{"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/stochastic-optimization/papers/3","list_of":"/task/stochastic-optimization","task":"Stochastic 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":3,"pages_in_order":14,"rows_per_page":100,"rows":[201,300],"of":1387,"counts":{"archive_papers_tagged":1387,"with_a_code_link":337,"where_syntology_ran_a_sample":91,"not_listed_spam_title":0,"listed":1387,"listed_where_code_ran":91,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":75,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":75,"listed_every_run_a_failure_of_syntologys_instrument":16,"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/stochastic-optimization","prev":"/task/stochastic-optimization/papers/2","next":"/task/stochastic-optimization/papers/4","papers":[{"url":"/paper/convergence-and-complexity-of-stochastic","slug":"convergence-and-complexity-of-stochastic","title":"Stochastic regularized majorization-minimization with weakly convex and multi-convex surrogates","date":"2022-01-05","arxiv_id":"2201.01652","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-client-sampling-in-federated","slug":"adaptive-client-sampling-in-federated","title":"Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback","date":"2021-12-28","arxiv_id":"2112.14332","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-with-dynamic-convex","slug":"reinforcement-learning-with-dynamic-convex","title":"Reinforcement Learning with Dynamic Convex Risk Measures","date":"2021-12-26","arxiv_id":"2112.13414","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reinforcement-learning-with-dynamic-convex#ran","syntology_url":"https://syntology.ai/paper/2112.13414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13414"}},"official":{"repos":["acoache/rl-dynamicconvexrisk"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bcd-nets-scalable-variational-approaches-for-1","slug":"bcd-nets-scalable-variational-approaches-for-1","title":"BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery","date":"2021-12-06","arxiv_id":"2112.02761","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bcd-nets-scalable-variational-approaches-for-1#ran","syntology_url":"https://syntology.ai/paper/2112.02761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02761"}},"official":{"repos":["ermongroup/bcd-nets"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mdpgt-momentum-based-decentralized-policy","slug":"mdpgt-momentum-based-decentralized-policy","title":"MDPGT: Momentum-based Decentralized Policy Gradient Tracking","date":"2021-12-06","arxiv_id":"2112.02813","repositories_listed":1,"syntology":null},{"url":"/paper/linear-speedup-in-personalized-collaborative","slug":"linear-speedup-in-personalized-collaborative","title":"Linear Speedup in Personalized Collaborative Learning","date":"2021-11-10","arxiv_id":"2111.05968","repositories_listed":1,"syntology":null},{"url":"/paper/bilevel-stochastic-methods-for-optimization","slug":"bilevel-stochastic-methods-for-optimization","title":"Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems","date":"2021-10-01","arxiv_id":"2110.00604","repositories_listed":1,"syntology":null},{"url":"/paper/slimtrain-a-stochastic-approximation-method","slug":"slimtrain-a-stochastic-approximation-method","title":"slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks","date":"2021-09-28","arxiv_id":"2109.14002","repositories_listed":1,"syntology":null},{"url":"/paper/coco-denoiser-using-co-coercivity-for","slug":"coco-denoiser-using-co-coercivity-for","title":"COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization","date":"2021-09-07","arxiv_id":"2109.03207","repositories_listed":1,"syntology":null},{"url":"/paper/vector-transport-free-riemannian-lbfgs-for","slug":"vector-transport-free-riemannian-lbfgs-for","title":"Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds","date":"2021-08-25","arxiv_id":"2108.11019","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-under-time-drift","slug":"stochastic-optimization-under-time-drift","title":"Stochastic Optimization under Distributional Drift","date":"2021-08-16","arxiv_id":"2108.07356","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-optimization-under-time-drift#ran","syntology_url":"https://syntology.ai/paper/2108.07356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07356"}},"official":{"repos":["joshuacutler/TimeDriftExperiments"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bype-vae-bayesian-pseudocoresets-exemplar-vae","slug":"bype-vae-bayesian-pseudocoresets-exemplar-vae","title":"ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE","date":"2021-07-20","arxiv_id":"2107.09286","repositories_listed":1,"syntology":null},{"url":"/paper/non-asymptotic-estimates-for-tusla-algorithm","slug":"non-asymptotic-estimates-for-tusla-algorithm","title":"Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function","date":"2021-07-19","arxiv_id":"2107.08649","repositories_listed":1,"syntology":null},{"url":"/paper/augmented-tensor-decomposition-with","slug":"augmented-tensor-decomposition-with","title":"ATD: