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1. You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism

2. Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent

3. A naive aggregation algorithm for improving generalization in a class of learning problems

4. Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning

5. Evaluation of Prosumer Networks for Peak Load Management in Iran: A Distributed Contextual Stochastic Optimization Approach

6. Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence

7. Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

8. The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines

9. Proximal Point Method for Online Saddle Point Problem

10. Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective

11. Local Methods with Adaptivity via Scaling

12. Efficient Line Search Method Based on Regression and Uncertainty Quantification

13. Efficient model predictive control for nonlinear systems modelled by deep neural networks

14. Stability Evaluation via Distributional Perturbation Analysis

15. The Privacy Power of Correlated Noise in Decentralized Learning

16. From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization

17. Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging

18. Estimation Network Design framework for efficient distributed optimization

19. Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation

20. To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO

21. Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

22. Automatic Outlier Rectification via Optimal Transport

23. Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering

24. Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization

25. Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations

26. Learning Explicitly Conditioned Sparsifying Transforms

27. A cutting plane algorithm for globally solving low dimensional k-means clustering problems

28. Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness

29. Model-Free $\mu$-Synthesis: A Nonsmooth Optimization Perspective

30. Directional Convergence Near Small Initializations and Saddles in Two-Homogeneous Neural Networks

31. Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method

32. Dual Lagrangian Learning for Conic Optimization

33. Bayesian optimization as a flexible and efficient design framework for sustainable process systems

34. On the Correctness of the Generalized Isotonic Recursive Partitioning Algorithm

35. Graph Similarity Regularized Softmax for Semi-Supervised Node Classification

36. Convergence of Distributed Adaptive Optimization with Local Updates

37. SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems

38. From exponential to finite/fixed-time stability: Applications to optimization

39. Dynamic Range Reduction via Branch-and-Bound

40. Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation

41. Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate

42. Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guarantees

43. The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization

44. Training Safe Neural Networks with Global SDP Bounds

45. Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema

46. HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models

47. Optimization and Generalization Guarantees for Weight Normalization

48. Learning incomplete factorization preconditioners for GMRES

49. Riemannian Federated Learning via Averaging Gradient Stream

50. KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems