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arXiv 2024年

作者： Fornasier, Massimo Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany King Abdullah University of Science and Technology Thuwal Saudi Arabia KAUST AI Initiative Thuwal Saudi Arabia

Consensus-based optimization (CBO) is an agent-based derivative-free method for non-smooth global optimization that has been introduced in 2017 [40], leveraging a surprising interplay between stochastic exploration and Laplace’s principle. In addition to its versatility and effectiveness in handling high-dimensional, non-convex, and non-smooth optimization problems, this approach lends itself well to theoretical analysis. Indeed, its dynamics is governed by a degenerate nonlinear Fokker–Planck equation, whose large time behavior explains the convergence of the method. Recent results [10, 23] provide guarantees of convergence under the restrictive assumption of unique global minimizer for the objective function. In this work, we propose a novel and simple variation of CBO to tackle non-convex optimization problems with multiple global minimizers. Despite the simplicity of this new model, its analysis is particularly challenging because of its nonlinearity and nonlocal nature. We prove existence of solutions of the corresponding nonlinear Fokker–Planck equation and we show exponential concentration in time to the set of minimizers made of multiple smooth, convex, and compact components. Our proofs require combining several ingredients, such as delicate geometrical arguments, new variants of a quantitative Laplace principle, ad hoc regularizations and approximations, and regularity theory for parabolic equations. Ultimately, this result suggests that the corresponding CBO algorithm, formulated as an Euler–Maruyama discretization of the underlying empirical stochastic process, tends to converge to multiple global minimizers. © 2024, CC BY.

关键词： Fokker Planck equation

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DOES AN ENSEMBLE OF GANS LEAD TO BETTER PERFORMANCE WHEN TRAINING SEGMENTATION NETWORKS WITH SYNTHETIC IMAGES?

arXiv

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arXiv 2022年

作者： Larsson, Måns Akbar, Muhammad Usman Eklund, Anders Eigenvision Malmö Sweden Division of Medical Informatics Department of Biomedical Engineering Sweden Division of Statistics & Machine Learning Department of Computer and Information Science Sweden Linköping University Sweden

Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 20 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7% to 14.0% on specific classes. © 2022, CC BY.

关键词： Medical imaging

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Regression with comparisons: escaping the curse of dimensionality with ordinal information

The Journal of Machine Learning Research

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The Journal of machine learning Research 2020年第1期21卷 6480-6533页

作者： Yichong Xu Sivaraman Balakrishnan Aarti Singh Artur Dubrawski Machine Learning Department Department of Statistics and Data Science Machine Learning Department Auton Lab The Robotics Institute Carnegie Mellon University Pittsburgh PA

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alternative feedback. In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples. We consider ordinal feedback of varying qualities where we have either a perfect ordering of the samples, a noisy ordering of the samples or noisy pairwise comparisons between the samples. We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an underlying function with a very small labeled set, effectively escaping the curse of dimensionality. We also present lower bounds, that establish fundamental limits for the task and show that our algorithms are optimal in a variety of settings. Finally, we present extensive experiments on new datasets that demonstrate the efficacy and practicality of our algorithms and investigate their robustness to various sources of noise and model misspecification.

关键词： pairwise comparison ranking regression interactive learning

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BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation

arXiv

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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Hu, Miao Wu, Di Liao, Pengshan Guizani, Mohsen Sheng, Michael The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

arXiv

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arXiv 2024年

作者： Esders, Malte Schnake, Thomas Lederer, Jonas Kabylda, Adil Montavon, Grégoire Tkatchenko, Alexandre Müller, Klaus-Robert BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Machine Learning Group Berlin Institute of Technology Berlin10587 Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Department of Mathematics and Computer Science Free University of Berlin Germany Google Deepmind Berlin Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method

Predicting House Price Model : A Comprehensive Analysis with...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Mansi Sharma Deepak Sharma Rushikesh Burle Pawan Patil Isha Joge Chetan Puri Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Commerce & Management Sciences Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the *** dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset.

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Dynamic Channel Allocation Using Reinforcement learning Algorithm for Multiple Input Multiple Output Systems

Dynamic Channel Allocation Using Reinforcement Learning Algo...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Srinivasa Rao Bittla Raami Riadhusin Divyaraj G N S. Aravindh Ahila B Independent Researcher Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, Multiple-Input Multiple-Output systems (MIMO) play a crucial role in modern wireless networks by enhancing spectral efficiency and data rates. Traditional static, heuristic-based allocation methods struggled to adapt to real-time variations, handling high-dimensional state spaces, minimizing interference, and ensuring fairness in channel allocation. However, decision-making in dynamic environments requires intelligent and scalable approach that enables real-time decision-making by efficiently learning and adapting to changing network conditions. To overcome these issues. This research proposed Deep Q-Network with Prioritized Experience Replay (DQN-PER) for dynamic channel allocation for MIMO Systems. DQN-PER prioritizes high-value learning experiences and while also improving training efficiency and convergence speed. PER prioritizes important experiences based on Temporal-Difference (TD) error by allowing the model to focus on more informative transitions, which speeds up learning and improves adaptability. Simulation results demonstrate enhanced spectral efficiency, reduced packet loss, and improved Quality of Service (QoS) in dynamic wireless environments. These findings establish DQN-PER as a promising solution for intelligent and adaptive MIMO resource allocation. The proposed method achieved better results in terms of a packet drop rate 0.04 compared to existing method Advantage Actor-Critic (A2C).

关键词： Spectral efficiency Heuristic algorithms Wireless networks Decision making Quality of service Dynamic scheduling Real-time systems Resource management Optimization Convergence

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Meta Representation learning with Contextual Linear Bandits

arXiv

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arXiv 2022年

作者： Cella, Leonardo Lounici, Karim Pontil, Massimiliano Computational Statistics and Machine Learning Italian Institute of Technology Italy CMAP Ecole Polytechnique France Department of Computer Science University College London United Kingdom

Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience. In this paper we investigate meta-learning in the setting of stochastic linear bandit tasks. We assume that the tasks share a low dimensional representation, which has been partially acquired from previous learning tasks. We aim to leverage this information in order to learn a new downstream bandit task, which shares the same representation. Our principal contribution is to show that if the learned representation estimates well the unknown one, then the downstream task can be efficiently learned by a greedy policy that we propose in this work. We derive_an upper bound on the regret of this policy, which is, up to logarithmic factors, of order r√N(1 ∨ pd/T), where N is the horizon of the downstream task, T is the number of training tasks, d the ambient dimension and r d the dimension of the representation. We highlight that our strategy does not need to know r. We note that if T > d our bound achieves the same rate of optimal minimax bandit algorithms using the true underlying representation. Our analysis is inspired and builds in part upon previous work on meta-learning in the i.i.d. full information setting [Tripuraneni et al., 2021, Boursier et al., 2022]. As a separate contribution we show how to relax certain assumptions in those works, thereby improving their representation learning and risk analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Risk analysis

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Role of machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Zayd Balassem Ramya Srikanteswara Esakki Madura E Madugula Anjaneyulu M Devi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed_established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM.

关键词： Support vector machines Accuracy Neural networks Noise Power system stability Logic gates Feature extraction Smart grids data mining Convolutional neural networks

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