{"list":"other","total":55,"rows":[{"id":"1309.5549","title":"Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming","created":"2013-09-22","cited_by_count":724},{"id":"1608.04636","title":"Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-\\L{}ojasiewicz Condition","created":"2016-08-16","cited_by_count":539},{"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":"1807.01695","title":"SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator","created":"2018-07-04","cited_by_count":287},{"id":"1703.00887","title":"How to Escape Saddle Points Efficiently","created":"2017-03-02","cited_by_count":285},{"id":"1503.02101","title":"Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition","created":"2015-03-06","cited_by_count":243},{"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":"1309.2388","title":"Minimizing Finite Sums with the Stochastic Average Gradient","created":"2013-09-10","cited_by_count":164},{"id":"1603.06160","title":"Stochastic Variance Reduction for Nonconvex Optimization","created":"2016-03-19","cited_by_count":145},{"id":"1308.6594","title":"Mini-batch Stochastic Approximation Methods for Nonconvex Stochastic Composite Optimization","created":"2013-08-29","cited_by_count":143},{"id":"1603.05953","title":"Katyusha: The First Direct Acceleration of Stochastic Gradient Methods","created":"2016-03-18","cited_by_count":135},{"id":"2106.05203","title":"EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback","created":"2021-06-09","cited_by_count":133},{"id":"1605.07272","title":"Matrix Completion has No Spurious Local Minimum","created":"2016-05-24","cited_by_count":125},{"id":"2008.10898","title":"PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization","created":"2020-08-25","cited_by_count":119},{"id":"2002.11364","title":"Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization","created":"2020-02-26","cited_by_count":90},{"id":"1605.07221","title":"Global Optimality of Local Search for Low Rank Matrix Recovery","created":"2016-05-23","cited_by_count":82},{"id":"1711.00501","title":"Learning One-hidden-layer Neural Networks with Landscape Design","created":"2017-11-01","cited_by_count":81},{"id":"1403.4699","title":"A Proximal Stochastic Gradient Method with Progressive Variance Reduction","created":"2014-03-19","cited_by_count":78},{"id":"2102.07845","title":"MARINA: Faster Non-Convex Distributed Learning with Compression","created":"2021-02-15","cited_by_count":78},{"id":"1705.10412","title":"Gradient Descent Can Take Exponential Time to Escape Saddle Points","created":"2017-05-29","cited_by_count":74},{"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":"1711.10456","title":"Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent","created":"2017-11-28","cited_by_count":66},{"id":"1803.05999","title":"Escaping Saddles with Stochastic Gradients","created":"2018-03-15","cited_by_count":66},{"id":"1806.07811","title":"Stochastic Nested Variance Reduction for Nonconvex Optimization","created":"2018-06-20","cited_by_count":63},{"id":"1706.09156","title":"Non-convex Finite-Sum Optimization Via SCSG Methods","created":"2017-06-28","cited_by_count":61},{"id":"1506.02186","title":"A Universal Catalyst for First-Order Optimization","created":"2015-06-06","cited_by_count":54},{"id":"1708.08694","title":"Natasha 2: Faster Non-Convex Optimization Than SGD","created":"2017-08-29","cited_by_count":49},{"id":"1605.08003","title":"Tight Complexity Bounds for Optimizing Composite Objectives","created":"2016-05-25","cited_by_count":48},{"id":"1705.02766","title":"\"Convex Until Proven Guilty\": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions","created":"2017-05-08","cited_by_count":46},{"id":"2201.13320","title":"BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression","created":"2022-01-31","cited_by_count":41},{"id":"1902.00247","title":"Sharp Analysis for Nonconvex SGD Escaping from Saddle Points","created":"2019-02-01","cited_by_count":40},{"id":"1902.05679","title":"ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization","created":"2019-02-15","cited_by_count":37},{"id":"1711.06673","title":"Neon2: Finding Local Minima via First-Order Oracles","created":"2017-11-17","cited_by_count":36},{"id":"2206.09888","title":"SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression","created":"2022-06-20","cited_by_count":34},{"id":"1507.02000","title":"An optimal randomized incremental gradient method","created":"2015-07-08","cited_by_count":33},{"id":"1802.04477","title":"A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization","created":"2018-02-13","cited_by_count":33},{"id":"1711.01944","title":"First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time","created":"2017-11-03","cited_by_count":29},{"id":"2103.01447","title":"ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation","created":"2021-03-02","cited_by_count":29},{"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":"1904.09265","title":"SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points","created":"2019-04-19","cited_by_count":25},{"id":"1905.12412","title":"A unified variance-reduced accelerated gradient method for convex optimization","created":"2019-05-29","cited_by_count":24},{"id":"1602.05908","title":"Efficient approaches for escaping higher order saddle points in non-convex optimization","created":"2016-02-18","cited_by_count":23},{"id":"2107.09461","title":"CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression","created":"2021-07-20","cited_by_count":23},{"id":"1611.00756","title":"Accelerated Methods for Non-Convex Optimization","created":"2016-11-02","cited_by_count":20},{"id":"2108.04755","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","created":"2021-08-10","cited_by_count":20},{"id":"1609.03261","title":"Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method","created":"2016-09-12","cited_by_count":18},{"id":"2110.01165","title":"DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization","created":"2021-10-04","cited_by_count":16},{"id":"1905.00529","title":"Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization","created":"2019-05-01","cited_by_count":15},{"id":"1711.05762","title":"Random gradient extrapolation for distributed and stochastic optimization","created":"2017-11-15","cited_by_count":14},{"id":"2112.13097","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","created":"2021-12-24","cited_by_count":14},{"id":"1806.08782","title":"Finding Local Minima via Stochastic Nested Variance Reduction","created":"2018-06-22","cited_by_count":9},{"id":"2103.11333","title":"ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method","created":"2021-03-21","cited_by_count":9},{"id":"1611.01146","title":"Finding Approximate Local Minima Faster than Gradient Descent","created":"2016-11-03","cited_by_count":8},{"id":"1902.04811","title":"On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points","created":"2019-02-13","cited_by_count":8}],"next_cursor":null}