{ m m}^{2}$ ). A broadband operation is achieved through modifying a coplanar waveguide feed (CPW). Two $\lambda / 8$ stubs are created to enhance the entire operational frequency bandwidth. The simulation result of the reflection parameter achieves about 87 % fractional bandwidth ( $2.05-5.2 \text{GHz}$ ) based on the $\mathbf{- 1 0} \mathbf{d B}$ impedance bandwidth criterion. The design has total radiation efficiency above $\mathbf{7 2 \%}$ . Far-field radiation patterns results show a dualpattern (pattern diversity) among excited resonance modes frequencies. Such results make the proposed antenna an excellent candidate for current secure- and selective sub-6 GHz 5G communications systems.

关键词： 5G mobile communication Resonant frequency Bandwidth Directive antennas Resonance Broadband communication Coplanar waveguides Broadband antennas Impedance Antenna radiation patterns

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EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation

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National science Review 2024年第7期11卷 135-147页

作者： Yue Zhao Wei Zhang Tiejun Li Center for Data Science Peking University Zuse Institute Berlin Department of Mathematics and Computer Science Freie Universit?t Berlin Laboratory of Mathematics and Applied Mathematics(LMAM)and School of Mathematical Sciences Peking University Center for Machine Learning Research Peking University

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.

关键词： high-dimensional potential landscape non-equilibrium system entropy production rate dimensionality reduction deep learning

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Counterfactually comparing abstaining classifiers 23

Counterfactually comparing abstaining classifiers

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yo Joong Choe Aditya Gangrade Aaditya Ramdas Data Science Institute University of Chicago Department of EECS University of Michigan Dept. of Statistics and Data Science Machine Learning Department Carnegie Mellon University

Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes decision-making problems, as they can withhold uncertain predictions to improve their reliability and safety. When evaluating black-box abstaining classifier(s), however, we lack a principled approach that accounts for what the classifier would have predicted on its abstentions. These missing predictions matter when they can eventually be utilized, either directly or as a backup option in a failure mode. In this paper, we introduce a novel approach and perspective to the problem of evaluating and comparing abstaining classifiers by treating abstentions as missing data. Our evaluation approach is centered around defining the counterfactual score of an abstaining classifier, defined as the expected performance of the classifier had it not been allowed to abstain. We specify the conditions under which the counterfactual score is identifiable: if the abstentions are stochastic, and if the evaluation data is independent of the training data (ensuring that the predictions are missing at random), then the score is identifiable. Note that, if abstentions are deterministic, then the score is unidentifiable because the classifier can perform arbitrarily poorly on its abstentions. Leveraging tools from observational causal inference, we then develop nonparametric and doubly robust methods to efficiently estimate this quantity under identification. Our approach is examined in both simulated and real data experiments.

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An efficient doubly-robust test for the kernel treatment effect 23

An efficient doubly-robust test for the kernel treatment eff...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Diego Martinez-Taboada Aaditya Ramdas Edward H. Kennedy Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University Pittsburgh PA

The average treatment effect, which is the difference in expectation of the counter-factuals, 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.

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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A Review on the Applications of machine learning Algorithms for Mental Stress Detection 2

A Review on the Applications of Machine Learning Algorithms ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Motghare, Renuka Bhoyar, Shivam Verma, Prateek Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes © 2024 IEEE.

关键词： Adversarial machine learning

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Bringing closure to FDR control: beating the e-Benjamini-Hochberg procedure

arXiv

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arXiv 2025年

作者： Xu, Ziyu Fischer, Lasse Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Competence Center for Clinical Trials Bremen University of Bremen Germany Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States

False discovery rate (FDR) has been a key metric for error control in multiple hypothesis testing, and many methods have developed for FDR control across a diverse cross-section of settings and applications. We develop a closure principle for all FDR controlling procedures, i.e., we provide a characterization based on e-values for all admissible FDR controlling procedures. A general version of this closure principle can recover any multiple testing error metric and allows one to choose the error metric post-hoc. We leverage this idea to formulate the closed eBH procedure, a (usually strict) improvement over the eBH procedure for FDR control when provided with e-values. This also yields a closed BY (BY) procedure that dominates the Benjamini-Yekutieli (BY) procedure for FDR control with arbitrarily dependent p-values, thus proving that the latter is inadmissibile. We demonstrate the practical performance of our new procedures in simulations. Copyright © 2025, The Authors. All rights reserved.

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