#machine-learning

40 curated events

papersTODAY 04:00 UTC

Density Ratio Estimation and Importance-Weighted Regression Under Target Shift

This paper examines how to estimate density ratios and perform importance-weighted regression when the output distribution shifts between training and test data while the conditional input distribution given outputs stays the same. The authors derive optimal estimation approaches for this continuous-output target shift setting. The work falls in the statistical machine learning area of distribution shift and covariate/label shift correction.

research/machine-learning/distribution-shift/statistics

papersTODAY 04:00 UTC

Neural Modal Decomposition Derives Architectural Priors from Observables

A new arXiv preprint introduces neural modal decomposition, an approach that infers architectural priors for multi-port linear time-invariant systems by observing their behavior rather than relying on hand-specified designs. The authors note that RF cavities, photonic components, and superconducting quantum circuits, though physically distinct, can be described by a shared mathematical framework, which the method exploits. The work positions itself at the intersection of machine learning architecture design and engineering system modeling.

arxiv/research/machine-learning/physics

papersTODAY 04:00 UTC

SeqMaestro: interpretable machine learning links nucleotide sequences to biological hypotheses

A new arXiv paper introduces SeqMaestro, a method that analyzes nucleotide sequences using interpretable machine learning to connect raw sequence data with testable biological hypotheses. The approach aims to combine the interpretability of classical bioinformatics features, such as motifs and k-mer composition, with the predictive power of modern ML models. It targets applications across regulatory genomics, evolutionary biology, and phenotype prediction.

research/interpretability/genomics/machine-learning

papersTODAY 04:00 UTC

Machine Learning Method Screens Point Defects in Semiconductors

A new arXiv paper describes a machine-learning approach for prescreening point defects in semiconductors, aimed at applications in power electronics and quantum technologies. The work positions itself as an alternative to the high-throughput density-functional theory calculations that have traditionally dominated defect exploration. The authors frame the method as part of a broader shift away from conventional simulation workflows.

machine-learning/materials-science/dft/research

papersTODAY 04:00 UTC

Robotic Art Installations Use Embodied Machine Learning and Digital Evolution

A research paper describes three robotic art installations that explore adaptive behavior as an aesthetic theme. The works combine embodied machine learning with digital evolution to create an artificial ecosystem where open-ended novelty emerges through trial and error. Viewers are drawn into observing how the robots change and evolve over time.

robotics/machine-learning/art/evolutionary-computing

papersTODAY 04:00 UTC

arXiv paper proposes machine learning method to map and predict global ocean eco-provinces

A new preprint describes an approach that moves from identifying ecological marine provinces, called eco-provinces, toward predicting them using learning methods. The authors frame the work as a step toward trustworthy models that can support spatial habitat analysis as climate change affects marine ecosystems. The paper appears on arXiv under the cs.LG category as a cross-listing.

marine-ecology/climate/machine-learning/arxiv

papersTODAY 04:00 UTC

Paper Suggests Analyzing Fairness Through Utilities Instead of Constrained Policies

The authors argue that fairness criteria which restrict a predictor or policy can produce unwanted side effects, especially when the policy is optimized under those constraints. They propose instead examining fairness directly through utility functions, and offer initial steps toward fairness objectives that avoid those drawbacks. The work is a revised cross-listing on arXiv in machine learning.

fairness/machine-learning/research/arxiv

papersTODAY 04:00 UTC

Paper Proposes Multi-block Single-probe Estimator for Coupled Compositional Optimization

A new arXiv preprint introduces a variance reduction technique for finite-sum coupled compositional optimization, a setting where existing single-function estimators such as SPIDER, SARAH and STORM do not directly apply. The authors propose a multi-block, single-probe estimator intended to improve convergence rates in this coupled setting. The work is a theoretical optimization contribution.

optimization/arxiv/variance-reduction/machine-learning

papersTODAY 04:00 UTC

arXiv paper links control theory, inference, transport and thermodynamics in learning

A new arXiv preprint surveys how methods for learning structure from high-dimensional data connect to ideas from control theory, statistical inference, optimal transport and thermodynamics. The author argues these shared mathematical foundations bridge physics and applied mathematics with machine learning. The paper also outlines applications of this unified perspective.

research/theory/arxiv/machine-learning

papersTODAY 04:00 UTC

Paper narrows complexity gaps in nonconvex finite-sum optimization

A new arXiv paper studies finite-sum optimization under individual smoothness assumptions, where the best achievable incremental first-order oracle complexity has remained unresolved. It proposes a "dense weak hiding" approach that tightens the gap between existing algorithms, which need roughly n plus sqrt(n) times a smoothness-scaled term, and previously known lower bounds. The results cover both general nonconvex objectives and those satisfying the Polyak-Lojasiewicz condition.

