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

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Showing 1051–1100 of 1387 papers

TitleStatusHype
Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic OptimizationCode0
Learning Models with Uniform Performance via Distributionally Robust Optimization—0
Graphical Convergence of Subgradients in Nonconvex Optimization and Learning—0
Quasi-hyperbolic momentum and Adam for deep learningCode0
Towards Gradient Free and Projection Free Stochastic Optimization—0
Continuous-time Models for Stochastic Optimization AlgorithmsCode0
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational InferenceCode0
Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization—0
Bandit learning in concave N-person games—0
Optimal Adaptive and Accelerated Stochastic Gradient DescentCode0
Riemannian Adaptive Optimization MethodsCode2
Privacy-preserving Stochastic Gradual Learning—0
Exploring Curvature Noise in Large-Batch Stochastic Optimization—0
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems—0
Asynchronous decentralized accelerated stochastic gradient descent—0
Pricing American Options by Exercise Rate Optimization—0
Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points—0
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning—0
Graph Pattern Mining and Learning through User-defined Relations (Extended Version)Code0
A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation—0
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration—0
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes—0
Online Adaptive Methods, Universality and Acceleration—0
Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach—0
A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration—0
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization—0
Stochastic Gradient Descent with Biased but Consistent Gradient EstimatorsCode0
On-line Adaptative Curriculum Learning for GANsCode0
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information—0
Deep PDF: Probabilistic Surface Optimization and Density Estimation—0
Particle Filtering Methods for Stochastic Optimization with Application to Large-Scale Empirical Risk Minimization—0
signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization—0
Online Label Aggregation: A Variational Bayesian Approach—0
Maximizing Invariant Data Perturbation with Stochastic OptimizationCode0
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator—0
Learning concise representations for regression by evolving networks of treesCode1
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions—0
Stochastic PCA with _2 and _1 Regularization—0
Stochastic Proximal Algorithms for AUC Maximization—0
Data-driven satisficing measure and ranking—0
Direct Acceleration of SAGA using Sampled Negative Momentum—0
Finding Local Minima via Stochastic Nested Variance Reduction—0
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in AdamCode0
Swarming for Faster Convergence in Stochastic Optimization—0
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data—0
Spectral Inference Networks: Unifying Deep and Spectral LearningCode1
Variational Implicit ProcessesCode0
Accelerated Randomized Coordinate Descent Algorithms for Stochastic Optimization and Online Learning—0
AdaGrad stepsizes: Sharp convergence over nonconvex landscapesCode0
Modeling Realistic Degradations in Non-blind Deconvolution—0
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All datasetsCIFAR-100 WRN-28-10 - 200 EpochsCIFAR-10 WRN-28-10 - 200 EpochsCIFAR-10 ResNet-18 - 200 EpochsImageNet ResNet-50 - 90 EpochsPenn Treebank (Character Level) 3x1000 LSTM - 500 EpochsCIFAR-10CIFAR-100ImageNet ResNet-50 - 50 EpochsImageNet ResNet-50 - 60 EpochsAG NewsCoLAMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AvaGradAccuracy81.24—Unverified
2AdaShiftAccuracy81.12—Unverified
3Adam (eps-adjusted)Accuracy81.04—Unverified
4SGDAccuracy80.95—Unverified
5AdamWAccuracy79.87—Unverified
6AdaBoundAccuracy77.24—Unverified
#ModelMetricClaimedVerifiedStatus
1Adam (eps-adjusted)Accuracy96.36—Unverified
2AvaGradAccuracy96.2—Unverified
3SGDAccuracy96.14—Unverified
4AdaShiftAccuracy95.92—Unverified
5AdamWAccuracy95.89—Unverified
6AdaBoundAccuracy94.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SGD - cosine LR scheduleAccuracy95.55—Unverified
2LookaheadAccuracy95.27—Unverified
3SGDAccuracy95.23—Unverified
4ADAMAccuracy94.84—Unverified
#ModelMetricClaimedVerifiedStatus
1AvaGradTop 1 Accuracy76.51—Unverified
2SGDTop 1 Accuracy75.99—Unverified
3AdamWTop 1 Accuracy72.9—Unverified
4AdaBoundTop 1 Accuracy72.01—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBoundBit per Character (BPC)2.86—Unverified
2AdaShiftBit per Character (BPC)1.27—Unverified
3AdamWBit per Character (BPC)1.23—Unverified
4AvaGradBit per Character (BPC)1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Accuracy (max)86.85—Unverified
2Resnet34Accuracy (max)86.14—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Accuracy (max)58.48—Unverified
2Resnet34Accuracy (max)54.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SGDTop 5 Accuracy92.15—Unverified
2LookaheadTop 1 Accuracy75.13—Unverified
#ModelMetricClaimedVerifiedStatus
1LookaheadTop 1 Accuracy75.49—Unverified
2SGDTop 1 Accuracy75.15—Unverified
#ModelMetricClaimedVerifiedStatus
1BertAccuracy (max)93.99—Unverified
#ModelMetricClaimedVerifiedStatus
1BertAccuracy (max)86.34—Unverified
#ModelMetricClaimedVerifiedStatus
1MLPNLL0.05—Unverified