Augmenting CP Tensor Decomposition by Self Supervision","date":"2021-06-15","arxiv_id":"2106.07900","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/augmented-tensor-decomposition-with#ran","syntology_url":"https://syntology.ai/paper/2106.07900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07900"}},"official":{"repos":["ycq091044/atd"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/near-optimal-high-probability-complexity","slug":"near-optimal-high-probability-complexity","title":"High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise","date":"2021-06-10","arxiv_id":"2106.05958","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-aided-energy","slug":"stochastic-optimization-aided-energy","title":"Stochastic Optimization-Aided Energy-Efficient Information Collection in Internet of Underwater Things Networks","date":"2021-06-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/memory-based-optimization-methods-for-model","slug":"memory-based-optimization-methods-for-model","title":"Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning","date":"2021-06-09","arxiv_id":"2106.04911","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-up-graph-neural-networks-via-graph","slug":"scaling-up-graph-neural-networks-via-graph","title":"Scaling Up Graph Neural Networks Via Graph Coarsening","date":"2021-06-09","arxiv_id":"2106.05150","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scaling-up-graph-neural-networks-via-graph#ran","syntology_url":"https://syntology.ai/paper/2106.05150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05150"}},"official":{"repos":["szzhang17/Scaling-Up-Graph-Neural-Networks-Via-Graph-Coarsening"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stochastic-iterative-graph-matching","slug":"stochastic-iterative-graph-matching","title":"Stochastic Iterative Graph Matching","date":"2021-06-04","arxiv_id":"2106.02206","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-iterative-graph-matching#ran","syntology_url":"https://syntology.ai/paper/2106.02206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02206"}},"official":null}},{"url":"/paper/stochastic-control-through-approximate","slug":"stochastic-control-through-approximate","title":"Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference","date":"2021-05-17","arxiv_id":"2105.07693","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-bayesian-filtering-lends","slug":"discriminative-bayesian-filtering-lends","title":"Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions","date":"2021-04-27","arxiv_id":"2104.12949","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-of-area-under","slug":"stochastic-optimization-of-area-under","title":"Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence","date":"2021-04-18","arxiv_id":"2104.08736","repositories_listed":1,"syntology":null},{"url":"/paper/the-computational-asymptotics-of-gaussian","slug":"the-computational-asymptotics-of-gaussian","title":"The computational asymptotics of Gaussian variational inference and the Laplace approximation","date":"2021-04-13","arxiv_id":"2104.05886","repositories_listed":1,"syntology":null},{"url":"/paper/branch-and-pruning-optimization-towards","slug":"branch-and-pruning-optimization-towards","title":"Training Deep Neural Networks via Branch-and-Bound","date":"2021-04-05","arxiv_id":"2104.01730","repositories_listed":1,"syntology":null},{"url":"/paper/correcting-momentum-with-second-order","slug":"correcting-momentum-with-second-order","title":"Better SGD using Second-order Momentum","date":"2021-03-04","arxiv_id":"2103.03265","repositories_listed":1,"syntology":null},{"url":"/paper/fermi-fair-empirical-risk-minimization-via","slug":"fermi-fair-empirical-risk-minimization-via","title":"A Stochastic Optimization Framework for Fair Risk