SOTAVerified

Stochastic Optimization

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

Papers

Showing 110 of 1387 papers

TitleStatusHype
First-order methods for stochastic and finite-sum convex optimization with deterministic constraints0
Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters0
The Sample Complexity of Parameter-Free Stochastic Convex Optimization0
Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)0
Distribution free M-estimation0
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading0
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees0
Adaptive Semantic Token Communication for Transformer-based Edge InferenceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Resnet18Accuracy (max)58.48Unverified
2Resnet34Accuracy (max)54.5Unverified