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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 127
Holo-NN: Holographic Nearest Neighbor Search via Compressive Sensing
In the age of AI, privacy-preserving approximate nearest neighbor (ANN) search over high-dimensional embeddings has emerged as a critical yet frustratingly challenging domain, dominated by tradeoffs…Automated identification of ivory carvings using machine learning: A proof-of-concept approach to detecting the illegal wildlife trade
Despite a ban on the international trading of recently poached ivory, elephants are still killed for their tusks.AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science
Data science agents can automate end-to-end machine learning workflows, yet they remain far from competitive on human leaderboards.Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest
Auto-bidding systems in online advertising must balance competing objectives-maximizing budget delivery while minimizing cost per action-across heterogeneous campaigns under non-stationary market…RadonBench: A Benchmark of Classical and Learning-Guided Search Techniques for Problems in Combinatorial Geometry
Classical optimization and learning-guided search offer complementary approaches to open problems in discrete mathematics, but their outputs are difficult to compare when search spaces, validity…Not All Mislabels Are Equal: Efficient Task-Aware Auditing with Confidence Sequences
We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream…Image Clustering with Vision-Language Model Embeddings
Pretrained vision-language models provide image representations whose suitability for clustering may vary with feature extraction, image domain, and partitioning algorithm.GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation
Engineering standards are challenging knowledge sources for large language models (LLMs) because they are hierarchical, multimodal, and densely connected through figures, tables, cross-references…Sharp Bounds on Continuous Treatment Effects under Unobserved Confounding
We study partial identification of conditional treatment effects with a continuous treatment under unobserved confounding.Individual Fairness via Local Predictive Consistency
Individual fairness in machine learning requires that similar individuals receive similar predictions.All-in-One: A Composable Attack on LLM Transformations
Quantization, pruning, and model merging are popular transformations applied to deployment-ready LLMs to improve efficiency and downstream performance.Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification
Random Forest--based selectors such as Boruta depend on tree depth, yet Boruta's most used implementation, BorutaPy, recommends depths of 3 to 7 without published evidence.Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
Powder X-ray diffraction (XRD) is the primary high-throughput method for resolving crystal structures, a cornerstone of materials science.When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training
How does pre-trained model capacity relate to performance after adaptation for causal tasks?MLIPDiff: Guiding Denoising Diffusion Models for Structure-Based Drug Design via Machine-Learned Interatomic Potentials
Diffusion models have become a dominant approach to structure-based drug design.TempShap: When and How Much a Feature Matters in Time-Series Forecasting
Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when in the input window a feature acts, and how much its influence changes across the…Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection
Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are…Gaussian Approximation for Composition and Subsampling of Differentially Private Algorithms
The privacy analysis of differentially private stochastic gradient descent (DP-SGD) relies on bounds for the composition of subsampled mechanisms.Information-Theoretic Scaling Laws for Generative Diffusion Training
Diffusion models trained on increasingly large datasets sampled from the same data distribution will converge to the same learned distribution.When Are Nesterov Trajectories Eventually Locally Minimizing?
We revisit the variational characterization of Nesterov's celebrated method and study when Nesterov trajectories are eventually locally minimizing in a variational sense, a notion that we define…