optimization/theory/machine-learning/research

papersTODAY 04:00 UTC

Deletion Certificates for Support-Vector Memory: What They Cover and When They Expire

This paper examines how to verifiably delete stored items from a memory that places a support-vector boundary around keys and weights their values by the resulting coefficients. It shows that when a coefficient is exactly zero, the key can be removed without altering the current normal, and identifies the conditions under which such deletion guarantees stop holding. The work aims toward auditable removal in memory-based learning systems.

machine-learning/deletion/auditability/arxiv

papersTODAY 04:00 UTC

Study compares rotation representations for predicting SLM dental part build orientation

A new arXiv paper frames the choice of build orientation for selective laser melting of dental components as a supervised machine learning problem. Using orientation labels recorded by technicians in production data, the authors train models to predict a part's up-axis and compare several rotation representations against direct vector regression. The work targets automating a step that is currently performed manually.

additive-manufacturing/machine-learning/manufacturing/research

papersTODAY 04:00 UTC

arXiv Paper Reviews Machine Learning Methods for Imperfect Training Data

A new arXiv preprint examines how machine-learning pipelines behave when training or test data is incomplete, imbalanced, poorly labelled, or drawn from mismatched distributions. The authors survey measurement approaches and methods designed to keep models reliable under these common real-world conditions. The work is framed as an overview of challenges and remedies rather than a new model release.

arxiv/machine-learning/data-quality/research

papersTODAY 04:00 UTC

Multi-source conformal prediction method uses localization to handle heterogeneous data

A new arXiv paper proposes a conformal prediction approach that draws on multiple heterogeneous data sources rather than treating them as one pool. The method exploits differences between sources through localization, aiming to keep prediction sets reliable when the test distribution departs from any single source. This targets settings where combining sources is useful but naive pooling would break coverage guarantees.

conformal-prediction/research/uncertainty/machine-learning

papersTODAY 04:00 UTC

arXiv paper proposes clustering-assisted logistic model for PU classification beyond SCAR

A new arXiv preprint examines positive-unlabeled (PU) classification when the common SCAR assumption does not hold. The authors study logistic regression approaches, including a cluster-based method and Lasso-regularized variants, and add oversampling to improve performance. The work appears as a cross-listing in cs.AI and cs.LG.

arxiv/machine-learning/classification/research

papersTODAY 04:00 UTC

arXiv paper introduces agentic framework for high-throughput atomistic simulations

A new arXiv preprint describes a system that uses autonomous agents to run machine-learning interatomic potential simulations at scale. The work targets the difficulty of applying these potentials across broad chemical spaces, where near-ab initio accuracy is achieved at much lower computational cost. The abstract frames the effort as addressing a central bottleneck in practical use of such potentials.

agents/machine-learning/simulation/materials-science

papersTODAY 04:00 UTC

MMLA: Memory-Mediated Learning Architecture for Predictive Dual-State Adaptation

A new arXiv paper proposes a Memory-Mediated Learning Architecture (MMLA) that splits a learning system into slow base parameters, a bounded numerical policy carrier, and a bounded authoritative memory store. Its Predictive Dual-State Adaptation mechanism uses feedback to update the policy carrier while handling a problem-state component. The work is presented as a preprint on arXiv under cs.LG.

research/arxiv/machine-learning/memory

papersTODAY 04:00 UTC

arXiv Paper Proposes Leakage-Safe ML for Grid Job Runtime Prediction

A new arXiv preprint examines how machine learning models for predicting job runtimes in grid and distributed computing should be validated without data leakage. The authors argue that evaluation must reflect real deployment constraints, since overly optimistic results can mislead scheduling-aware resource management. The work revisits CPU burst time prediction with these safeguards in mind.

arxiv/machine-learning/scheduling/research

papersTODAY 04:00 UTC

AI Model Uses CRRT Pressure Waveforms to Predict Daily Mortality in AKI Patients

Researchers developed a machine learning approach that predicts day-by-day mortality risk for critically ill patients with acute kidney injury receiving continuous renal replacement therapy. Unlike existing tools that rely mainly on electronic health record data, this method incorporates minute-level machine pressure waveform signals. The work suggests that treatment device data can add predictive value beyond standard clinical parameters.

healthcare/machine-learning/mortality-prediction/research

papersSEP 11 04:00 UTC

Paper Proposes Langevin Gradient Descent for Data-Driven Hyperparameter Tuning

A new arXiv preprint introduces the Langevin Gradient Descent Algorithm (LGD), which approximates the mean of a posterior distribution defined by a regression problem's loss function and regularizer in order to tune gradient descent hyperparameters. The authors frame this as a learning-to-learn problem and provide generalization guarantees for the approach. The work targets data-driven tuning of optimization settings rather than hand-selected values.

