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• Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
  From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, …
  arxiv Keith Feldman, Louis Faust, Xian Wu, Chao Huang 等 2017-06-01T20:34:41Z 置信度 0.78 cs.CYcs.LGstat.ML
• From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
  Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can…
  arxiv Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau 等 2026-02-04T18:50:10Z 置信度 0.78 cs.LGcond-mat.mtrl-scics.AIphysics.chem-ph
• Reproducibility in Machine Learning for Health
  Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attentio…
  arxiv Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath 等 2019-07-02T15:46:46Z 置信度 0.78 cs.LGcs.CYstat.ML
• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black bo…
  arxiv Cynthia Rudin 2018-11-26T03:00:25Z 置信度 0.78 stat.MLcs.LG
• A Framework for Implementing Machine Learning on Omics Data
  The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These d…
  arxiv Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter, Hannah Thompson 等 2018-11-26T15:35:57Z 置信度 0.78 cs.LGcs.AIq-bio.GNstat.ML
• Explanatory machine learning for sequential human teaching
  The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories …
  arxiv Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid 2022-05-20T15:23:46Z 置信度 0.78 cs.AIcs.LG
• The Scientific Method in the Science of Machine Learning
  In the quest to align deep learning with the sciences to address calls for rigor, safety, and interpretability in machine learning systems, this contribution identifies key missing pieces: the stages of hypothesis formulation and testing, as well as statistica…
  arxiv Jessica Zosa Forde, Michela Paganini 2019-04-24T17:01:43Z 置信度 0.78 cs.LGstat.ML
• Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction
  As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a …
  arxiv Josie Williams, Narges Razavian 2019-11-18T15:04:31Z 置信度 0.78 cs.LGstat.APstat.ML
• ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
  The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experi…
  arxiv Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov 等 2018-07-14T08:07:31Z 置信度 0.78 cs.LGcs.DBcs.IRstat.ML
• Teaching Uncertainty Quantification in Machine Learning through Use Cases
  Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use…
  arxiv Matias Valdenegro-Toro 2021-08-19T14:22:17Z 置信度 0.78 cs.LGstat.ML
• Multimodal Machine Learning for Automated ICD Coding
  This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular d…
  arxiv Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao 等 2018-10-31T15:39:32Z 置信度 0.78 cs.LGstat.ML
• On-Device Machine Learning: An Algorithms and Learning Theory Perspective
  The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in pe…
  arxiv Sauptik Dhar, Junyao Guo, Jiayi Liu, Samarth Tripathi 等 2019-11-02T01:16:02Z 置信度 0.78 cs.LGcs.DCstat.ML
• Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
  We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models t…
  arxiv Y. Sultan Abylkairov, Matthew C. Edwards, Daniil Orel, Ayan Mitra 等 2024-09-22T16:11:25Z 置信度 0.78 astro-ph.HEgr-qc
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
  Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-…
  arxiv Sung Whan Yoon, Jun Seo, Jaekyun Moon 2019-05-16T06:21:28Z 置信度 0.78 cs.LGstat.ML
• Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
  This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
  arxiv Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen 等 2018-11-17T20:14:43Z 置信度 0.78 cs.LGstat.ML
• Bayesian Differential Privacy for Machine Learning
  Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often ex…
  arxiv Aleksei Triastcyn, Boi Faltings 2019-01-28T14:37:17Z 置信度 0.78 cs.LGcs.CRstat.ML
• Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
  Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of da…
  arxiv Sebastian Raschka, Joshua Patterson, Corey Nolet 2020-02-12T05:20:59Z 置信度 0.78 cs.LGstat.ML
