{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/distributed-optimization/papers/5","list_of":"/task/distributed-optimization","task":"Distributed Optimization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":6,"rows_per_page":100,"rows":[401,500],"of":536,"counts":{"archive_papers_tagged":536,"with_a_code_link":86,"where_syntology_ran_a_sample":14,"not_listed_spam_title":0,"listed":536,"listed_where_code_ran":14,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":13,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":13,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/distributed-optimization","prev":"/task/distributed-optimization/papers/4","next":"/task/distributed-optimization/papers/6","papers":[{"url":null,"slug":"improving-rate-of-convergence-via-gain","title":"Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework","date":"2020-02-24","arxiv_id":"2002.10515","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-frequency-calibration-for-doa","title":"Multi-frequency calibration for DOA estimation with distributed sensors","date":"2020-02-24","arxiv_id":"2002.11498","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-extra-for-smooth-distributed","title":"Revisiting EXTRA for Smooth Distributed Optimization","date":"2020-02-24","arxiv_id":"2002.10110","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-mean-estimation-with-optimal","title":"New Bounds For Distributed Mean Estimation and Variance Reduction","date":"2020-02-21","arxiv_id":"2002.09268","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-sampling-distributed-stochastic","title":"Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets","date":"2020-02-20","arxiv_id":"2002.08528","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-geometry-of-sign-gradient-descent-1","title":"The Geometry of Sign Gradient Descent","date":"2020-02-19","arxiv_id":"2002.08056","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-over-block-cyclic","title":"Distributed Optimization over Block-Cyclic Data","date":"2020-02-18","arxiv_id":"2002.07454","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-local-sgd-better-than-minibatch-sgd","title":"Is Local SGD Better than Minibatch SGD?","date":"2020-02-18","arxiv_id":"2002.07839","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-averaging-methods-for-randomized","title":"Distributed Averaging Methods for Randomized Second Order Optimization","date":"2020-02-16","arxiv_id":"2002.06540","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-the-fundamental-convergence","title":"Differentially Quantized Gradient Methods","date":"2020-02-06","arxiv_id":"2002.02508","repositories_listed":0,"syntology":null},{"url":null,"slug":"acceleration-for-compressed-gradient-descent-1","title":"Acceleration for Compressed Gradient Descent in Distributed Optimization","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-error-of-randomized-newton","title":"Estimating the Error of Randomized Newton Methods: A Bootstrap Approach","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-graph-learning-over-optimization","title":"Graph Learning Under Partial Observability","date":"2019-12-18","arxiv_id":"1912.08465","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-for-reinforcement-learning-from","title":"Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents","date":"2019-12-01","arxiv_id":"1912.00498","repositories_listed":0,"syntology":null},{"url":null,"slug":"layer-wise-adaptive-gradient-sparsification","title":"Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees","date":"2019-11-20","arxiv_id":"1911.08727","repositories_listed":0,"syntology":null},{"url":null,"slug":"vqsgd-vector-quantized-stochastic-gradient","title":"vqSGD: Vector Quantized Stochastic Gradient Descent","date":"2019-11-18","arxiv_id":"1911.07971","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-accelerated-admm-for-distributed","title":"Learning-Accelerated ADMM for Distributed Optimal Power Flow","date":"2019-11-08","arxiv_id":"1911.03019","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-convergence-of-local-descent-methods","title":"On the Convergence of Local Descent Methods in Federated Learning","date":"2019-10-31","arxiv_id":"1910.14425","repositories_listed":0,"syntology":null},{"url":null,"slug":"popsgd-decentralized-stochastic-gradient-1","title":"Asynchronous Decentralized SGD with Quantized and Local Updates","date":"2019-10-27","arxiv_id":"1910.12308","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsification-as-a-remedy-for-staleness-in","title":"Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD","date":"2019-10-21","arxiv_id":"1910.09466","repositories_listed":0,"syntology":null},{"url":null,"slug":"popsgd-decentralized-stochastic-gradient","title":"PopSGD: Decentralized Stochastic Gradient Descent in the Population