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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 70
End-to-End Guarantees for Masked Diffusion Models under Conditional Mixing
Masked diffusion models train a denoiser on masked cross-entropy and generate by unmasking many positions in parallel.Smooth Action Chunking Via Movement-Primitive Representations for Imitation Learning
Imitation learning has become a widely adopted approach for vision-based robot policies, owing to its conceptual simplicity, scalability, and ability to leverage large-scale datasets.Sparse Fine-Tuning for Parameter-Efficient Adversarial Training
Deep neural networks achieve strong performance across many tasks but remain vulnerable to adversarial perturbations.Achieving Minimax-Optimal First-Order and Best-of-Both-Worlds Regret Simultaneously in Multi-Armed Bandits
We study first-order and best-of-both-worlds regret guarantees in multi-armed bandits.CBE: A Complexity-adaptive Boundary-aware Ensemble Method for Imbalanced Data
In many real-world classification tasks, datasets exhibit class imbalance, which can degrade the generalization performance of conventional learning algorithms.Topology Survives Shuffling: Robust Long-Context Memory Access Prediction for Graph Analytics
Graph analytics generates irregular, topology-dependent memory-access streams via indirect traversals, limiting data prefetching.Does Federated Training Degrade Explainability? A Comparative Analysis of Grad-CAM Faithfulness in Centralized vs. Federated Deepfake Detection
Automated detection has become the essential safeguard against realistic synthetic media, but deploying it at scale requires training on sensitive data from multiple organizations, putting privacy…Interpretable by Design Tabular Prediction via In-Context Local Linear Learning
In-context learning (ICL) lets tabular foundation models make predictions without dataset-specific training or fine-tuning.Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties
Accurately predicting the transport properties of liquid electrolytes is critical for next-generation battery design and requires force fields that are both quantum-mechanically accurate and fast…Last-Iterate Convergence for Constrained MDPs: From Dual Descent to Primal-Dual
We study policy optimization for infinite-horizon, discounted constrained Markov decision processes.Conic-Lifting for Learning-rate-free Convex Optimization
A central topic in online and stochastic optimization is to design algorithms that do not require tuning of hyperparameters.ROAD: Modern Adversarial Training for Simple One-Step Distillation at Scale
One-step distillation turns pretrained diffusion models into fast generators, but strong results often require substantial training machinery and compute.The Cost of Working Memory in Streaming Mean Estimation
A streaming estimator must compute from the evidence that fits in memory.Comprehensive empirical evaluation of feature extractors in computer vision
Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications.Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem
The rapid expansion of digital banking and mobile financial services creates growing challenges for fraud detection, particularly under severe class imbalance and operational constraints.LatentWeave: Test-Time Latent Optimization for Multimodal Reasoning
Recent advances in test-time optimization have shown that the multimodal reasoning performance of large multimodal language models (MLLMs) can be improved during inference without modifying backbone…TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals.Quantifying the Relay-Coverage Trade-off in Mobile Multihop Flooding
Network-wide flooding in mobile multihop networks selects relays from locally maintained topology information.Trajeform: Embedding Crowd Data
Crowd simulation aims to generate agent behaviours that are rich, natural, and appropriate to their surrounding environment.Diffusion models for the $O(3)$ nonlinear sigma model
A score-based diffusion model is trained on the two-dimensional classical Heisenberg model, the lattice representation of the $O(3)$ nonlinear sigma model.