{"list":"other","total":53,"rows":[{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1606.04838","title":"Optimization Methods for Large-Scale Machine Learning","created":"2016-06-15","cited_by_count":1150},{"id":"1407.0202","title":"SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives","created":"2014-07-01","cited_by_count":402},{"id":"1809.07599","title":"Sparsified SGD with Memory","created":"2018-09-20","cited_by_count":376},{"id":"2003.10422","title":"A Unified Theory of Decentralized SGD with Changing Topology and Local Updates","created":"2020-03-23","cited_by_count":367},{"id":"1905.11881","title":"Why gradient clipping accelerates training: A theoretical justification for adaptivity","created":"2019-05-28","cited_by_count":341},{"id":"2003.00307","title":"Loss landscapes and optimization in over-parameterized non-linear systems and neural networks","created":"2020-02-29","cited_by_count":233},{"id":"1703.00102","title":"SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient","created":"2017-03-01","cited_by_count":220},{"id":"1901.09401","title":"SGD: General Analysis and Improved Rates","created":"2019-01-27","cited_by_count":207},{"id":"1309.2388","title":"Minimizing Finite Sums with the Stochastic Average Gradient","created":"2013-09-10","cited_by_count":164},{"id":"2301.11235","title":"Handbook of Convergence Theorems for (Stochastic) Gradient Methods","created":"2023-01-26","cited_by_count":163},{"id":"2002.03329","title":"Better Theory for SGD in the Nonconvex World","created":"2020-02-09","cited_by_count":158},{"id":"1712.06559","title":"The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning","created":"2017-12-18","cited_by_count":150},{"id":"1610.02132","title":"QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding","created":"2016-10-07","cited_by_count":147},{"id":"1810.07288","title":"Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron","created":"2018-10-16","cited_by_count":143},{"id":"2002.12410","title":"On Biased Compression for Distributed Learning","created":"2020-02-27","cited_by_count":140},{"id":"1603.05953","title":"Katyusha: The First Direct Acceleration of Stochastic Gradient Methods","created":"2016-03-18","cited_by_count":135},{"id":"1901.09269","title":"Distributed Learning with Compressed Gradient Differences","created":"2019-01-26","cited_by_count":112},{"id":"2011.02828","title":"Local SGD: Unified Theory and New Efficient Methods","created":"2020-11-03","cited_by_count":88},{"id":"1905.11261","title":"A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent","created":"2019-05-27","cited_by_count":84},{"id":"2008.00051","title":"On the Convergence of SGD with Biased Gradients","created":"2020-07-31","cited_by_count":83},{"id":"1907.04232","title":"Unified Optimal Analysis of the (Stochastic) Gradient Method","created":"2019-07-09","cited_by_count":82},{"id":"2010.00892","title":"Variance-Reduced Methods for Machine Learning","created":"2020-10-02","cited_by_count":79},{"id":"1901.08689","title":"Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop","created":"2019-01-24","cited_by_count":68},{"id":"2010.12292","title":"Linearly Converging Error Compensated SGD","created":"2020-10-23","cited_by_count":61},{"id":"1806.06573","title":"Distributed learning with compressed gradients","created":"2018-06-18","cited_by_count":55},{"id":"1308.6370","title":"Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition","created":"2013-08-29","cited_by_count":54},{"id":"2006.11077","title":"A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning","created":"2020-06-19","cited_by_count":43},{"id":"2108.00951","title":"Rethinking gradient sparsification as total error minimization","created":"2021-08-02","cited_by_count":41},{"id":"1506.03662","title":"Variance Reduced Stochastic Gradient Descent with Neighbors","created":"2015-06-11","cited_by_count":39},{"id":"2102.06489","title":"Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness","created":"2021-02-12","cited_by_count":37},{"id":"2202.07262","title":"Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods","created":"2022-02-15","cited_by_count":37},{"id":"2006.11573","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","created":"2020-06-20","cited_by_count":36},{"id":"1805.02632","title":"Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching","created":"2018-05-07","cited_by_count":34},{"id":"2111.08611","title":"Stochastic Extragradient: General Analysis and Improved Rates","created":"2021-11-16","cited_by_count":31},{"id":"2012.06188","title":"Recent Theoretical Advances in Non-Convex Optimization","created":"2020-12-11","cited_by_count":30},{"id":"2006.07013","title":"A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization","created":"2020-06-12","cited_by_count":28},{"id":"2202.00998","title":"3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation","created":"2022-02-02","cited_by_count":27},{"id":"1602.02442","title":"A Simple Practical Accelerated Method for Finite Sums","created":"2016-02-08","cited_by_count":18},{"id":"2205.04180","title":"EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization","created":"2022-05-09","cited_by_count":17},{"id":"1906.01481","title":"L-SVRG and L-Katyusha with Arbitrary Sampling","created":"2019-06-04","cited_by_count":14},{"id":"2106.03056","title":"MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization","created":"2021-06-06","cited_by_count":14},{"id":"1902.00947","title":"Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions","created":"2019-02-03","cited_by_count":12},{"id":"2207.03730","title":"Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology","created":"2022-07-08","cited_by_count":10},{"id":"2208.13592","title":"Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey","created":"2022-08-29","cited_by_count":10},{"id":"1901.08788","title":"Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise","created":"2019-01-25","cited_by_count":9},{"id":"1905.11266","title":"One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods","created":"2019-05-27","cited_by_count":8},{"id":"2206.10452","title":"Shifted Compression Framework: Generalizations and Improvements","created":"2022-06-21","cited_by_count":6},{"id":"2004.02635","title":"Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms","created":"2020-04-03","cited_by_count":5},{"id":"1905.11535","title":"A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions","created":"2019-05-27","cited_by_count":4},{"id":"2201.12206","title":"A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization and Coordinate Descent","created":"2022-01-28","cited_by_count":4},{"id":"2203.02383","title":"Distributed Methods with Absolute Compression and Error Compensation","created":"2022-03-04","cited_by_count":4},{"id":"2012.04402","title":"A Primal-Dual Framework for Decentralized Stochastic Optimization","created":"2020-12-08","cited_by_count":3}],"next_cursor":null}