Key papers

• GRPO, Dr. GRPO, and DAPO Are Three Operations on One Number: The Group-Standard-Deviation Identity (2026)
  Yong Yi Bay et al.
  7.37
• How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size (2026)
  Fabian Schaipp
  5.88
• Distributionally Robust Linear Regression With Block Lewis Weights (2026)
  Naren Sarayu Manoj et al.
  5.01
• Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization (2026)
  Xuefeng Liu et al.
  5.01
• eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions (2026)
  Qijun Chen et al.
  5.01
• Full Bayesian Reinforcement Learning via LF-IBIS (2026)
  Stefano Masini et al.
  5.01
• Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model (2026)
  Francis Bach (SIERRA)
  5.01
• Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks (2026)
  Matej Benko et al.
  5.01
• Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction (2026)
  Yurui Zheng et al.
  5.01
• An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility (2026)
  Sampreeti Bhattacharya et al.
  5.01
• Proactive Inpatient Bed Requests for Emergency Department Admissions (2026)
  QIan Cheng et al.
  5.01
• From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators (2026)
  Nisha Chandramoorthy et al.
  4.39
• Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes (2026)
  Agnieszka Kope\'c et al.
  4.39
• Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization (2026)
  Peilin Liu et al.
  4.39
• Prototype Language Models (2026)
  Dan Ley et al.
  4.39
• From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research (2026)
  Ka Ching Chan et al.
  4.39
• Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity (2026)
  Huichao Li et al.
  4.39
• Characterizing and Identifying Separable Graphical Models (2026)
  Christopher Meek et al.
  4.39
• Decision-Aware Training for Sample-Based Generative Models (2026)
  Kornelius Raeth et al.
  4.39
• From Approximation to Emergence: A Theory of Deep Learning (2026)
  Zhilin Zhao
  4.39
• Conditional Inference Trees and Forests for Feature Selection (2026)
  Robert Milletich et al.
  4.39
• Role-Aware Neural Convex Divergence Heads for Asymmetric Representation Learning (2026)
  He Huang et al.
  4.39
• Spectral Higher-Order Neural Networks Have Sharp Expressivity Bounds (2026)
  Gianluca Peri et al.
  4.39
• Free energy landscape of Dense Associative Memory (2026)
  Sumedha et al.
  4.39
• Adaptive Bayesian Online Learning via Expert Aggregation (2026)
  Jungbin Jun et al.
  4.39
• Polynomial Histograms for Memory-Efficient Representation of Long-tailed System Distributions (2026)
  Murray Stokely et al.
  4.27
• Byzantine Machine Learning: MultiKrum and an optimal notion of robustness (2026)
  Gilles Bareilles et al.
  3.98
• Online Safety Monitoring for LLMs (2026)
  Mona Schirmer et al.
  3.51
• Distributional Determinantal Point Process for Repulsive Clustering of Distributions (2026)
  Khai Nguyen et al.
  3.51
• Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature (2026)
  Rachid Caich et al.
  3.51
• Range Penalization: Theoretical Insights with Applications in Federated Learning (2026)
  Yiyuan She et al.
  3.45
• Scalable Decentralized Learning with Teleportation (2025)
  Yuki Takezawa et al.
  2.60
• A Structural Interpretation of GELU and Threshold-Transmission Activations via the First-Order Loss Function (2026)
  Roberto Rossi
  2.00
• Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography (2026)
  Boyuan Deng et al.
  2.00
• Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration (2026)
  Vishnu Bindu Balachandran
  2.00
• An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning (2026)
  Maximilian Dax et al.
  2.00
• A Defense of the Quadratic Model (2026)
  Alexandru Meterez et al.
  2.00
• Prior laundering: learned priors with inherited, undetectable overconfidence (2026)
  Ali Siahkoohi et al.
  2.00
• Longitudinal Random Forests for Sparse and Irregular Response Trajectories (2026)
  Yangsheng Wang et al.
  2.00
• Simulation-Based Empirical Bayes (2026)
  Xinwei Shen et al.
  2.00
• Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study (2026)
  Kaihua Ding
  2.00
• Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes (2026)
  Lu Luo et al.
  2.00
• From Score Approximation to Distribution Approximation in Score-Based Diffusion Models (2026)
  Lan V. Truong
  2.00
• Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model (2026)
  Ruijian Han et al.
  2.00
• Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis (2026)
  Laura M. Montaldo et al.
  2.00
• Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs (2026)
  Akira Kitaoka
  2.00
• General Value Functions for Remaining Useful Life and Failure-Mode Prediction (2026)
  Hao Yan et al.
  2.00
• An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection (2026)
  Romain Hermary et al.
  2.00
• Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting (2026)
  Wan Zhang et al.
  2.00
• Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks (2026)
  Masahiro Tanaka
  2.00
• Learning Ergodic Dynamical Systems from a Finite Trajectory (2026)
  Oleksii Kachaiev et al.
  2.00
• Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)
  Ali Fakhar (UGA) et al.
  2.00
• Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent (2026)
  Peng Zhao
  2.00
• CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference (2026)
  Jiyuan Tan et al.
  2.00
• Multi-Agent Conformal Prediction with Personalized Statistical Validity (2026)
  Martin V. Vejling and Christophe A. N. Biscio and Adrien Mazoyer and Petar Popovski and Shashi Raj Pandey
  1.94
• Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks (2026)
  Greg Ongie et al.
  1.94
• A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning (2026)
  Dario Filatrella et al.
  1.89
• FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling (2026)
  Guangyi Zhang et al.
  1.89
• Modulated learning for private and distributed regression with just a single sample per client device (2026)
  Praneeth Vepakomma et al.
  1.89
• Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning (2026)
  Hao Chen and Zavareh Bozorgasl
  1.89