Minimization","date":"2021-02-24","arxiv_id":"2102.12586","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fermi-fair-empirical-risk-minimization-via#ran","syntology_url":"https://syntology.ai/paper/2102.12586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12586"}},"official":{"repos":["optimization-for-data-driven-science/FERMI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sinkhorn-label-allocation-semi-supervised","slug":"sinkhorn-label-allocation-semi-supervised","title":"Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training","date":"2021-02-17","arxiv_id":"2102.08622","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sinkhorn-label-allocation-semi-supervised#ran","syntology_url":"https://syntology.ai/paper/2102.08622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08622"}},"official":{"repos":["stanford-futuredata/sinkhorn-label-allocation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/parameter-free-stochastic-optimization-of","slug":"parameter-free-stochastic-optimization-of","title":"Parameter-free Stochastic Optimization of Variationally Coherent Functions","date":"2021-01-30","arxiv_id":"2102.00236","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-gradient-variance-reduction-by","slug":"stochastic-gradient-variance-reduction-by","title":"Stochastic Gradient Variance Reduction by Solving a Filtering Problem","date":"2020-12-22","arxiv_id":"2012.12418","repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-history-for-byzantine-robust","slug":"learning-from-history-for-byzantine-robust","title":"Learning from History for Byzantine Robust Optimization","date":"2020-12-18","arxiv_id":"2012.10333","repositories_listed":1,"syntology":null},{"url":"/paper/non-monotone-risk-functions-for-learning","slug":"non-monotone-risk-functions-for-learning","title":"Learning with risks based on M-location","date":"2020-12-04","arxiv_id":"2012.02424","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-from-a-k-dpp-without-looking-at-all-1","slug":"sampling-from-a-k-dpp-without-looking-at-all-1","title":"Sampling from a k-DPP without looking at all items","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mixing-adam-and-sgd-a-combined-optimization","slug":"mixing-adam-and-sgd-a-combined-optimization","title":"Mixing ADAM and SGD: a Combined Optimization Method","date":"2020-11-16","arxiv_id":"2011.08042","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-batching-for-efficient-non-linear","slug":"progressive-batching-for-efficient-non-linear","title":"Progressive Batching for Efficient Non-linear Least Squares","date":"2020-10-21","arxiv_id":"2010.10968","repositories_listed":1,"syntology":null},{"url":"/paper/bi-level-score-matching-for-learning-energy","slug":"bi-level-score-matching-for-learning-energy","title":"Bi-level Score Matching for Learning Energy-based Latent Variable Models","date":"2020-10-15","arxiv_id":"2010.07856","repositories_listed":1,"syntology":null},{"url":"/paper/expectigrad-fast-stochastic-optimization-with-1","slug":"expectigrad-fast-stochastic-optimization-with-1","title":"Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties","date":"2020-10-03","arxiv_id":"2010.01356","repositories_listed":1,"syntology":null},{"url":"/paper/practical-precoding-via-asynchronous","slug":"practical-precoding-via-asynchronous","title":"Practical Precoding via Asynchronous Stochastic Successive Convex Approximation","date":"2020-10-03","arxiv_id":"2010.01360","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-multi-output-gaussian-process","slug":"generalized-multi-output-gaussian-process","title":"Generalized Multi-Output Gaussian Process Censored Regression","date":"2020-09-10","arxiv_id":"2009.04822","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-for-low-thrust","slug":"reinforcement-learning-for-low-thrust","title":"Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions","date":"2020-08-19","arxiv_id":"2008.08501","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-forests","slug":"stochastic-optimization-forests","title":"Stochastic Optimization Forests","date":"2020-08-17","arxiv_id":"2008.07473","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-optimization-forests#ran","syntology_url":"https://syntology.ai/paper/2008.07473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07473"}},"official":{"repos":["CausalML/StochOptForest"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/binary-search-and-first-order-gradient-based","slug":"binary-search-and-first-order-gradient-based","title":"Binary Search and First Order Gradient Based Method for Stochastic Optimization","date":"2020-07-27","arxiv_id":"2007.13413","repositories_listed":1,"syntology":null},{"url":"/paper/randomized-automatic-differentiation","slug":"randomized-automatic-differentiation","title":"Randomized Automatic