• Isoelastic Agents and Wealth Updates in Machine Learning Markets
  Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with …
  arxiv Amos Storkey, Jono Millin, Krzysztof Geras 2012-06-27T19:59:59Z 置信度 0.78 cs.LGcs.GTstat.ML
• A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design
  Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timeline…
  arxiv Jingshu Liu, Patricia J Allen, Luke Benz, Daniel Blickstein 等 2021-11-14T18:24:11Z 置信度 0.78 cs.LGstat.APstat.ML
• Law and Adversarial Machine Learning
  When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with…
  arxiv Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen 2018-10-25T06:17:34Z 置信度 0.78 cs.LGcs.CRcs.CYstat.ML
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
  Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners…
  arxiv Stefan Studer, Thanh Binh Bui, Christian Drescher, Alexander Hanuschkin 等 2020-03-11T08:25:49Z 置信度 0.78 cs.LGcs.SEstat.ML
• SmartChoices: Hybridizing Programming and Machine Learning
  We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to buildin…
  arxiv Victor Carbune, Thierry Coppey, Alexander Daryin, Thomas Deselaers 等 2018-10-01T11:14:22Z 置信度 0.78 cs.LGcs.PLstat.ML
• An Inertial Newton Algorithm for Deep Learning
  We introduce a new second-order inertial optimization method for machine learning called INNA. It exploits the geometry of the loss function while only requiring stochastic approximations of the function values and the generalized gradients. This makes INNA fu…
  arxiv Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels 2019-05-29T09:00:49Z 置信度 0.78 cs.LGmath.OCstat.ML
• Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
  Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal lay…
  arxiv Haoyu Yang, Wen Chen, Piyush Pathak, Frank Gennari 等 2019-12-12T06:52:12Z 置信度 0.78 cs.LGcs.AIstat.ML
• Asymptotics of Network Embeddings Learned via Subsampling
  Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for v…
  arxiv Andrew Davison, Morgane Austern 2021-07-06T02:54:53Z 置信度 0.78 stat.MLcs.LGmath.ST
• U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
  Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learni…
  arxiv Jiaee Cheong, Aditya Bangar, Sinan Kalkan, Hatice Gunes 2025-01-16T17:39:25Z 置信度 0.78 cs.LG
• Alpha MAML: Adaptive Model-Agnostic Meta-Learning
  Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classificati…
  arxiv Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr 2019-05-17T18:45:25Z 置信度 0.78 cs.LGcs.AIstat.ML
• Unfairness towards subjective opinions in Machine Learning
  Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and…
  arxiv Agathe Balayn, Alessandro Bozzon, Zoltan Szlavik 2019-11-06T16:11:41Z 置信度 0.78 cs.LGcs.CYcs.HCstat.ML
• InfoGram and Admissible Machine Learning
  We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transp…
  arxiv Subhadeep Mukhopadhyay 2021-08-17T00:04:38Z 置信度 0.78 stat.MLcs.AIcs.LGecon.EM
• ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
  Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-…
  arxiv Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov 2018-11-11T09:45:41Z 置信度 0.78 stat.MLcs.LG
• Model-Agnostic Interpretability of Machine Learning
  Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, in…
  arxiv Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin 2016-06-16T23:39:41Z 置信度 0.78 stat.MLcs.LG
• Transfer Learning with Pre-trained Conditional Generative Models
  Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network arc…
  arxiv Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa 等 2022-04-27T10:36:32Z 置信度 0.78 cs.LGcs.AIstat.ML
• Learning Generative Models across Incomparable Spaces
  Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), wh…
  arxiv Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka 2019-05-14T08:56:12Z 置信度 0.78 cs.LGstat.ML
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
  Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a…
  arxiv Li Yang, Abdallah Shami 2020-07-30T21:11:01Z 置信度 0.78 cs.LGstat.ML
• Is machine learning good or bad for the natural sciences?
  Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data.…
  arxiv David W. Hogg, Soledad Villar 2024-05-28T12:01:52Z 置信度 0.78 stat.MLastro-ph.IMcs.LGphysics.data-an
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
  Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architectu…
  arxiv Esteban Real, Chen Liang, David R. So, Quoc V. Le 2020-03-06T19:00:04Z 置信度 0.78 cs.LGcs.NEstat.ML
• The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
  No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in …
  arxiv Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson 2023-04-11T17:22:22Z 置信度 0.78 cs.LGstat.ML
• Federated Learning with Superquantile Aggregation for Heterogeneous Data
  We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail stat…
  arxiv Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaid Harchaoui 2021-12-17T11:00:23Z 置信度 0.78 cs.LGmath.OCstat.ML
• DriveML: An R Package for Driverless Machine Learning
  In recent years, the concept of automated machine learning has become very popular. Automated Machine Learning (AutoML) mainly refers to the automated methods for model selection and hyper-parameter optimization of various algorithms such as random forests, gr…
  arxiv Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli 2020-05-01T16:40:25Z 置信度 0.78 cs.LGstat.ML
• Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
  Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster conv…
  arxiv Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya 2018-12-26T17:33:43Z 置信度 0.78 cs.LGstat.ML
• Migration through Machine Learning Lens -- Predicting Sexual and Reproductive Health Vulnerability of Young Migrants
  In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration …
  arxiv Amber Nigam, Pragati Jaiswal, Uma Girkar, Teertha Arora 等 2019-10-06T07:09:13Z 置信度 0.78 cs.LGcs.CYstat.ML
• Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
  Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization fo…
  arxiv Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis 2022-09-30T19:33:53Z 置信度 0.78 cs.LGcs.CR
• Automated Machine Learning for Unsupervised Tabular Tasks
  In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our in…
  arxiv Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim, Murat Onur Yildirim 等 2025-10-08T21:31:22Z 置信度 0.78 cs.LG
• One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
  The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these…
  arxiv Kacper Sokol, Peter Flach 2020-01-27T13:10:12Z 置信度 0.78 cs.LGcs.AIstat.ML
• Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
  As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important. It is often argued that accuracy or other similar generalization performance m…
  arxiv Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang 等 2018-06-25T21:37:21Z 置信度 0.78 stat.MLcs.ITcs.LG
• Learning Flat Latent Manifolds with VAEs
  Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space. Prevalent …
  arxiv Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer 等 2020-02-12T09:54:52Z 置信度 0.78 stat.MLcs.LG
• Proceedings of the 29th International Conference on Machine Learning (ICML-12)
  This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12). The conference was held in Edinburgh, Scotland, June 27th - July 3rd, 2012.
  arxiv John Langford, Joelle Pineau 2012-07-19T14:08:22Z 置信度 0.78 cs.LGstat.ML
• ChainerRL: A Deep Reinforcement Learning Library
  In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art re…
  arxiv Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa 2019-12-09T08:59:15Z 置信度 0.78 cs.LGcs.AIstat.ML
• Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
  Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but the mod…
  arxiv Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré 2019-09-27T02:42:58Z 置信度 0.78 cs.LGstat.ML
• Estimation of Corporate Greenhouse Gas Emissions via Machine Learning
  As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For t…
  arxiv You Han, Achintya Gopal, Liwen Ouyang, Aaron Key 2021-09-09T14:50:26Z 置信度 0.78 cs.LGstat.ML
• Machine Learning for a Low-cost Air Pollution Network
  Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially a…
  arxiv Michael T. Smith, Joel Ssematimba, Mauricio A. Alvarez, Engineer Bainomugisha 2019-11-28T21:32:59Z 置信度 0.78 cs.LGstat.APstat.ML
• Operator-valued Kernels for Learning from Functional Response Data
  In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel H…
  arxiv Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu 等 2015-10-28T09:18:50Z 置信度 0.78 cs.LGstat.ML
• A Tutorial on Meta-Reinforcement Learning
  While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach …
  arxiv Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong 等 2023-01-19T12:01:41Z 置信度 0.78 cs.LG
• Graph topology inference benchmarks for machine learning
  Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: I) clustering of vertices, II) semi-supervised classification of vertices, …
  arxiv Carlos Lassance, Vincent Gripon, Gonzalo Mateos 2020-07-16T09:40:32Z 置信度 0.78 cs.LGstat.ML
• Lale: Consistent Automated Machine Learning
  Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent…
  arxiv Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram 等 2020-07-04T00:55:41Z 置信度 0.78 cs.LGcs.AI
