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This model then serves as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization. [ 3 ] [ 4 ] [ 5 ] RLHF has applications in various domains in machine learning, including natural language processing tasks such as text summarization and conversational agents , computer vision tasks ...
Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. [1] Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference information.
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Learning rate; Least squares; Least-squares spectral analysis; Lemke's algorithm; Level-set method; Levenberg–Marquardt algorithm; Lexicographic max-min optimization; Lexicographic optimization; Limited-memory BFGS; Line search; Linear-fractional programming; Lloyd's algorithm; Local convergence; Local search (optimization) Luus–Jaakola
Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function. The function need not be differentiable, and no derivatives are taken. The function must be a real-valued function of a fixed number of real-valued inputs.
It is particularly useful in machine learning for minimizing the cost or loss function. [1] Gradient descent should not be confused with local search algorithms, although both are iterative methods for optimization. Gradient descent is generally attributed to Augustin-Louis Cauchy, who first suggested it in 1847. [2]