Differentiation","date":"2020-07-20","arxiv_id":"2007.10412","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":1,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/randomized-automatic-differentiation#ran","syntology_url":"https://syntology.ai/paper/2007.10412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10412"}},"official":{"repos":["PrincetonLIPS/RandomizedAutomaticDifferentiation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sequential-quadratic-optimization-for","slug":"sequential-quadratic-optimization-for","title":"Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization","date":"2020-07-20","arxiv_id":"2007.10525","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-learning-rates-with-maximum","slug":"adaptive-learning-rates-with-maximum","title":"MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients","date":"2020-06-21","arxiv_id":"2006.11918","repositories_listed":1,"syntology":null},{"url":"/paper/a-better-alternative-to-error-feedback-for","slug":"a-better-alternative-to-error-feedback-for","title":"A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning","date":"2020-06-19","arxiv_id":"2006.11077","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-better-alternative-to-error-feedback-for#ran","syntology_url":"https://syntology.ai/paper/2006.11077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11077"}},"official":{"repos":["SamuelHorvath/Compressed_SGD_PyTorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/acmo-angle-calibrated-moment-methods-for","slug":"acmo-angle-calibrated-moment-methods-for","title":"ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization","date":"2020-06-12","arxiv_id":"2006.07065","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-for-performative","slug":"stochastic-optimization-for-performative","title":"Stochastic Optimization for Performative Prediction","date":"2020-06-12","arxiv_id":"2006.06887","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/stochastic-optimization-for-performative#ran","syntology_url":"https://syntology.ai/paper/2006.06887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06887"}},"official":{"repos":["zykls/performative-prediction"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/quadruply-stochastic-gaussian-processes","slug":"quadruply-stochastic-gaussian-processes","title":"Quadruply Stochastic Gaussian Processes","date":"2020-06-04","arxiv_id":"2006.03015","repositories_listed":1,"syntology":null},{"url":"/paper/a-modification-of-quasi-newton-s-methods","slug":"a-modification-of-quasi-newton-s-methods","title":"A fast and simple modification of Newton's method helping to avoid saddle points","date":"2020-06-02","arxiv_id":"2006.01512","repositories_listed":1,"syntology":null},{"url":"/paper/coolmomentum-a-method-for-stochastic","slug":"coolmomentum-a-method-for-stochastic","title":"CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing","date":"2020-05-29","arxiv_id":"2005.14605","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-with-heavy-tailed","slug":"stochastic-optimization-with-heavy-tailed","title":"Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping","date":"2020-05-21","arxiv_id":"2005.10785","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/stochastic-optimization-with-heavy-tailed#ran","syntology_url":"https://syntology.ai/paper/2005.10785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10785"}},"official":{"repos":["eduardgorbunov/accelerated_clipping"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/k-sums-another-side-of-k-means","slug":"k-sums-another-side-of-k-means","title":"k-sums: another side of k-means","date":"2020-05-19","arxiv_id":"2005.09485","repositories_listed":1,"syntology":null},{"url":"/paper/an-analysis-of-the-adaptation-speed-of-causal","slug":"an-analysis-of-the-adaptation-speed-of-causal","title":"An Analysis of the Adaptation Speed of Causal Models","date":"2020-05-18","arxiv_id":"2005.09136","repositories_listed":1,"syntology":null},{"url":"/paper/unbiased-mlmc-stochastic-gradient-based","slug":"unbiased-mlmc-stochastic-gradient-based","title":"Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs","date":"2020-05-18","arxiv_id":"2005.08414","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unbiased-mlmc-stochastic-gradient-based#ran","syntology_url":"https://syntology.ai/paper/2005.08414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08414"}},"official":{"repos":["Goda-Research-Group/MLMC_stochastic_gradient"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/byzantine-robust-decentralized-stochastic","slug":"byzantine-robust-decentralized-stochastic","title":"Byzantine-Robust Decentralized Stochastic Optimization over Static and Time-Varying Networks","date":"2020-05-12","arxiv_id":"2005.06276","repositories_listed":1,"syntology":null},{"url":"/paper/distributionally-robust-neural-networks","slug":"distributionally-robust-neural-networks","title":"Distributionally