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statistical machine learning

GeneTech 知识引擎共收录 356 篇相关科研实体，横跨 23 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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代表性文献（按质量分排序，前 60 条）

• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
  2026 · AI 安全与对齐 · The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. T…
• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
  2026 · AI 驱动科学发现 · The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. T…
• DICE: Data Influence Cascade in Decentralized Learning
  2025 · AI 安全与对齐 · Decentralized learning offers a promising approach to crowdsource data consumptions and computational workloads across geographically distributed compute interconnected through peer-to-peer networks, accommodating the ex…
• The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
  2025 · AI 驱动科学发现 · This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MP…
• DICE: Data Influence Cascade in Decentralized Learning
  2025 · 脑科学 · Decentralized learning offers a promising approach to crowdsource data consumptions and computational workloads across geographically distributed compute interconnected through peer-to-peer networks, accommodating the ex…
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing
  2025 · 量子材料 · Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we introduce TensorHyper-VQC, a novel tensor-tra…
• Activation degree thresholds and expressiveness of polynomial neural networks
  2024 · AI 安全与对齐 · We study the expressive power of deep polynomial neural networks through the geometry of their neurovariety. We introduce the notion of the activation degree threshold of a network architecture to express when the dimens…
• Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
  2024 · AI 驱动科学发现 · 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-conditione…
• Sequential Best-Arm Identification with Application to Brain-Computer Interface
  2023 · 仿生智能 · A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. It allows individuals to interact with the device using only their thoughts,…
• Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
  2023 · 仿生智能 · Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these tw…
• Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
  2023 · 脑科学 · Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these tw…
• Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
  2023 · 低空经济 · As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based modeling. While the Bayesian approaches prev…
• Quantum Algorithms for the Pathwise Lasso
  2023 · 量子计算 · We present a novel quantum high-dimensional linear regression algorithm with an $\ell_1$-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm. Similarly to available classical algorithms for La…
• Statistical Complexity of Quantum Learning
  2023 · 量子材料 · Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing. As in classical learnin…
• Position: agentic AI orchestration should be Bayes-consistent
  2026 · AI Agent 生态 · LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. …
• Adaptive Querying with AI Persona Priors
  2026 · AI 安全与对齐 · We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets. Classical Bayesian design and computerized adapt…
• Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning
  2026 · AI 安全与对齐 · Differentiable optimization layers are traditionally integrated in predict-then-optimize frameworks where a neural model estimates parameters that subsequently serve as fixed inputs to downstream decision-making optimiza…
• Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
  2026 · AI 驱动科学发现 · Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support for calibrated predictive uncertainty. We…
• Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
  2026 · 低空经济 · In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), whereby we look to learn a posterior distr…
• Self-Supervised Learning by Curvature Alignment
  2025 · AI 安全与对齐 · Self-supervised learning (SSL) has recently advanced through non-contrastive methods that couple an invariance term with variance, covariance, or redundancy-reduction penalties. While such objectives shape first- and sec…
• Composition and Alignment of Diffusion Models using Constrained Learning
  2025 · AI 安全与对齐 · Diffusion models have become prevalent in generative modeling due to their ability to sample from complex distributions. To improve the quality of generated samples and their compliance with user requirements, two common…
• Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders
  2025 · AI 安全与对齐 · Manifold alignment (MA) involves a set of techniques for learning shared representations across domains, yet many traditional MA methods are incapable of performing out-of-sample extension, limiting their real-world appl…
• Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma
  2025 · AI 安全与对齐 · Reinforcement Learning from Human Feedback (RLHF) is widely used for aligning large language models, yet practitioners face a persistent puzzle: improving safety often reduces fairness, scaling to diverse populations bec…
• Silenced Biases: The Dark Side LLMs Learned to Refuse
  2025 · AI 安全与对齐 · Safety-aligned large language models (LLMs) are becoming increasingly widespread, especially in sensitive applications where fairness is essential and biased outputs can cause significant harm. However, evaluating the fa…
• Towards a Learning Theory of Representation Alignment
  2025 · AI 安全与对齐 · It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment o…
• Computing frustration and near-monotonicity in deep neural networks
  2025 · AI 安全与对齐 · For the signed graph associated to a deep neural network, one can compute the frustration level, i.e., test how close or distant the graph is to structural balance. For all the pretrained deep convolutional neural networ…
• Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling
  2025 · 地外矿物 · Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challenging. Inspired by recent progress in gener…
• OceanForecastBench: A Benchmark Dataset for Data-Driven Global Ocean Forecasting
  2025 · 深海科技 · Global ocean forecasting aims to predict key ocean variables such as temperature, salinity, and currents, which is essential for understanding and describing oceanic phenomena. In recent years, data-driven deep learning-…
