{"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/regression-1/papers/20","list_of":"/task/regression-1","task":"regression","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":20,"pages_in_order":95,"rows_per_page":100,"rows":[1901,2000],"of":9424,"counts":{"archive_papers_tagged":9424,"with_a_code_link":2445,"where_syntology_ran_a_sample":449,"not_listed_spam_title":0,"listed":9424,"listed_where_code_ran":449,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":374,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":374,"listed_every_run_a_failure_of_syntologys_instrument":75,"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/regression-1","prev":"/task/regression-1/papers/19","next":"/task/regression-1/papers/21","papers":[{"url":"/paper/dense-regression-network-for-video-grounding","slug":"dense-regression-network-for-video-grounding","title":"Dense Regression Network for Video Grounding","date":"2020-04-07","arxiv_id":"2004.03545","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-differences-between-song-and-speech","slug":"on-the-differences-between-song-and-speech","title":"On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers","date":"2020-04-01","arxiv_id":"2004.00200","repositories_listed":1,"syntology":null},{"url":"/paper/eolo-embedded-object-segmentation-only-look","slug":"eolo-embedded-object-segmentation-only-look","title":"EOLO: Embedded Object Segmentation only Look Once","date":"2020-03-31","arxiv_id":"2004.00123","repositories_listed":1,"syntology":null},{"url":"/paper/random-machines-regression-approach-an","slug":"random-machines-regression-approach-an","title":"Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice","date":"2020-03-27","arxiv_id":"2003.12643","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-algorithms-for-multidimensional","slug":"efficient-algorithms-for-multidimensional","title":"Efficient Algorithms for Multidimensional Segmented Regression","date":"2020-03-24","arxiv_id":"2003.11086","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-error-and-correlation-based","slug":"evaluation-of-error-and-correlation-based","title":"Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition","date":"2020-03-24","arxiv_id":"2003.10724","repositories_listed":1,"syntology":null},{"url":"/paper/boosttree-and-boostforest-for-ensemble","slug":"boosttree-and-boostforest-for-ensemble","title":"BoostTree and BoostForest for Ensemble Learning","date":"2020-03-21","arxiv_id":"2003.09737","repositories_listed":1,"syntology":null},{"url":"/paper/distnet-deep-tracking-by-displacement","slug":"distnet-deep-tracking-by-displacement","title":"DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine","date":"2020-03-17","arxiv_id":"2003.07790","repositories_listed":1,"syntology":null},{"url":"/paper/linear-regression-without-correspondences-via","slug":"linear-regression-without-correspondences-via","title":"Linear Regression without Correspondences via Concave Minimization","date":"2020-03-17","arxiv_id":"2003.07706","repositories_listed":1,"syntology":null},{"url":"/paper/experimental-comparison-of-semi-parametric","slug":"experimental-comparison-of-semi-parametric","title":"Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index","date":"2020-03-13","arxiv_id":"2003.08820","repositories_listed":1,"syntology":null},{"url":"/paper/causal-interaction-trees-tree-based-subgroup","slug":"causal-interaction-trees-tree-based-subgroup","title":"Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data","date":"2020-03-06","arxiv_id":"2003.03042","repositories_listed":1,"syntology":null},{"url":"/paper/event-based-angular-velocity-regression-with","slug":"event-based-angular-velocity-regression-with","title":"Event-Based Angular Velocity Regression with Spiking Networks","date":"2020-03-05","arxiv_id":"2003.02790","repositories_listed":1,"syntology":null},{"url":"/paper/flexible-bayesian-nonlinear-model","slug":"flexible-bayesian-nonlinear-model","title":"Flexible Bayesian Nonlinear Model Configuration","date":"2020-03-05","arxiv_id":"2003.02929","repositories_listed":1,"syntology":null},{"url":"/paper/pac-bayesian-meta-learning-with-implicit","slug":"pac-bayesian-meta-learning-with-implicit","title":"PAC-Bayes meta-learning with implicit task-specific