Robust Neural Networks","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/disjoint-principal-component-analysis-by","slug":"disjoint-principal-component-analysis-by","title":"Disjoint principal component analysis by constrained binary particle swarm optimization","date":"2020-04-22","arxiv_id":"2004.10701","repositories_listed":1,"syntology":null},{"url":"/paper/generating-tertiary-protein-structures-via-an","slug":"generating-tertiary-protein-structures-via-an","title":"Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder","date":"2020-04-08","arxiv_id":"2004.07119","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generating-tertiary-protein-structures-via-an#ran","syntology_url":"https://syntology.ai/paper/2004.07119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07119"}},"official":null}},{"url":"/paper/direct-loss-minimization-for-sparse-gaussian","slug":"direct-loss-minimization-for-sparse-gaussian","title":"Direct loss minimization algorithms for sparse Gaussian processes","date":"2020-04-07","arxiv_id":"2004.03083","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-identification-of-true-labels-for","slug":"progressive-identification-of-true-labels-for","title":"Progressive Identification of True Labels for Partial-Label Learning","date":"2020-02-19","arxiv_id":"2002.08053","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/progressive-identification-of-true-labels-for#ran","syntology_url":"https://syntology.ai/paper/2002.08053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08053"}},"official":{"repos":["Lvcrezia77/PRODEN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/polyfold-an-interactive-visual-simulator-for","slug":"polyfold-an-interactive-visual-simulator-for","title":"PolyFold: an interactive visual simulator for distance-based protein folding","date":"2020-02-14","arxiv_id":"2002.11592","repositories_listed":1,"syntology":null},{"url":"/paper/adaptivity-of-stochastic-gradient-methods-for","slug":"adaptivity-of-stochastic-gradient-methods-for","title":"Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization","date":"2020-02-13","arxiv_id":"2002.05359","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adaptivity-of-stochastic-gradient-methods-for#ran","syntology_url":"https://syntology.ai/paper/2002.05359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05359"}},"official":null}},{"url":"/paper/deep-learning-enabled-simulated-annealing-for","slug":"deep-learning-enabled-simulated-annealing-for","title":"Self-Directed Online Machine Learning for Topology Optimization","date":"2020-02-04","arxiv_id":"2002.01927","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-learning-enabled-simulated-annealing-for#ran","syntology_url":"https://syntology.ai/paper/2002.01927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.01927"}},"official":{"repos":["deng-cy/deep_learning_topology_opt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-kernel-mean-embedding-approach-to-reducing","slug":"a-kernel-mean-embedding-approach-to-reducing","title":"A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control","date":"2020-01-28","arxiv_id":"2001.10398","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-of-plain","slug":"stochastic-optimization-of-plain","title":"Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods","date":"2020-01-24","arxiv_id":"2001.08856","repositories_listed":1,"syntology":null},{"url":"/paper/cprop-adaptive-learning-rate-scaling-from","slug":"cprop-adaptive-learning-rate-scaling-from","title":"CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity","date":"2019-12-24","arxiv_id":"1912.11493","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-community-and-node","slug":"bridging-the-gap-between-community-and-node","title":"Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection","date":"2019-12-17","arxiv_id":"1912.08808","repositories_listed":1,"syntology":null},{"url":"/paper/cyanure-an-open-source-toolbox-for-empirical","slug":"cyanure-an-open-source-toolbox-for-empirical","title":"Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more","date":"2019-12-17","arxiv_id":"1912.08165","repositories_listed":1,"syntology":null},{"url":"/paper/domain-independent-dominance-of-adaptive-1","slug":"domain-independent-dominance-of-adaptive-1","title":"Domain-independent Dominance of Adaptive Methods","date":"2019-12-04","arxiv_id":"1912.01823","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-role-of-momentum-in","slug":"understanding-the-role-of-momentum-in","title":"Understanding the Role of Momentum in