Model","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-consensus-linearly-convergent","title":"Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs","date":"2019-09-22","arxiv_id":"1909.10070","repositories_listed":0,"syntology":null},{"url":null,"slug":"convex-set-disjointness-distributed-learning","title":"Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility","date":"2019-09-08","arxiv_id":"1909.03547","repositories_listed":0,"syntology":null},{"url":null,"slug":"proximal-gradient-flow-and-douglas-rachford","title":"Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints","date":"2019-08-23","arxiv_id":"1908.09043","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-flows-and-accelerated-proximal","title":"Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization","date":"2019-08-02","arxiv_id":"1908.00865","repositories_listed":0,"syntology":null},{"url":null,"slug":"popt4jlib-a-paralleldistributed-optimization","title":"Popt4jlib: A Parallel/Distributed Optimization Library for Java","date":"2019-08-01","arxiv_id":"1908.00338","repositories_listed":0,"syntology":null},{"url":null,"slug":"centralised-and-distributed-optimization-for","title":"Centralised and Distributed Optimization for Aggregated Flexibility Services Provision","date":"2019-07-18","arxiv_id":"1907.08125","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-encoding-for-byzantine-resilient","title":"Data Encoding for Byzantine-Resilient Distributed Optimization","date":"2019-07-05","arxiv_id":"1907.02664","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotic-network-independence-in","title":"Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning","date":"2019-06-28","arxiv_id":"1906.12345","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-for-smart-cyber","title":"Distributed Optimization for Smart Cyber-Physical Networks","date":"2019-06-25","arxiv_id":"1906.10760","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-architectures-implementing-trusted","title":"Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)","date":"2019-06-18","arxiv_id":"1906.07690","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-for-over","title":"Distributed Optimization for Over-Parameterized Learning","date":"2019-06-14","arxiv_id":"1906.06205","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-communication-complexity-of-optimization","title":"The Communication Complexity of Optimization","date":"2019-06-13","arxiv_id":"1906.05832","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-accurate-statistical","title":"Communication-Efficient Accurate Statistical Estimation","date":"2019-06-12","arxiv_id":"1906.04870","repositories_listed":0,"syntology":null},{"url":null,"slug":"qsparse-local-sgd-distributed-sgd-with","title":"Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations","date":"2019-06-06","arxiv_id":"1906.02367","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-distributed-optimization","title":"Deep Learning for Distributed Optimization: Applications to Wireless Resource Management","date":"2019-05-31","arxiv_id":"1905.13378","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-sparsified-sgd-with-error","title":"Accelerated Sparsified SGD with Error Feedback","date":"2019-05-29","arxiv_id":"1905.12224","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-estimation-of-the-inverse-hessian","title":"Distributed estimation of the inverse Hessian by determinantal averaging","date":"2019-05-28","arxiv_id":"1905.11546","repositories_listed":0,"syntology":null},{"url":null,"slug":"leader-stochastic-gradient-descent-for","title":"Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension","date":"2019-05-24","arxiv_id":"1905.10395","repositories_listed":0,"syntology":null},{"url":null,"slug":"byzantine-fault-tolerant-distributed-linear","title":"Byzantine Fault Tolerant Distributed Linear Regression","date":"2019-03-20","arxiv_id":"1903.08752","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-consensus-based","title":"Differentially Private Consensus-Based Distributed Optimization","date":"2019-03-19","arxiv_id":"1903.07792","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-provably-communication-efficient","title":"A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems","date":"2019-03-16","arxiv_id":"1903.06871","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-distributed-learning-secure-machine","title":"SLSGD: Secure and Efficient Distributed On-device Machine Learning","date":"2019-03-16","arxiv_id":"1903.06996","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-maintaining-linear-convergence-of","title":"On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication","date":"2019-02-26","arxiv_id":"1902.11163","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsity-constrained-distributed-unmixing-of","title":"Sparsity Constrained Distributed Unmixing of Hyperspectral Data","date":"2019-02-20","arxiv_id":"1902.07593","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-frank-wolfe-communication-efficient","title":"Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free","date":"2019-02-17","arxiv_id":"1902.06332","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-subsampled-newton-methods-work-for-high","title":"Do Subsampled Newton Methods Work for High-Dimensional Data?","date":"2019-02-13","arxiv_id":"1902.04952","repositories_listed":0,"syntology":null},{"url":null,"slug":"predict-globally-correct-locally-parallel-in","title":"Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks","date":"2019-02-07","arxiv_id":"1902.02542","repositories_listed":0,"syntology":null},{"url":null,"slug":"99-of-parallel-optimization-is-inevitably-a","title":"99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it","date":"2019-01-27","arxiv_id":"1901.09437","repositories_listed":0,"syntology":null},{"url":null,"slug":"trajectory-normalized-gradients-for","title":"Trajectory