posteriors","date":"2020-03-05","arxiv_id":"2003.02455","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/pac-bayesian-meta-learning-with-implicit#ran","syntology_url":"https://syntology.ai/paper/2003.02455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02455"}},"official":{"repos":["cnguyen10/few_shot_meta_learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sleipnir-deterministic-and-provably-accurate","slug":"sleipnir-deterministic-and-provably-accurate","title":"SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives","date":"2020-03-05","arxiv_id":"2003.02658","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/sleipnir-deterministic-and-provably-accurate#ran","syntology_url":"https://syntology.ai/paper/2003.02658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02658"}},"official":{"repos":["sdi1100041/SLEIPNIR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/regression-via-implicit-models-and-optimal","slug":"regression-via-implicit-models-and-optimal","title":"Regression via Implicit Models and Optimal Transport Cost Minimization","date":"2020-03-03","arxiv_id":"2003.01296","repositories_listed":1,"syntology":null},{"url":"/paper/ml4chem-a-machine-learning-package-for","slug":"ml4chem-a-machine-learning-package-for","title":"ML4Chem: A Machine Learning Package for Chemistry and Materials Science","date":"2020-03-02","arxiv_id":"2003.13388","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-framework-for-ensemble-distribution","slug":"a-general-framework-for-ensemble-distribution","title":"A general framework for ensemble distribution distillation","date":"2020-02-26","arxiv_id":"2002.11531","repositories_listed":1,"syntology":null},{"url":"/paper/provable-meta-learning-of-linear","slug":"provable-meta-learning-of-linear","title":"Provable Meta-Learning of Linear Representations","date":"2020-02-26","arxiv_id":"2002.11684","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-and-mitigating-the-tradeoff","slug":"understanding-and-mitigating-the-tradeoff","title":"Understanding and Mitigating the Tradeoff Between Robustness and Accuracy","date":"2020-02-25","arxiv_id":"2002.10716","repositories_listed":1,"syntology":null},{"url":"/paper/affective-expression-analysis-in-the-wild","slug":"affective-expression-analysis-in-the-wild","title":"Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model","date":"2020-02-21","arxiv_id":"2002.09120","repositories_listed":1,"syntology":null},{"url":"/paper/fast-local-linear-regression-with-anchor","slug":"fast-local-linear-regression-with-anchor","title":"Fast local linear regression with anchor regularization","date":"2020-02-21","arxiv_id":"2003.05747","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-principal-component-regression-via","slug":"sparse-principal-component-regression-via","title":"Sparse principal component regression via singular value decomposition approach","date":"2020-02-21","arxiv_id":"2002.09188","repositories_listed":1,"syntology":null},{"url":"/paper/deep-regularization-and-direct-training-of","slug":"deep-regularization-and-direct-training-of","title":"Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows","date":"2020-02-19","arxiv_id":"2002.08335","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-foreign-exchange-rate-a","slug":"forecasting-foreign-exchange-rate-a","title":"Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques","date":"2020-02-19","arxiv_id":"2002.10247","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-boosting-neural-networks-grownet","slug":"gradient-boosting-neural-networks-grownet","title":"Gradient Boosting Neural Networks: GrowNet","date":"2020-02-19","arxiv_id":"2002.07971","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gradient-boosting-neural-networks-grownet#ran","syntology_url":"https://syntology.ai/paper/2002.07971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.07971"}},"official":{"repos":["sbadirli/GrowNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/large-scale-biometry-with-interpretable","slug":"large-scale-biometry-with-interpretable","title":"Large-scale biometry with interpretable neural network regression on UK Biobank body MRI","date":"2020-02-17","arxiv_id":"2002.06862","repositories_listed":1,"syntology":null},{"url":"/paper/fast-fair-regression-via-efficient","slug":"fast-fair-regression-via-efficient","title":"Fast Fair Regression via Efficient Approximations of Mutual Information","date":"2020-02-14","arxiv_id":"2002.06200","repositories_listed":1,"syntology":null},{"url":"/paper/high-performance-logistic-regression-for","slug":"high-performance-logistic-regression-for","title":"High Performance Logistic Regression for Privacy-Preserving Genome