Stochastic Gradient Methods","date":"2019-10-30","arxiv_id":"1910.13962","repositories_listed":1,"syntology":null},{"url":"/paper/communication-censored-distributed-stochastic","slug":"communication-censored-distributed-stochastic","title":"Communication-Censored Distributed Stochastic Gradient Descent","date":"2019-09-09","arxiv_id":"1909.03631","repositories_listed":1,"syntology":null},{"url":"/paper/dp-lssgd-a-stochastic-optimization-method-to","slug":"dp-lssgd-a-stochastic-optimization-method-to","title":"DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM","date":"2019-06-28","arxiv_id":"1906.12056","repositories_listed":1,"syntology":null},{"url":"/paper/topic-modeling-via-full-dependence-mixtures","slug":"topic-modeling-via-full-dependence-mixtures","title":"Topic Modeling via Full Dependence Mixtures","date":"2019-06-13","arxiv_id":"1906.06181","repositories_listed":1,"syntology":null},{"url":"/paper/online-forecasting-of-total-variation-bounded","slug":"online-forecasting-of-total-variation-bounded","title":"Online Forecasting of Total-Variation-bounded Sequences","date":"2019-06-08","arxiv_id":"1906.03364","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-the-variance-in-online-optimization","slug":"reducing-the-variance-in-online-optimization","title":"Reducing the variance in online optimization by transporting past gradients","date":"2019-06-08","arxiv_id":"1906.03532","repositories_listed":1,"syntology":null},{"url":"/paper/a-generic-acceleration-framework-for","slug":"a-generic-acceleration-framework-for","title":"A Generic Acceleration Framework for Stochastic Composite Optimization","date":"2019-06-03","arxiv_id":"1906.01164","repositories_listed":1,"syntology":null},{"url":"/paper/190600255","slug":"190600255","title":"Data-Pooling in Stochastic Optimization","date":"2019-06-01","arxiv_id":"1906.00255","repositories_listed":1,"syntology":null},{"url":"/paper/admm-for-efficient-deep-learning-with-global","slug":"admm-for-efficient-deep-learning-with-global","title":"ADMM for Efficient Deep Learning with Global Convergence","date":"2019-05-31","arxiv_id":"1905.13611","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-uncertainty-of-loss-landscape-for","slug":"exploiting-uncertainty-of-loss-landscape-for","title":"Exploiting Uncertainty of Loss Landscape for Stochastic Optimization","date":"2019-05-30","arxiv_id":"1905.13200","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploiting-uncertainty-of-loss-landscape-for#ran","syntology_url":"https://syntology.ai/paper/1905.13200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.13200"}},"official":{"repos":["bsvineethiitg/adams"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/non-cooperative-aerial-base-station-placement","slug":"non-cooperative-aerial-base-station-placement","title":"Non-cooperative Aerial Base Station Placement via Stochastic Optimization","date":"2019-05-10","arxiv_id":"1905.03988","repositories_listed":1,"syntology":null},{"url":"/paper/190503652","slug":"190503652","title":"Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization","date":"2019-05-09","arxiv_id":"1905.03652","repositories_listed":1,"syntology":null},{"url":"/paper/the-step-decay-schedule-a-near-optimal","slug":"the-step-decay-schedule-a-near-optimal","title":"The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares","date":"2019-04-29","arxiv_id":"1904.12838","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-step-decay-schedule-a-near-optimal#ran","syntology_url":"https://syntology.ai/paper/1904.12838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12838"}},"official":{"repos":["D-X-Y/ResNeXt-DenseNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/log-barrier-constrained-cnns","slug":"log-barrier-constrained-cnns","title":"Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions","date":"2019-04-08","arxiv_id":"1904.04205","repositories_listed":1,"syntology":null},{"url":"/paper/online-variance-reduction-with-mixtures","slug":"online-variance-reduction-with-mixtures","title":"Online Variance Reduction with Mixtures","date":"2019-03-29","arxiv_id":"1903.12416","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-of-sorting-networks-1","slug":"stochastic-optimization-of-sorting-networks-1","title":"Stochastic Optimization of Sorting Networks via Continuous Relaxations","date":"2019-03-21","arxiv_id":"1903.08850","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-optimization-of-sorting-networks-1#ran","syntology_url":"https://syntology.ai/paper/1903.08850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.08850"}},"official":{"repos":["ermongroup/neuralsort"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-importance-of-better-models-in-stochastic","slug":"the-importance-of-better-models-in-stochastic","title":"The