• Regression Networks for Meta-Learning Few-Shot Classification
  We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction o…
  arxiv Arnout Devos, Matthias Grossglauser 2019-05-31T13:35:41Z 置信度 0.78 cs.LGcs.CVstat.ML
• Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
  It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the me…
  arxiv Marco Puts, David Salgado, Piet Daas 2024-09-06T15:57:25Z 置信度 0.78 stat.MLcs.LGstat.ME
• A Perspective on Symbolic Machine Learning in Physical Sciences
  Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpret…
  arxiv Nour Makke, Sanjay Chawla 2025-02-25T09:02:02Z 置信度 0.78 cs.LGhep-phhep-th
• Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
  We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding re…
  arxiv Shahriar Noroozizadeh, Jeremy C. Weiss, George H. Chen 2023-12-10T16:43:15Z 置信度 0.78 cs.LGcs.AIstat.ML
• Machine Learning: The Basics
  Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engine…
  arxiv Alexander Jung 2018-05-14T08:08:33Z 置信度 0.78 cs.LGstat.ML
• Assisted Learning for Organizations with Limited Imbalanced Data
  In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In thi…
  arxiv Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou 2021-09-20T05:57:52Z 置信度 0.78 cs.LG
• FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
  Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data fro…
  arxiv Dianbo Liu, Timothy Miller, Raheel Sayeed, Kenneth D. Mandl 2018-11-28T06:06:38Z 置信度 0.78 cs.CYcs.LG
• Relevance As a Metric for Evaluating Machine Learning Algorithms
  In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. …
  arxiv Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta, Johan J. Lukkien 2013-03-28T11:01:53Z 置信度 0.78 stat.MLcs.LG
• Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence
  Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients …
  arxiv Thomas Janssoone, Clémence Bic, Dorra Kanoun, Pierre Hornus 等 2018-11-29T15:08:55Z 置信度 0.78 cs.LGcs.AIstat.ML
• What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
  Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that simple approximations, such as linear models or decision-trees, are inherently huma…
  arxiv Owen Lahav, Nicholas Mastronarde, Mihaela van der Schaar 2018-11-27T04:26:36Z 置信度 0.78 cs.LGstat.ML
• Predicting ice flow using machine learning
  Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniq…
  arxiv Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio 2019-10-20T07:56:18Z 置信度 0.78 cs.LGphysics.ao-phphysics.geo-phstat.ML
• The State of Julia for Scientific Machine Learning
  Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and la…
  arxiv Edward Berman, Jacob Ginesin 2024-10-14T01:43:23Z 置信度 0.78 cs.LGcs.MScs.PL
• Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
  Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential domain for multi-mod…
  arxiv Francisco Mena, Dino Ienco, Cassio F. Dantas, Roberto Interdonato 等 2025-10-22T13:29:32Z 置信度 0.78 cs.CVcs.AIcs.LG
• Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
  We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particular…
  arxiv Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou 等 2024-10-30T20:46:26Z 置信度 0.78 cs.LGcs.AIcs.ROstat.ML
• Impact of Legal Requirements on Explainability in Machine Learning
  The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how…
  arxiv Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay 2020-07-10T16:57:18Z 置信度 0.78 cs.AIcs.CYcs.LG
• Hyperparameter Optimization in Machine Learning
  Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these techn…
  arxiv Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein 等 2024-10-30T09:39:22Z 置信度 0.78 stat.MLcs.LG
• Physics-driven machine learning models coupling PyTorch and Firedrake
  Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in many realistic applications PDE modelling provides an incomplete description of th…
  arxiv Nacime Bouziani, David A. Ham 2023-03-13T05:42:58Z 置信度 0.78 cs.LGcs.MSmath.NAphysics.comp-ph
• Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare
  The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investiga…
  arxiv Mark Sendak, Gaurav Sirdeshmukh, Timothy Ochoa, Hayley Premo 等 2022-08-04T13:58:36Z 置信度 0.78 stat.MLcs.LG
• P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
  Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can limit its practicality. Alternative techniques, such as Monte Carlo dropout and d…
  arxiv Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi, Yong-Bin Zhuang 等 2025-09-03T01:36:27Z 置信度 0.78 cs.LGcond-mat.mtrl-sci
• Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
  Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a stu…
  arxiv Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu 等 2020-07-01T15:00:43Z 置信度 0.78 cs.LGcs.CVstat.ML
• Multi-Fidelity Active Learning with GFlowNets
  In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, …
  arxiv Alex Hernandez-Garcia, Nikita Saxena, Moksh Jain, Cheng-Hao Liu 等 2023-06-20T17:43:42Z 置信度 0.78 cs.LGq-bio.BM
• Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
  We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generat…