Analysis","date":"2020-02-13","arxiv_id":"2002.05377","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-uncertainty-intervals-from","slug":"estimating-uncertainty-intervals-from","title":"Estimating Uncertainty Intervals from Collaborating Networks","date":"2020-02-12","arxiv_id":"2002.05212","repositories_listed":1,"syntology":null},{"url":"/paper/cyclic-boosting-an-explainable-supervised","slug":"cyclic-boosting-an-explainable-supervised","title":"Cyclic Boosting -- an explainable supervised machine learning algorithm","date":"2020-02-09","arxiv_id":"2002.03425","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-tree-ensembles-for-regularized","slug":"stochastic-tree-ensembles-for-regularized","title":"Stochastic tree ensembles for regularized nonlinear regression","date":"2020-02-09","arxiv_id":"2002.03375","repositories_listed":1,"syntology":null},{"url":"/paper/multi-source-deep-gaussian-process-kernel","slug":"multi-source-deep-gaussian-process-kernel","title":"Conditional Deep Gaussian Processes: multi-fidelity kernel learning","date":"2020-02-07","arxiv_id":"2002.02826","repositories_listed":1,"syntology":null},{"url":"/paper/spectrum-dependent-learning-curves-in-kernel","slug":"spectrum-dependent-learning-curves-in-kernel","title":"Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks","date":"2020-02-07","arxiv_id":"2002.02561","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/spectrum-dependent-learning-curves-in-kernel#ran","syntology_url":"https://syntology.ai/paper/2002.02561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02561"}},"official":{"repos":["Pehlevan-Group/NTK_Learning_Curves"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-boosting-for-regression-problems","slug":"robust-boosting-for-regression-problems","title":"Robust Boosting for Regression Problems","date":"2020-02-06","arxiv_id":"2002.02054","repositories_listed":1,"syntology":null},{"url":"/paper/a-deterministic-streaming-sketch-for-ridge","slug":"a-deterministic-streaming-sketch-for-ridge","title":"A Deterministic Streaming Sketch for Ridge Regression","date":"2020-02-05","arxiv_id":"2002.02013","repositories_listed":1,"syntology":null},{"url":"/paper/overfitting-can-be-harmless-for-basis-pursuit","slug":"overfitting-can-be-harmless-for-basis-pursuit","title":"Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree","date":"2020-02-02","arxiv_id":"2002.00492","repositories_listed":1,"syntology":null},{"url":"/paper/a-sparsity-inducing-nuclear-norm-estimator","slug":"a-sparsity-inducing-nuclear-norm-estimator","title":"A Sparsity Inducing Nuclear-Norm Estimator (SpINNEr) for Matrix-Variate Regression in Brain Connectivity Analysis","date":"2020-01-30","arxiv_id":"2001.11548","repositories_listed":1,"syntology":null},{"url":"/paper/improving-generalizability-of-fake-news","slug":"improving-generalizability-of-fake-news","title":"Improving Generalizability of Fake News Detection Methods using Propensity Score Matching","date":"2020-01-28","arxiv_id":"2002.00838","repositories_listed":1,"syntology":null},{"url":"/paper/an-optimized-pipeline-for-functional","slug":"an-optimized-pipeline-for-functional","title":"An optimized pipeline for functional connectivity analysis in the rat brain","date":"2020-01-27","arxiv_id":"2001.09857","repositories_listed":1,"syntology":null},{"url":"/paper/6d-object-pose-regression-via-supervised","slug":"6d-object-pose-regression-via-supervised","title":"6D Object Pose Regression via Supervised Learning on Point Clouds","date":"2020-01-24","arxiv_id":"2001.08942","repositories_listed":1,"syntology":null},{"url":"/paper/the-reciprocal-bayesian-lasso","slug":"the-reciprocal-bayesian-lasso","title":"The Reciprocal Bayesian LASSO","date":"2020-01-23","arxiv_id":"2001.08327","repositories_listed":1,"syntology":null},{"url":"/paper/partially-shared-variational-auto-encoders","slug":"partially-shared-variational-auto-encoders","title":"Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift","date":"2020-01-22","arxiv_id":"2001.07895","repositories_listed":1,"syntology":null},{"url":"/paper/generalization-of-change-point-detection-in","slug":"generalization-of-change-point-detection-in","title":"Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation","date":"2020-01-17","arxiv_id":"2001.06386","repositories_listed":1,"syntology":null},{"url":"/paper/recovering-network-structure-from-aggregated","slug":"recovering-network-structure-from-aggregated","title":"Recovering Network Structure from Aggregated Relational Data using Penalized Regression","date":"2020-01-16","arxiv_id":"2001.06052","repositories_listed":1,"syntology":null},{"url":"/paper/learning-overlapping-representations-for-the","slug":"learning-overlapping-representations-for-the","title":"Learning Overlapping Representations for the Estimation of Individualized Treatment Effects","date":"2020-01-14","arxiv_id":"2001.04754","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-pool-based-active-learning-for","slug":"unsupervised-pool-based-active-learning-for","title":"Unsupervised Pool-Based Active Learning for Linear Regression","date":"2020-01-14","arxiv_id":"2001.05028","repositories_listed":1,"syntology":null},{"url":"/paper/radial-based-analysis-of-grnn-in-non-textured","slug":"radial-based-analysis-of-grnn-in-non-textured","title":"Radial Based Analysis of GRNN in Non-Textured Image Inpainting","date":"2020-01-13","arxiv_id":"2001.04215","repositories_listed":1,"syntology":null},{"url":"/paper/choosing-the-sample-with-lowest-loss-makes","slug":"choosing-the-sample-with-lowest-loss-makes","title":"Choosing the Sample with Lowest Loss makes SGD Robust","date":"2020-01-10","arxiv_id":"2001.03316","repositories_listed":1,"syntology":null},{"url":"/paper/classical-and-quantum-regression-analysis-for","slug":"classical-and-quantum-regression-analysis-for","title":"Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode","date":"2020-01-02","arxiv_id":"2004.01257","repositories_listed":1,"syntology":null},{"url":"/paper/quantile-causal-discovery","slug":"quantile-causal-discovery","title":"Quantile Causal Discovery","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/approximate-inference-for-fully-bayesian","slug":"approximate-inference-for-fully-bayesian","title":"Approximate Inference for Fully Bayesian Gaussian Process Regression","date":"2019-12-31","arxiv_id":"1912.13440","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/approximate-inference-for-fully-bayesian#ran","syntology_url":"https://syntology.ai/paper/1912.13440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.13440"}},"official":null}},{"url":"/paper/randomly-projected-additive-gaussian","slug":"randomly-projected-additive-gaussian","title":"Randomly Projected Additive Gaussian Processes for Regression","date":"2019-12-30","arxiv_id":"1912.12834","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/randomly-projected-additive-gaussian#ran","syntology_url":"https://syntology.ai/paper/1912.12834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.12834"}},"official":{"repos":["idelbrid/Randomly-Projected-Additive-GPs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/communication-efficient-integrative","slug":"communication-efficient-integrative","title":"Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions","date":"2019-12-26","arxiv_id":"1912.11928","repositories_listed":1,"syntology":null},{"url":"/paper/occer-one-class-classification-by-ensembles","slug":"occer-one-class-classification-by-ensembles","title":"One-Class Classification by Ensembles of Regression models -- a detailed study","date":"2019-12-26","arxiv_id":"1912.11475","repositories_listed":1,"syntology":null},{"url":"/paper/one-sh-ot-decision-making-with-and-without","slug":"one-sh-ot-decision-making-with-and-without","title":"One-Shot Decision-Making with and without Surrogates","date":"2019-12-19","arxiv_id":"1912.08956","repositories_listed":1,"syntology":null},{"url":"/paper/heteroscedastic-gaussian-process-regression","slug":"heteroscedastic-gaussian-process-regression","title":"Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures","date":"2019-12-18","arxiv_id":"1912.08843","repositories_listed":1,"syntology":null},{"url":"/paper/extrinsic-kernel-ridge-regression-classifier","slug":"extrinsic-kernel-ridge-regression-classifier","title":"Extrinsic Kernel Ridge Regression Classifier for Planar Kendall Shape Space","date":"2019-12-17","arxiv_id":"1912.08202","repositories_listed":1,"syntology":null},{"url":"/paper/kernel-based-ensemble-learning-in-python","slug":"kernel-based-ensemble-learning-in-python","title":"Kernel-Based Ensemble Learning in Python","date":"2019-12-17","arxiv_id":"1912.08311","repositories_listed":1,"syntology":null},{"url":"/paper/more-data-can-hurt-for-linear-regression","slug":"more-data-can-hurt-for-linear-regression","title":"More Data Can Hurt for Linear Regression: Sample-wise Double Descent","date":"2019-12-16","arxiv_id":"1912.07242","repositories_listed":1,"syntology":null},{"url":"/paper/estimation-and-hac-based-inference-for","slug":"estimation-and-hac-based-inference-for","title":"High-Dimensional