18 datasets found

The dataset is used to test the algorithm for minimizing a convex objective function with noisy oracle.

The CEC-2017 benchmark functions dataset is used to evaluate the performance of optimization algorithms. It contains 30 functions with different characteristics, including...

The dataset used in the paper is a collection of optimization problems, including linear and quadratic programming problems, and is used to evaluate the performance of the...

The dataset used for the proposed efficient correlation-based discretization method for annealing machines.

The dataset used in the paper is a ridge regression problem (Millionsong) and a support vector machine (RCV1) and a matrix completion problem (Netflix) on a large-scale...

The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used a Bayesian Optimization algorithm and a dataset for industrial process...

The dataset used in the paper is a stateful performative setting, where the data distribution reacts to the deployed model. The goal is to learn a model that both induces a...

The dataset used in the paper is a synthetic dataset for ridge regression problem over a network of agents, modeled as a Erdos-Renyi graph with m = 30 nodes and edge probability...

DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks.

The dataset used in the paper is a 3-d Gaussian distributed dataset.

The dataset used in the paper is a synthetic dataset generated by the component decoupled model described in Section 3.

This dataset is used to test the robustness of watermarking methods against adaptive attacks.

The dataset used in this paper is a stochastic multi-level composition optimization problem.

The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function.

The dataset is used to analyze the convergence of Riemannian stochastic gradient descent (RSGD) on Riemannian manifolds.

The dataset used in the paper is a least squares regression problem instance.

The dataset used in the paper is not explicitly mentioned, but it is mentioned that the authors used Rosenbrock function for optimization.

The dataset used in this paper is a continuous-time stochastic control problem for SGD and similar stochastic gradient descent algorithms.