  arxiv Truyen Tran, Dinh Phung, Svetha Venkatesh 2014-08-01T00:32:32Z 置信度 0.78 stat.MLcs.LGstat.ME
• Cyclic Boosting -- an explainable supervised machine learning algorithm
  Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the p…
  arxiv Felix Wick, Ulrich Kerzel, Michael Feindt 2020-02-09T18:52:42Z 置信度 0.78 cs.LGstat.ML
• Interpretable Machine Learning Models for the Digital Clock Drawing Test
  The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position wi…
  arxiv William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au 等 2016-06-23T02:08:58Z 置信度 0.78 stat.MLcs.LG
• The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
  We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve sparse regression problems with $10^7$ features in minutes and $10^8$ features i…
  arxiv Dimitris Bertsimas, Vassilis Digalakis 2020-06-11T16:43:02Z 置信度 0.78 cs.LGstat.ML
• Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
  Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive …
  arxiv Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann 等 2018-11-30T02:30:10Z 置信度 0.78 cs.LGstat.ML
• Analysis, Identification and Prediction of Parkinson Disease Sub-Types and Progression through Machine Learning
  This paper represents a groundbreaking advancement in Parkinson disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression. Utilizing a comprehensive dataset encompassing both clini…
  arxiv Ashwin Ram 2023-06-07T19:54:56Z 置信度 0.78 cs.LG
• Using Visual Analytics to Interpret Predictive Machine Learning Models
  It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to und…
  arxiv Josua Krause, Adam Perer, Enrico Bertini 2016-06-17T21:56:43Z 置信度 0.78 stat.MLcs.LG
• Thermosalient Phase Transitions from Machine Learning Interatomic Potential
  We developed an accurate machine learning interatomic potential for the thermosalient molecular crystal N-2-propylidene-4-hydroxybenzohydrazide. This crystal exhibits one of the largest mechanical responses during its thermosalient phase transition. Leveraging…
  crossref 2024-10-07T15:20:19Z 置信度 0.70
• Transferable Machine Learning Interatomic Potential for Carbon Hydrogen Systems
  In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) c…
  crossref Somayeh Faraji, Mingjie Liu 2024-06-07T08:33:49Z 置信度 0.70
• Machine Learning Interatomic Potential for Atmospheric Chemistry
  Aerosols consist of solid or liquid particulate matter suspended in the atmosphere, varying in chemical composition and dimension. They play crucial roles in Earth’s climate system by affecting radiative forcing, cloud formation, and air quality, for…
  crossref Lucas Bandeira, Hilda Sandström, Patrick Rinke 2025-03-15T03:19:02Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Machine learning interatomic potential enhanced thermochemistry predictions across organic compound space
  This work presents thermoMLIP, a machine-learning-interatomic-potential-based workflow for efficient conformer optimization and thermochemistry prediction across organic molecules. By combining MLIP-driven geometry optimization with robust thermochemical corre…
  crossref Bowen Deng, Thijs Stuyver 2026-05-12T07:13:26Z 置信度 0.70
• DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning
  crossref 2020-11-12T17:05:10Z 置信度 0.70
• Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations
  Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulations with accuracies akin to first-princip…
  crossref 2024-12-18T07:10:22Z 置信度 0.70
• Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential
  Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with data sets from on-the-fly active learni…
  crossref 2024-02-23T21:21:05Z 置信度 0.70
• Supplementary Material
  crossref 2025-12-18T08:41:02Z 置信度 0.70
• Energetically Anchored Machine-Learning Interatomic Potential Embeddings for Reliable Hydrogen Evolution Electrocatalyst Prediction
  crossref 2026-07-23T14:20:19Z 置信度 0.70
• Modeling Equilibrium SolidLiquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials
  The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a t…
  crossref 2025-11-20T17:40:32Z 置信度 0.70
• Decision letter for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Estimating Potential-Dependent Physicochemical Properties at MetalElectrolyte Interfaces Using Machine Learning Interatomic Potentials
  crossref 2026-04-13T10:15:19Z 置信度 0.70
• Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
  crossref 2023-12-18T08:20:24Z 置信度 0.70
• Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing StressStrain and Vibrational Properties
  Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and ab initio methods. In this work, we present a reactiv…
  crossref 2025-07-12T13:20:21Z 置信度 0.70
• Machine Learning Interatomic Potential for Modeling the Mechanical and Thermal Properties of Naphthyl-Based Nanotubes
  Two-dimensional (2D) nanomaterials are at the forefront of potential technological advancements. Carbon-based materials have been extensively studied since synthesizing graphene, which revealed properties of great interest for novel applications across diverse…
  crossref 2025-01-28T10:49:58Z 置信度 0.70
• A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
  crossref 2026-07-20T14:30:43Z 置信度 0.70