Key papers

• Mean Field Reinforcement Learning (2026)
  Ren\'e Carmona et al.
  5.49
• Distributionally Robust Linear Regression With Block Lewis Weights (2026)
  Naren Sarayu Manoj et al.
  5.01
• ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces (2026)
  Xun Dong et al.
  5.01
• GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics (2026)
  Jeffrey Fang et al.
  5.01
• Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement (2026)
  Yiquan Gao
  5.01
• Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model (2026)
  Francis Bach (SIERRA)
  5.01
• An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks (2026)
  Joseph Webb et al.
  5.01
• Proactive Inpatient Bed Requests for Emergency Department Admissions (2026)
  QIan Cheng et al.
  5.01
• Byzantine Machine Learning: MultiKrum and an optimal notion of robustness (2026)
  Gilles Bareilles et al.
  3.98
• ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning (2026)
  Yilie Huang et al.
  3.51
• A Unified Framework for Gradient Aggregation in Multi-Objective Optimization (2026)
  Zeou Hu et al.
  3.39
• FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection (2026)
  Yutong He et al.
  3.34
• Achieving Linear Speedup for Composite Federated Learning (2026)
  Kun Huang et al.
  3.23
• Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning (2025)
  Xusheng Zhang et al.
  2.68
• Scalable Decentralized Learning with Teleportation (2025)
  Yuki Takezawa et al.
  2.60
• Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation (2025)
  Kaoru Otsuka et al.
  2.51
• A Structural Interpretation of GELU and Threshold-Transmission Activations via the First-Order Loss Function (2026)
  Roberto Rossi
  2.00
• Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning (2026)
  Julian G. Soltes
  2.00
• A Defense of the Quadratic Model (2026)
  Alexandru Meterez et al.
  2.00
• Smart predict-then-robustly-optimize (2026)
  Aakil Caunhye et al.
  2.00
• On the Convergence of Stochastic Low-Rank Adaptation (2026)
  Ru Wang et al.
  2.00
• Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs (2026)
  Akira Kitaoka
  2.00
• Singular value soft-thresholding via the polar decomposition (2026)
  Stephen Becker
  2.00
• Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers (2026)
  Samuel Erickson et al.
  1.94
• Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks (2026)
  Greg Ongie et al.
  1.94
• Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation (2026)
  Ilya Levin et al.
  1.89
• LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging (2026)
  Yassine Maziane et al.
  1.89
• LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication (2026)
  Wei Liu et al.
  1.83
• Optimizing Stochastic Gradient Push under Broadcast Communications (2026)
  Tuan Nguyen and Ting He
  1.83
• FedSEA: Achieving Benefit of Parallelization in Federated Online Learning (2026)
  Harekrushna Sahu et al.
  1.83
• Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints (2026)
  Konstantinos Ziliaskopoulos and Alexander Vinel
  1.83
• Subspace Optimization for Efficient Federated Learning under Heterogeneous Data (2026)
  Shuchen Zhu et al.
  1.83
• Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need (2026)
  Xinran Zheng et al.
  1.78
• Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data (2026)
  Pu Qiu et al.
  1.78
• On Performance Guarantees for Federated Learning with Personalized Constraints (2026)
  Mohammadjavad Ebrahimi et al.
  1.78
• Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation (2026)
  Fateme Maleki et al.
  1.78
• Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions (2026)
  Rustem Islamov et al.
  1.78
• Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach (2026)
  Abdelkrim Alahyane (LAAS-SARA) et al.
  1.78
• Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning (2026)
  Shahryar Zehtabi et al.
  1.72
• From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity (2026)
  Renaud Gaucher et al.
  1.72
• Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms (2026)
  Kevin Kurian Thomas Vaidyan et al.
  1.72
• Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation (2026)
  Aleksandr Beznosikov et al.
  1.72
• Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking (2026)
  Afroditi Kolomvaki et al.
  1.72
• Accelerating Decentralized Optimization via Overlapping Local Steps (2026)
  Yijie Zhou and Shi Pu
  1.67
• First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data (2026)
  Artavazd Maranjyan
  1.67
• Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems (2026)
  Dmitry Bylinkin et al.
  1.67
• Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise (2026)
  Menglian Wang et al.
  1.67
• On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization (2026)
  Sharan Sahu et al.
  1.67
• BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training (2026)
  Laurent Condat et al.
  1.67
• FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization (2026)
  Antesh Upadhyay et al.
  1.67
• SA-PEF: Step-Ahead Partial Error Feedback for Efficient Federated Learning (2026)
  Dawit Kiros Redie et al.
  1.67
• Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method (2025)
  Yan Huang et al.
  1.61
• Non-Convex Federated Optimization under Cost-Aware Client Selection (2025)
  Xiaowen Jiang et al.
  1.61
• FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation (2025)
  Runze You and Shi Pu
  1.61
• First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions (2025)
  Egor Shulgin and Grigory Malinovsky and Sarit Khirirat and Peter Richt\'arik
  1.61
• Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback (2025)
  Tomas Ortega et al.
  1.61
• Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning (2025)
  Arnaud Descours (UCBL) et al.
  1.56
• Communication-Efficient Learning for Satellite Constellations (2025)
  Ruxandra-Stefania Tudose et al.
  1.56
• Trade-off in Estimating the Number of Byzantine Clients in Federated Learning (2025)
  Ziyi Chen et al.
  1.50
• Optimization Methods and Software for Federated Learning (2025)
  Konstantin Burlachenko
  1.44