Key papers

• GPTQ-2D: Cubic-Time Two-Sided Adaptive Rounding (2026)
  Jiale Chen et al.
  7.37
• Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs (2026)
  Akira Kitaoka
  5.49
• Singular value soft-thresholding via the polar decomposition (2026)
  Stephen Becker
  5.49
• ScoreShield: Differentially Private Release of Similarity Scores (2026)
  Behrooz Razeghi et al.
  5.49
• Denoising growth complexity: Data geometry and certified schedules for diffusion sampling (2026)
  Martin J. Wainwright
  5.49
• Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction (2026)
  Hiroki Hamaguchi et al.
  5.49
• Field Codes for Distributed Coupling Samplers and Certified Empirical Transport (2026)
  Hung Mai et al.
  5.49
• An exact information theory of generalization phase transitions in Bayesian diffusion models (2026)
  Henry Hunt et al.
  5.01
• RELTA-SGLD: Relative-Growth Localized Taming for Nonconvex Stochastic-Gradient Langevin Learning (2026)
  Yiwei Zhou et al.
  5.01
• Prior laundering: learned priors with inherited, undetectable overconfidence (2026)
  Ali Siahkoohi et al.
  5.01
• Smart predict-then-robustly-optimize (2026)
  Aakil Caunhye et al.
  5.01
• Distributional Determinantal Point Process for Repulsive Clustering of Distributions (2026)
  Khai Nguyen et al.
  5.01
• Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model (2026)
  Ruijian Han et al.
  5.01
• Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)
  Ali Fakhar (UGA) et al.
  5.01
• Multiclass Classification without Labels via Posterior Simplex Geometry (2026)
  Rapha\"el Bonnet-Guerrini et al.
  5.01
• Generative Distributionally Robust Optimization (2026)
  Ziwei Zhang et al.
  5.01
• A Foundational Perspective for Partitional Clustering on Networks (2026)
  Derya Ipek Eroglu et al.
  5.01
• Variance-Reduced Conditional Gradient Methods under Markovian Sampling for Nonconvex Composite Optimization (2026)
  Zhaojun Peng
  5.01
• When Kernel Ridge Regression Meets the H\"older-Zygmund Class: Minimax Optimality and Failure of Properness (2026)
  Yuxuan Hou
  5.01
• Randomizing the Number of Centers in k-means++ (2026)
  Vaclav Rozhon
  5.01
• Origins and mitigation of double descent in reduced order modeling (2026)
  Andrei A. Klishin et al.
  5.01
• Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions (2026)
  Shuo-Chieh Huang et al.
  5.01
• Feature Bagging Provides Stability (2026)
  Yuheng Ma et al.
  5.01
• On the robustness of noisy solutions in non-convex neural networks (2026)
  Enrico M. Malatesta et al.
  5.01
• Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise (2026)
  Vaneet Aggarwal
  5.01
• Verifiable Rewards for Calibrated Probabilistic Forecasting (2026)
  Sadanand Singh et al.
  4.39
• Provable Pruning for Efficient 3D Gaussian Splatting via Coresets (2026)
  Waseem Mousa et al.
  4.39
• Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem (2026)
  Bla\v{z} P\v{s}eni\v{c}nik et al.
  4.39
• Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization (2026)
  Junjie Guo
  4.39
• Geometric mean-based pairwise comparison method with the reference values -- statistical approach (2026)
  Konrad Ku{\l}akowski et al.
  4.39
• Sharper Analysis of Single-Loop Methods for Bilevel Optimization (2026)
  Yubo Zhou et al.
  4.39
• Learning from Noise: Effective-Rank Collapse and Out-of-Distribution Rejection in Restricted Boltzmann Machines (2026)
  Oshada Rathnayake et al.
  4.39
• Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity (2026)
  Roberto Faleh et al.
  4.39
• Projection-Domain Sensitivity Analysis of Vertebral DRRs Under Intrinsic Calibration Perturbation (2026)
  Lin Li et al.
  4.39
• Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds (2026)
  Swagatam Das et al.
  4.39
• Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs (2026)
  Shrestha Datta et al.
  4.39
• Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO) (2026)
  Haricharan Balasundaram et al.
  4.39
• Efficient Online Proportional Sampling with Applications to Smoothed Online Learning (2026)
  Amirmahdi Mirfakhar et al.
  4.39
• Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective (2026)
  Melveena Jolly et al.
  4.39
• Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions (2026)
  Yongquan Shi et al.
  4.39
• Fundamental Limitations of Fixed-Budget Best-Arm Identification (2026)
  Motti Goldberger
  4.39
• Efficient Sequential Calibration with $O(T^{2/3-\epsilon})$ Error Bound (2026)
  Zihan Zhang
  4.39
• Price of Fairness in Bandits: A Tight Minimax Characterization (2026)
  Dhruv Sarkar et al.
  4.39
• Operator-Aware Mixed-Precision Tolerance Calibration for Tensor Kernels (2026)
  Dipankar Sarkar
  4.39
• FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration (2026)
  Filippo Cenacchi et al.
  4.39
• Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization (2026)
  Zhen Huang et al.
  4.39
• PAC--Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors (2026)
  Nicola Aladrah et al.
  4.39
• Contraction-Gauge Preconditioning for Quantized Matrix Multiplication (2026)
  Piyush Sao et al.
  4.39
• ABOPD: Antibody CDR Design via On-Policy Distillation (2026)
  Zhuo Yang et al.
  4.39
• Optimizing Regret (2026)
  Irene Aldridge
  4.39
• Provable diffusion-based posterior sampling for linear inverse problems via DDIM (2026)
  Yuchen Jiao et al.
  4.39
• Online Variance Reduction for Domain Adaptation on Streaming Data (2026)
  Andrea Napoli
  4.39
• Reliability-Aware Bayesian Optimization of 1310 nm PCSELs with FDTD Verification (2026)
  Jinglin Yu et al.
  4.39
• Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA (2026)
  Sergio A. Alvarez
  4.39
• Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study (2026)
  Kaihua Ding
  4.39
• From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning (2026)
  Jiaxin Deng et al.
  4.39
• Unbiased Open World Regularization for Fair Self-Supervised Learning (2026)
  L{\'e}o Nicollier (CB et al.
  4.39
• From Score Approximation to Distribution Approximation in Score-Based Diffusion Models (2026)
  Lan V. Truong
  4.39
• Trajectory-Regularized Stochastic Optimal Control via KL Divergence (2026)
  Mintae Kim et al.
  4.39
• Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates (2026)
  Anjian Li et al.
  4.39