• On the Similarities of Embeddings in Contrastive Learning
  2025 · 具身智能 · Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for under…
• A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example
  2025 · 合成生物与生物制造 · Bioprocess mechanistic modeling is essential for advancing intelligent digital twin representation of biomanufacturing, yet challenges persist due to complex intracellular regulation, stochastic system behavior, and limi…
• Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation
  2026 · AI 安全与对齐 · Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environme…
• A Primer on Quantum Machine Learning
  2025 · 量子材料 · Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle opti…
• Supervised Kernel Thinning
  2024 · AI 安全与对齐 · The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size significantly smaller than the input points,…
• How Does Variance Shape the Regret in Contextual Bandits?
  2024 · AI 安全与对齐 · We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax bounds, we show that the eluder dime…
• A Theoretical Understanding of Self-Correction through In-context Alignment
  2024 · AI 安全与对齐 · Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting …
• Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
  2024 · AI 驱动科学发现 · 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,…
• Hyperparameter Optimization in Machine Learning
  2024 · AI 驱动科学发现 · 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 effec…
• Error-controlled non-additive interaction discovery in machine learning models
  2024 · AI 驱动科学发现 · Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance.…
• Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage
  2024 · 碳中和与 CCUS · This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by th…
• PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis
  2024 · 深海科技 · The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in t…
• Sensitivity Analysis on Policy-Augmented Graphical Hybrid Models with Shapley Value Estimation
  2024 · 合成生物与生物制造 · Driven by the critical challenges in biomanufacturing, including high complexity and high uncertainty, we propose a comprehensive and computationally efficient sensitivity analysis framework for general nonlinear policy-…
• Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Process
  2024 · 合成生物与生物制造 · Biomanufacturing innovation relies on an efficient Design of Experiments (DoEs) to optimize processes and product quality. Traditional DoE methods, ignoring the underlying bioprocessing mechanisms, often suffer from a la…
• Delta-AI: Local objectives for amortized inference in sparse graphical models
  2023 · AI 安全与对齐 · We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $Δ$-amortized inference ($Δ$-AI). Our approach is based on the observation that when the sampling of varia…
• Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics
  2023 · AI 安全与对齐 · Deep equilibrium (DEQ) models replace the multiple-layer stacking of conventional deep networks with a fixed-point iteration of a single-layer transformation. Having been demonstrated to be competitive in a variety of re…
• The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
  2023 · AI 驱动科学发现 · 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,…
• Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
  2023 · AI 驱动科学发现 · 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 learni…
• The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
  2023 · 仿生智能 · Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based o…
• The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
  2023 · 脑科学 · Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based o…
• Multi-Armed Bandits and Quantum Channel Oracles
  2023 · 量子计算 · Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up …
• Nonperturbative renormalization for the neural network-QFT correspondence
  2021 · AI 安全与对齐 · In a recent work arXiv:2008.08601, Halverson, Maiti and Stoner proposed a description of neural networks in terms of a Wilsonian effective field theory. The infinite-width limit is mapped to a free field theory, while fi…
• Align, then memorise: the dynamics of learning with feedback alignment
  2020 · AI 安全与对齐 · Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks. Despite relying on random feedback weights…
• Statistical Guarantees for Regularized Neural Networks
  2020 · AI 安全与对齐 · Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for learning neural networks from data, especi…
• Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness
  2020 · AI 安全与对齐 · Deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However, they are widely seen as uninterpretable "black boxes", as it can be difficult t…
• Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena
  2020 · AI 安全与对齐 · In the context of neural network models, overparametrization refers to the phenomena whereby these models appear to generalize well on the unseen data, even though the number of parameters significantly exceeds the sampl…
• Learning Latent Causal Structures with a Redundant Input Neural Network
  2020 · AI 安全与对齐 · Most causal discovery algorithms find causal structure among a set of observed variables. Learning the causal structure among latent variables remains an important open problem, particularly when using high-dimensional d…
• The prospects of quantum computing in computational molecular biology
  2020 · 生命科学 · Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nea…
• AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference
  2020 · 低空经济 · Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the ch…
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
  2020 · 低空经济 · Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of contin…
• Is machine learning good or bad for the natural sciences?
  2024 · AI 驱动科学发现 · 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 …
• Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications
  2024 · 仿生智能 · The control and modeling of robot dynamics have increasingly adopted model-free control strategies using machine learning. Given the non-linear elastic nature of bionic robotic systems, learning-based methods provide rel…

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