research/optimization/machine-learning/arxiv

papersSEP 11 04:00 UTC

Graybox machine learning approach applied to Bayesian quantum sensing

A new arXiv preprint describes a graybox machine learning method for Bayesian quantum sensing. The approach combines physics-informed modeling with data-driven components to improve how quantum sensors estimate parameters. It aims to address practical performance limits that keep quantum sensors from reaching their theoretical advantages in fields such as materials science and healthcare.

quantum/machine-learning/research/sensing

papersSEP 11 04:00 UTC

Semidefinite Programming Method Proposed for Quantum Channel Learning

A new arXiv paper addresses how to reconstruct a quantum channel from a finite sample of classical measurement data. The author shows that when total fidelity can be written as a ratio of two quadratic forms, the learning task can be cast as a semidefinite program. This framing is illustrated with cases such as mapping a mixed state to a pure state under projective operations.

quantum-computing/optimization/research/machine-learning

papersSEP 11 04:00 UTC

arXiv paper reviews spatial fairness assessment in predictive models

A preprint posted to arXiv examines how researchers evaluate whether predictive models treat people from different geographic areas fairly. The work focuses on the common assumption that individuals can be tied to a single location, instead framing fairness through activity-space patterns. It is a revised version of an earlier submission.

fairness/arxiv/geospatial/machine-learning

papersSEP 11 04:00 UTC

Study evaluates machine learning weather models in Northern Norway

A new arXiv preprint assesses how machine learning weather prediction models perform in Northern Norway, a region with narrow fjords and difficult terrain where forecasts are notoriously hard. While such models have shown strong results on global reanalysis benchmarks, the authors examine whether that skill holds up in this demanding local setting. The work contributes a station-based evaluation framework for comparing these models in operational conditions.

weather-forecasting/benchmarks/machine-learning/arxiv

papersSEP 11 04:00 UTC

Mammography Risk Model Relies on Privileged History Distillation

A new arXiv paper proposes a method for predicting future breast cancer risk from longitudinal mammography screening data. The approach, called privileged history distillation, addresses the drop in performance that current models show when a patient's prior examinations are not available. It appears to be a revised submission (v2) to arXiv's machine learning category.

medical-imaging/risk-prediction/arxiv/machine-learning

papersSEP 11 04:00 UTC

arXiv Study Benchmarks Non-Conformity Score Functions for Conformal Prediction

A revised arXiv preprint surveys and compares non-conformity score functions used in conformal prediction for classification. Conformal prediction generates prediction sets rather than single labels, with guarantees that the sets cover the true class at a chosen rate. The work evaluates how different scoring choices affect the efficiency and validity of those sets.

conformal-prediction/benchmarking/uncertainty/machine-learning

papersSEP 10 04:00 UTC

Statistical Study of Bias in Generalized Zero-Shot Learning via Handwriting Recognition

A new arXiv paper cross-listed in AI and machine learning introduces a statistical framework for examining bias in generalized zero-shot learning, where models must recognize classes that never appeared in training. The authors ground the analysis in handwriting recognition, a setting where skewed distributions can disproportionately affect underrepresented groups. The work aims to extend bias measurement beyond the relatively narrow conditions covered by traditional GZSL methods.

zero-shot-learning/bias/handwriting-recognition/machine-learning

papersSEP 10 04:00 UTC

Kernel-Based Modular Discriminant Analysis Framework Proposed for Small-Sample Learning

A newly posted arXiv paper introduces a kernel-based modular discriminant analysis framework targeting the small-sample-size problem in machine learning, where labeled data are scarce due to cost, accessibility, or ethical constraints. The work addresses limitations of existing methods that struggle to perform reliably when training examples are limited.

machine-learning/algorithms/classification/small-sample

papersSEP 10 04:00 UTC

TIDE: interpretable battery degradation estimation method introduced in arXiv paper

A research paper on arXiv presents TIDE, a framework that combines contextual learning with symbolic distillation to estimate battery degradation in a trustworthy and interpretable manner. The authors argue that accurate battery health estimation is essential for control, maintenance, and service life in battery-powered systems, particularly as such systems become increasingly connected and intelligent.

research/machine-learning/interpretability/battery

papersSEP 10 04:00 UTC

Study proposes ensembling framework for quantifying algorithmic stability

A new machine learning preprint introduces a general framework for measuring how sensitive an algorithm is to perturbations of its input data, with the relevant notion of perturbation varying by setting. The authors tie this stability analysis to ensembling, indicating that combining multiple models can help make learning algorithms less sensitive to changes in the data.

