Key papers

• Control Laguerre Tessellation: Semi-discrete Optimal Transport Over Control Systems (2026)
  Ripon C. Sarker et al.
  5.49
• Lipschitzian SLLNs for random functions (2026)
  Lai Tian et al.
  5.49
• On the Role of Normalization in Binary Iterative Hard Thresholding for 1-bit Compressed Sensing (2026)
  Arya Mazumdar et al.
  5.01
• RELTA-SGLD: Relative-Growth Localized Taming for Nonconvex Stochastic-Gradient Langevin Learning (2026)
  Yiwei Zhou et al.
  5.01
• Online Optimization of Difference-of-Convex Compositions with Smooth Mappings (2026)
  Jingwei Ji et al.
  5.01
• Local Stability and Gaussian Smoothing of Quantized Neural Networks (2026)
  Sergey Salishev et al.
  5.01
• On Optimization Complexity of Second-Order Certified Unlearning (2026)
  Nikita Doikov et al.
  5.01
• Decentralized Online Riemannian Optimization for Strongly Geodesically Convex Functions (2026)
  Zhanyuan Cai et al.
  5.01
• Bandit PCA with Minimax Optimal Regret (2026)
  Mo\"ise Blanchard et al.
  4.39
• Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite (2026)
  Yushi Hirose et al.
  4.39
• Parallel gradient boosting for flexible estimation of conditional distributions (2026)
  R\'emy Chapelle (CESP et al.
  4.39
• Non-Asymptotic Variational Learning for Monotone Nonlinear Multiscale Elliptic Equations: Scale-Robust Primal-Dual Bounds and Strong-Form Statistical Ill-Conditioning (2026)
  Ronald Katende
  4.39
• A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing (2026)
  Mohsen Dastpak et al.
  3.51
• From Operations to Elderly Care Outcomes: A Thematic Review of Industrial Engineering and Decision-Support Approaches (2026)
  Shayan Farhang Pazhooh et al.
  3.51
• A Non-generative Framework and Convex Relaxations for Unsupervised Learning (2016)
  Elad Hazan et al.
  —
• Stochastic Variational Optimization (2018)
  Thomas Bird et al.
  —
• Provable Smoothness Guarantees for Black-Box Variational Inference (2019)
  Justin Domke
  —
• New Risk Bounds for 2D Total Variation Denoising (2019)
  Sabyasachi Chatterjee et al.
  —
• A total variation based regularizer promoting piecewise-Lipschitz reconstructions (2019)
  Martin Burger et al.
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• Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function (2019)
  Arvind U. Raghunathan et al.
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• Variational Bayesian Methods for Stochastically Constrained System Design Problems (2020)
  Prateek Jaiswal et al.
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• The statistical effect of entropic regularization in optimal transportation (2020)
  Eustasio del Barrio and Jean-Michel Loubes
  —
• Non-Convex SGD Learns Halfspaces with Adversarial Label Noise (2020)
  Ilias Diakonikolas et al.
  —
• Approximation Based Variance Reduction for Reparameterization Gradients (2020)
  Tomas Geffner et al.
  —
• Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting (2020)
  Biraj Subhra Guha et al.
  —
• Quantized Variational Inference (2020)
  Amir Dib
  —
• Neural Collapse with Cross-Entropy Loss (2020)
  Jianfeng Lu et al.
  —
• Quasi-Newton Quasi-Monte Carlo for variational Bayes (2021)
  Sifan Liu and Art B. Owen
  —
• Variational Actor-Critic Algorithms (2021)
  Yuhua Zhu et al.
  —
• Entropic Gromov-Wasserstein between Gaussian Distributions (2021)
  Khang Le and Dung Le and Huy Nguyen and Dat Do and Tung Pham and Nhat Ho
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• Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives (2022)
  Pierre-Alexandre Mattei and Jes Frellsen
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• Accelerating Stochastic Probabilistic Inference (2022)
  Minta Liu et al.
  —
• Real order total variation with applications to the loss functions in learning schemes (2022)
  Pan Liu et al.
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• The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data (2022)
  Takashi Takahashi
  —
• A Note on the Convergence of Mirrored Stein Variational Gradient Descent under $(L_0,L_1)-$Smoothness Condition (2022)
  Lukang Sun et al.
  —
• Convergence Rates for Regularized Optimal Transport via Quantization (2022)
  Stephan Eckstein et al.
  —
• Lower Bounds for the Total Variation Distance Given Means and Variances of Distributions (2022)
  Tomohiro Nishiyama
  —
• Solving stochastic weak Minty variational inequalities without increasing batch size (2023)
  Thomas Pethick et al.
  —
• On the Convergence of Black-Box Variational Inference (2023)
  Kyurae Kim et al.
  —
• Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? (2023)
  Kyurae Kim et al.
  —
• Variational Barycentric Coordinates (2023)
  Ana Dodik et al.
  —
• A Variational Perspective on High-Resolution ODEs (2023)
  Hoomaan Maskan et al.
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• Max-sliced 2-Wasserstein distance (2024)
  March T. Boedihardjo
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