Key papers

• Weak-to-Strong Learning in Decision Making (2026)
  Jingwei Ji et al.
  6.23
• An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning (2026)
  Maximilian Dax et al.
  5.89
• 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
• Improved lower bounds for the Shannon capacity of odd cycles (2026)
  Nathaniel Itty 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
• Natural Invariant Measures for Chaotic Game Dynamics: Finding Order in Chaos (2026)
  Jakub Bielawski et al.
  5.01
• Simulation-Based Empirical Bayes (2026)
  Xinwei Shen et al.
  5.01
• Distributional Determinantal Point Process for Repulsive Clustering of Distributions (2026)
  Khai Nguyen et al.
  5.01
• Interventional Score Geometry for Causal Inference (2026)
  Mojtaba Eslami
  5.01
• Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model (2026)
  Ruijian Han et al.
  5.01
• Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks (2026)
  Masahiro Tanaka
  5.01
• Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)
  Ali Fakhar (UGA) et al.
  5.01
• Learning to Optimize at Scale: A Benders Decomposition-TransfORmers Framework for Stochastic Combinatorial Optimization (2026)
  Seung Jin Choi et al.
  5.01
• A Resolution of the SS--RS--GD Inequalities (2026)
  Binghui Peng
  5.01
• Learning from the Descent Direction: Adaptive Gradient Descent under One-Sided H\"older Regularity (2026)
  Arzu Ahmadova et al.
  5.01
• Nesterov acceleration in optimizing over probability measures (2026)
  Jiaqi Tang et al.
  5.01
• Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements (2026)
  Taiqi Zhou et al.
  5.01
• When Rates Are Geometric: Rate-Certificate Transfer for Contact Splittings in Optimization (2026)
  George A Kevrekidis
  5.01
• Verifiable Rewards for Calibrated Probabilistic Forecasting (2026)
  Sadanand Singh et al.
  4.39
• Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example (2026)
  Yufei Wu et al.
  4.39
• Provable Pruning for Efficient 3D Gaussian Splatting via Coresets (2026)
  Waseem Mousa et al.
  4.39
• Scalable Maximal Frequent Episode Mining with Desbordante (2026)
  Maxim Ivanov et al.
  4.39
• Personalized Causal Recourse: A Human-In-The-Loop Approach (2026)
  Denise Tampieri et al.
  4.39
• Efficient Discovery of Conditional Dependencies with Desbordante (2026)
  Ivan Kozhukov et al.
  4.39
• Piercing Gilbreath's Conjecture: From Deep Number Theory Insights to Fintech and Cybersecurity (2026)
  Vincent Granville
  4.39
• CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling (2026)
  Zuwang He et al.
  4.39
• SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR (2026)
  Ujjwala Vadrevu
  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
• Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data (2026)
  Yalcin Akcay et al.
  4.39
• Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization (2026)
  Junjie Guo
  4.39
• Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing (2026)
  Sujay Uday Rittikar
  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
• Is Model Instability just Noise to be Tolerated or a Property that can be Managed? (2026)
  Amirali Rayegan 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
• Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds (2026)
  Swagatam Das et al.
  4.39
• Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems (2026)
  L. A. Zhukov 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
• Relaxing Faithfulness with Intervention-Only Causal Discovery (2026)
  Bijan Mazaheri et al.
  4.39
• Efficient Sequential Calibration with $O(T^{2/3-\epsilon})$ Error Bound (2026)
  Zihan Zhang
  4.39
• Tabular Foundation Models for Discrete Choice Estimation (2026)
  Liu Liu et al.
  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
• Attractor Geometry Determines the Identifiability Limits of System Discovery (2026)
  Matteo Gallo et al.
  4.39
• Contraction-Gauge Preconditioning for Quantized Matrix Multiplication (2026)
  Piyush Sao et al.
  4.39
• Optimizing Regret (2026)
  Irene Aldridge
  4.39
• RAMP: Recognition parametrisation by Amortised Message Passing (2026)
  Lior Fox et al.
  4.39
• Provable diffusion-based posterior sampling for linear inverse problems via DDIM (2026)
  Yuchen Jiao et al.
  4.39
• Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence (2026)
  Runlong Zhou et al.
  4.39
• Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias (2026)
  Zheng Li et al.
  4.39