Normalized Gradients for Distributed Optimization","date":"2019-01-24","arxiv_id":"1901.08227","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-nesterov-gradient-methods-over","title":"Distributed Nesterov gradient methods over arbitrary graphs","date":"2019-01-21","arxiv_id":"1901.06995","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-continuous-time-analysis-of-distributed","title":"A continuous-time analysis of distributed stochastic gradient","date":"2018-12-28","arxiv_id":"1812.10995","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-unmixing-based-on-clustered","title":"Hyperspectral Unmixing Based on Clustered Multitask Networks","date":"2018-12-27","arxiv_id":"1812.10788","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-distributed-optimization-for","title":"Stochastic Distributed Optimization for Machine Learning from Decentralized Features","date":"2018-12-16","arxiv_id":"1812.06415","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-non-smooth-constrained-programs-with","title":"Solving Non-smooth Constrained Programs with Lower Complexity than \\mathcal{O}(1/\\varepsilon): A Primal-Dual Homotopy Smoothing Approach","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"markov-chain-block-coordinate-descent","title":"Markov Chain Block Coordinate Descent","date":"2018-11-22","arxiv_id":"1811.08990","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-convex-optimization-with-limited","title":"Distributed Convex Optimization With Limited Communications","date":"2018-10-29","arxiv_id":"1810.12457","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-in-wireless-sensor","title":"Distributed optimization in wireless sensor networks: an island-model framework","date":"2018-10-05","arxiv_id":"1810.02679","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dual-approach-for-optimal-algorithms-in","title":"A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks","date":"2018-09-03","arxiv_id":"1809.00710","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-primal-dual-algorithm-converges-to","title":"Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exact-quantized-decentralized-gradient","title":"An Exact Quantized Decentralized Gradient Descent Algorithm","date":"2018-06-29","arxiv_id":"1806.11536","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tight-convergence-analysis-for-stochastic","title":"A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates","date":"2018-06-26","arxiv_id":"1806.10188","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-compensated-quantized-sgd-and-its","title":"Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization","date":"2018-06-21","arxiv_id":"1806.08054","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distributed-second-order-algorithm-you-can","title":"A Distributed Second-Order Algorithm You Can Trust","date":"2018-06-20","arxiv_id":"1806.07569","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-learning-with-compressed","title":"Distributed learning with compressed gradients","date":"2018-06-18","arxiv_id":"1806.06573","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-optimization-strategy-for-multi","title":"Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm","date":"2018-05-25","arxiv_id":"1806.06062","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-quantization-for-communication","title":"Double Quantization for Communication-Efficient Distributed Optimization","date":"2018-05-25","arxiv_id":"1805.10111","repositories_listed":0,"syntology":null},{"url":null,"slug":"tie-line-characteristics-based-partitioning","title":"Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems","date":"2018-05-24","arxiv_id":"1805.09779","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-centralized-deep-multi-agent","title":"Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients","date":"2018-05-22","arxiv_id":"1805.08776","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-projection-free","title":"Communication-Efficient Projection-Free Algorithm for Distributed Optimization","date":"2018-05-20","arxiv_id":"1805.07841","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aggregation-via-good-enough-model","title":"Model Aggregation via Good-Enough Model Spaces","date":"2018-05-20","arxiv_id":"1805.07782","repositories_listed":0,"syntology":null},{"url":null,"slug":"gosgd-distributed-optimization-for-deep","title":"GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange","date":"2018-04-04","arxiv_id":"1804.01852","repositories_listed":0,"syntology":null},{"url":null,"slug":"fundamental-resource-trade-offs-for-encoded","title":"Fundamental Resource Trade-offs for Encoded Distributed