machine-learning/theory/ensembling/stability

papersSEP 10 04:00 UTC

arXiv paper proposes iteratively reweighted least squares for fixed-charge network flow

A new arXiv study, cross-listed in AI and machine learning, tackles the fixed-charge network flow problem, where continuous flow decisions are intertwined with binary choices about which arcs to activate. The authors introduce a method based on iteratively reweighted least squares to discover the support of active arcs, addressing a computationally hard model that underpins many network design and resource allocation tasks. The work offers an alternative optimization perspective on a classic combinatorial challenge.

optimization/machine-learning/network-design

papersSEP 10 04:00 UTC

Researchers Propose Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation

A new arXiv paper introduces a subsampled variant of the Davis-Kahan theorem, a classical result that quantifies how far the eigenspaces of a symmetric matrix drift under perturbation. The proposed bound aims to make such eigenspace error control practical for very high-dimensional matrices, where computing leading eigenvectors directly is prohibitively expensive. This line of work is relevant to spectral methods in large-scale machine learning and statistics.

spectral-analysis/theory/machine-learning/arxiv

papersSEP 10 04:00 UTC

Paper introduces constraint-aware discrete black-box optimization using tensor decomposition

A new arXiv preprint tackles optimization of costly-to-evaluate functions over discrete domains under constraints. The proposed method incorporates tensor decomposition into sequential model-based optimization, using surrogate models to improve sample efficiency.

optimization/machine-learning/surrogate-models/research

papersSEP 10 04:00 UTC

Researchers Propose Influence-Based Weighting for Personalized Federated Learning

A revised arXiv preprint introduces a personalized federated learning method that weights each client's parameter updates according to its influence, rather than relying on fixed aggregation weights. The approach is designed to let devices with different data distributions and preferences train collaboratively while keeping their data private. The updated version appears across arXiv's cs.AI and cs.LG listings.

federated-learning/privacy/personalization/machine-learning

papersSEP 10 04:00 UTC

ML models with different inductive biases tested for cosmological inference from galaxy catalogs

Researchers used simulated galaxy catalogs from the CAMELS hydrodynamic simulations to infer the matter density parameter with machine learning models that embody different inductive biases. The paper evaluates how these architectural assumptions shape field-level likelihood-free inference of cosmological parameters.

cosmology/machine-learning/simulation/inference

papersSEP 10 04:00 UTC

Statistical Method Proposed for Determining Sample Sizes in Machine Learning Prediction Models

Researchers have introduced a statistical framework for estimating how much data is needed to train machine learning prediction models. The approach addresses a key limitation of conventional power analysis, which normally requires the predictor-outcome relationship and effect structure to be defined in advance—something that is impractical for nonlinear models that learn complex patterns from data. The work appears in a new arXiv preprint filed under both artificial intelligence and machine learning categories.

machine-learning/statistics/methodology/research

papersSEP 10 04:00 UTC

TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping

A new arXiv paper introduces TimeCues Studio, a workspace for placing precise annotations such as points, segments, and loops on music recordings. The tool supports both hand labeling and algorithmic prototyping, targeting the shortage of annotated training data that limits machine-learning approaches in multimedia applications.

music/annotation/machine-learning/datasets

papersSEP 10 04:00 UTC

Predicting HARQ Retransmissions to Improve MCS Selection in 5G AI-RAN

Researchers tackle a weakness in 5G link adaptation, where modulation and coding scheme choices depend on channel measurements and HARQ feedback that quickly become outdated in fast-changing conditions. Their data-driven approach predicts the likelihood of retransmissions, allowing the network to select more robust transmission parameters proactively. The work targets AI-enhanced radio access networks, where such predictions could improve throughput and reliability.

5g/wireless/machine-learning/research

papersSEP 10 04:00 UTC

CoGReV: A Confidence-Gated Post-Hoc Belief Revision Framework for Phishing Website Classification

Researchers introduce CoGReV, a framework that applies confidence-gated, non-monotonic belief revision to adjust machine learning outputs in phishing website detection. The goal is to reduce false alarms that burden human analysts reviewing classifier decisions, which can otherwise lead to alert fatigue and weaker oversight. The paper appears on arXiv as a version-3 replacement.

phishing/cybersecurity/machine-learning/classification

papersSEP 10 04:00 UTC

Physics-Guided Machine Learning Extrapolation Framework Validated on Diffusion Benchmark

A new arXiv paper introduces a physics-guided machine learning framework designed to make reliable predictions outside the limited operating ranges in which engineering models are typically trained. The authors argue that extrapolation, rather than interpolation, is the central challenge for applied ML, and they validate their approach using a classical transient diffusion problem as a benchmark.

physics-informed/extrapolation/machine-learning/research