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Stochastic Optimization
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Stochastic Optimization is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.
Source: ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
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Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching
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Multi-Timescale Gradient Sliding for Distributed Optimization
Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals
Distributed gradient methods under heavy-tailed communication noise
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
Distribution free M-estimation
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
Federated Learning: From Theory to Practice
Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials
Adapting Novelty towards Generating Antigens for Antivirus systems
Adaptive Semantic Token Communication for Transformer-based Edge Inference
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
CMA-ES with Radial Basis Function Surrogate for Black-Box Optimization
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Contextual Learning for Stochastic Optimization
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