importance of better models in stochastic optimization","date":"2019-03-20","arxiv_id":"1903.08619","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/the-importance-of-better-models-in-stochastic#ran","syntology_url":"https://syntology.ai/paper/1903.08619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.08619"}},"official":null}},{"url":"/paper/deepobs-a-deep-learning-optimizer-benchmark-1","slug":"deepobs-a-deep-learning-optimizer-benchmark-1","title":"DeepOBS: A Deep Learning Optimizer Benchmark Suite","date":"2019-03-13","arxiv_id":"1903.05499","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deepobs-a-deep-learning-optimizer-benchmark-1#ran","syntology_url":"https://syntology.ai/paper/1903.05499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.05499"}},"official":{"repos":["fsschneider/deepobs"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/active-probabilistic-inference-on-matrices","slug":"active-probabilistic-inference-on-matrices","title":"Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization","date":"2019-02-20","arxiv_id":"1902.07557","repositories_listed":1,"syntology":null},{"url":"/paper/personalization-and-optimization-of-decision","slug":"personalization-and-optimization-of-decision","title":"Personalized Treatment Selection using Causal Heterogeneity","date":"2019-01-29","arxiv_id":"1901.10550","repositories_listed":1,"syntology":null},{"url":"/paper/reparameterizable-subset-sampling-via","slug":"reparameterizable-subset-sampling-via","title":"Reparameterizable Subset Sampling via Continuous Relaxations","date":"2019-01-29","arxiv_id":"1901.10517","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-conditional-gradient-method-for","slug":"stochastic-conditional-gradient-method-for","title":"Stochastic Frank-Wolfe for Composite Convex Minimization","date":"2019-01-29","arxiv_id":"1901.10348","repositories_listed":1,"syntology":null},{"url":"/paper/dadam-a-consensus-based-distributed-adaptive","slug":"dadam-a-consensus-based-distributed-adaptive","title":"DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization","date":"2019-01-25","arxiv_id":"1901.09109","repositories_listed":1,"syntology":null},{"url":"/paper/surrogate-losses-for-online-learning-of","slug":"surrogate-losses-for-online-learning-of","title":"Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization","date":"2019-01-25","arxiv_id":"1901.09068","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-methods-for-nonconvex-optimization","slug":"adaptive-methods-for-nonconvex-optimization","title":"Adaptive Methods for Nonconvex Optimization","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scalable-robust-kidney-exchange","slug":"scalable-robust-kidney-exchange","title":"Scalable Robust Kidney Exchange","date":"2018-11-08","arxiv_id":"1811.03532","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/scalable-robust-kidney-exchange#ran","syntology_url":"https://syntology.ai/paper/1811.03532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.03532"}},"official":{"repos":["duncanmcelfresh/RobustKidneyExchange"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/kalman-gradient-descent-adaptive-variance","slug":"kalman-gradient-descent-adaptive-variance","title":"Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization","date":"2018-10-29","arxiv_id":"1810.12273","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-time-models-for-stochastic","slug":"continuous-time-models-for-stochastic","title":"Continuous-time Models for Stochastic Optimization Algorithms","date":"2018-10-05","arxiv_id":"1810.02565","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-antithetic-sampling-for","slug":"differentiable-antithetic-sampling-for","title":"Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference","date":"2018-10-05","arxiv_id":"1810.02555","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-adaptive-and-accelerated-stochastic","slug":"optimal-adaptive-and-accelerated-stochastic","title":"Optimal Adaptive and Accelerated Stochastic Gradient Descent","date":"2018-10-01","arxiv_id":"1810.00553","repositories_listed":1,"syntology":null},{"url":"/paper/riemannian-adaptive-optimization-methods","slug":"riemannian-adaptive-optimization-methods","title":"Riemannian Adaptive Optimization Methods","date":"2018-10-01","arxiv_id":"1810.00760","repositories_listed":1,"syntology":null}],"record_sha256":"4e84c4936c1ebec889ce4d0f4a8d28265275ca5b5854c5232593905e3ff938f9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}