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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–800 of 1387 papers

TitleStatusHype
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration—0
Global-Order GFlowNets—0
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck—0
Gradient Clipping Helps in Non-Smooth Stochastic Optimization with Heavy-Tailed Noise—0
Gradient Descent, Stochastic Optimization, and Other Tales—0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Gradient-free stochastic optimization for additive models—0
Gradient Sparification for Asynchronous Distributed Training—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy—0
Gradient-Variation Online Learning under Generalized Smoothness—0
Graph-Homomorphic Perturbations for Private Decentralized Learning—0
Graphical Convergence of Subgradients in Nonconvex Optimization and Learning—0
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization—0
Gray-box inference for structured Gaussian process models—0
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization—0
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent—0
Heavy-tailed Streaming Statistical Estimation—0
Heavy-Tail Phenomenon in Decentralized SGD—0
Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework—0
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization—0
Heteroscedasticity-aware residuals-based contextual stochastic optimization—0
Hierarchical Policy Blending as Inference for Reactive Robot Control—0
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization—0
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data—0
Higher order measures of risk and stochastic dominance—0
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping—0
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails—0
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance—0
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations—0
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics—0
How Free is Parameter-Free Stochastic Optimization?—0
How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function—0
How to Improve Sample Complexity of SGD over Highly Dependent Data?—0
Huber-energy measure quantization—0
Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication—0
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization—0
Hydroassets Portfolio Management for Intraday Electricity Trading from a Discrete Time Stochastic Optimization Perspective—0
Hyperlink Regression via Bregman Divergence—0
Implicit Parameter-free Online Learning with Truncated Linear Models—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Importance Sampling for Minimization of Tail Risks: A Tutorial—0
Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization—0
Improved Dropout for Shallow and Deep Learning—0
Improved Langevin Monte Carlo for stochastic optimization via landscape modification—0
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints—0
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations—0
Improving Offline Contextual Bandits with Distributional Robustness—0
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach—0
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking—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