Optimization","date":"2018-03-31","arxiv_id":"1804.00217","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stochastic-large-scale-machine-learning","title":"A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations","date":"2018-03-29","arxiv_id":"1803.11287","repositories_listed":0,"syntology":null},{"url":null,"slug":"sucag-stochastic-unbiased-curvature-aided","title":"SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization","date":"2018-03-22","arxiv_id":"1803.08198","repositories_listed":0,"syntology":null},{"url":null,"slug":"redundancy-techniques-for-straggler","title":"Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning","date":"2018-03-14","arxiv_id":"1803.05397","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-rate-of-sign-stochastic-gradient","title":"Convergence rate of sign stochastic gradient descent for non-convex functions","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-consensus-via-min-sum-splitting","title":"Accelerated consensus via Min-Sum Splitting","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-algorithms-for-distributed","title":"Optimal Algorithms for Distributed Optimization","date":"2017-12-01","arxiv_id":"1712.00232","repositories_listed":0,"syntology":null},{"url":null,"slug":"cswa-aggregation-free-spatial-temporal","title":"CSWA: Aggregation-Free Spatial-Temporal Community Sensing","date":"2017-11-15","arxiv_id":"1711.05712","repositories_listed":0,"syntology":null},{"url":null,"slug":"straggler-mitigation-in-distributed","title":"Straggler Mitigation in Distributed Optimization Through Data Encoding","date":"2017-11-14","arxiv_id":"1711.04969","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-unmixing-of-hyperspectral-data","title":"Distributed Unmixing of Hyperspectral Data With Sparsity Constraint","date":"2017-11-03","arxiv_id":"1711.01249","repositories_listed":0,"syntology":null},{"url":null,"slug":"zeroth-order-nonconvex-multi-agent","title":"Zeroth Order Nonconvex Multi-Agent Optimization over Networks","date":"2017-10-27","arxiv_id":"1710.09997","repositories_listed":0,"syntology":null},{"url":"/paper/gradient-sparsification-for-communication","slug":"gradient-sparsification-for-communication","title":"Gradient Sparsification for Communication-Efficient Distributed Optimization","date":"2017-10-26","arxiv_id":"1710.09854","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sequential-approximation-framework-for","title":"A Sequential Approximation Framework for Coded Distributed Optimization","date":"2017-10-24","arxiv_id":"1710.09001","repositories_listed":0,"syntology":null},{"url":null,"slug":"dscovr-randomized-primal-dual-block","title":"DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization","date":"2017-10-13","arxiv_id":"1710.05080","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-very-large-scale-bundle","title":"Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"giant-globally-improved-approximate-newton","title":"GIANT: Globally Improved Approximate Newton Method for Distributed Optimization","date":"2017-09-11","arxiv_id":"1709.03528","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-debiased-distributed-estimation-for-sparse","title":"Debiased distributed learning for sparse partial linear models in high dimensions","date":"2017-08-18","arxiv_id":"1708.05487","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-distributed-and-federated","title":"Stochastic, Distributed and Federated Optimization for Machine Learning","date":"2017-07-04","arxiv_id":"1707.01155","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-consensus-admm-for-distributed","title":"Adaptive Consensus ADMM for Distributed Optimization","date":"2017-06-09","arxiv_id":"1706.02869","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-algorithms-for-feature-extraction","title":"Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks","date":"2017-05-15","arxiv_id":"1705.08252","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-distributed-dual-averaging-over","title":"Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity","date":"2017-04-18","arxiv_id":"1704.05193","repositories_listed":0,"syntology":null},{"url":null,"slug":"dope-distributed-optimization-for-pairwise","title":"DOPE: Distributed Optimization for Pairwise Energies","date":"2017-04-11","arxiv_id":"1704.03116","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-learning-on-networks-part-ii","title":"Private Learning on Networks: Part II","date":"2017-03-27","arxiv_id":"1703.09185","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-online-optimization-approach-for-multi","title":"An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise","date":"2017-02-21","arxiv_id":"1702.06219","repositories_listed":0,"syntology":null},{"url":null,"slug":"hemingway-modeling-distributed-optimization","title":"Hemingway: Modeling Distributed Optimization Algorithms","date":"2017-02-20","arxiv_id":"1702.05865","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-deep-neural-networks-via","title":"Training Deep Neural Networks via Optimization Over Graphs","date":"2017-02-11","arxiv_id":"1702.03380","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-integrated-optimization-learning-approach","title":"An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids","date":"2016-12-18","arxiv_id":"1612.05971","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-learning-on-networks","title":"Private Learning on Networks","date":"2016-12-15","arxiv_id":"1612.05236","repositories_listed":0,"syntology":null}],"record_sha256":"31d4345f1707a468ae241647de45d309c2a93a5268cfde43e4c03ffea9323d1c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}