Granger Causality Tests with an Application to VIX and News","date":"2019-12-13","arxiv_id":"1912.06307","repositories_listed":1,"syntology":null},{"url":"/paper/inferring-distributions-over-depth-from-a","slug":"inferring-distributions-over-depth-from-a","title":"Inferring Distributions Over Depth from a Single Image","date":"2019-12-12","arxiv_id":"1912.06268","repositories_listed":1,"syntology":null},{"url":"/paper/deep-symbolic-regression-recovering","slug":"deep-symbolic-regression-recovering","title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients","date":"2019-12-10","arxiv_id":"1912.04871","repositories_listed":1,"syntology":null},{"url":"/paper/integration-of-neural-network-based-symbolic","slug":"integration-of-neural-network-based-symbolic","title":"Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery","date":"2019-12-10","arxiv_id":"1912.04825","repositories_listed":1,"syntology":null},{"url":"/paper/dually-supervised-feature-pyramid-for-object","slug":"dually-supervised-feature-pyramid-for-object","title":"Dually Supervised Feature Pyramid for Object Detection and Segmentation","date":"2019-12-08","arxiv_id":"1912.03730","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-2019-portuguese-parliament-election","slug":"real-time-2019-portuguese-parliament-election","title":"Real-time 2019 Portuguese Parliament Election Results Dataset","date":"2019-12-05","arxiv_id":"1912.08922","repositories_listed":1,"syntology":null},{"url":"/paper/screening-data-points-in-empirical-risk","slug":"screening-data-points-in-empirical-risk","title":"Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions","date":"2019-12-05","arxiv_id":"1912.02566","repositories_listed":1,"syntology":null},{"url":"/paper/mixture-dense-regression-for-object-detection","slug":"mixture-dense-regression-for-object-detection","title":"Mixture Dense Regression for Object Detection and Human Pose Estimation","date":"2019-12-02","arxiv_id":"1912.00821","repositories_listed":1,"syntology":null},{"url":"/paper/a-first-order-algorithmic-framework-for-1","slug":"a-first-order-algorithmic-framework-for-1","title":"A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/globally-optimal-learning-for-structured","slug":"globally-optimal-learning-for-structured","title":"Globally Optimal Learning for Structured Elliptical Losses","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-semi-supervised-learning-with-non","slug":"graph-based-semi-supervised-learning-with-non","title":"Graph-Based Semi-Supervised Learning with Non-ignorable Non-response","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/partitioning-structure-learning-for-segmented","slug":"partitioning-structure-learning-for-segmented","title":"Partitioning Structure Learning for Segmented Linear Regression Trees","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/selecting-optimal-decisions-via","slug":"selecting-optimal-decisions-via","title":"Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-precise-end-to-end-weakly-supervised-1","slug":"towards-precise-end-to-end-weakly-supervised-1","title":"Towards Precise End-to-end Weakly Supervised Object Detection Network","date":"2019-11-27","arxiv_id":"1911.12148","repositories_listed":1,"syntology":null},{"url":"/paper/causally-denoise-word-embeddings-using-half","slug":"causally-denoise-word-embeddings-using-half","title":"Causally Denoise Word Embeddings Using Half-Sibling Regression","date":"2019-11-24","arxiv_id":"1911.10524","repositories_listed":1,"syntology":null},{"url":"/paper/actively-learning-gaussian-process-dynamics","slug":"actively-learning-gaussian-process-dynamics","title":"Actively Learning Gaussian Process Dynamics","date":"2019-11-22","arxiv_id":"1911.09946","repositories_listed":1,"syntology":null},{"url":"/paper/random-machines-a-bagged-weighted-support","slug":"random-machines-a-bagged-weighted-support","title":"Random Machines: A bagged-weighted support vector model with free kernel choice","date":"2019-11-21","arxiv_id":"1911.09411","repositories_listed":1,"syntology":null},{"url":"/paper/solar-event-tracking-with-deep-regression","slug":"solar-event-tracking-with-deep-regression","title":"Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation","date":"2019-11-19","arxiv_id":"1911.08350","repositories_listed":1,"syntology":null},{"url":"/paper/the-secret-revealer-generative-model-1","slug":"the-secret-revealer-generative-model-1","title":"The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks","date":"2019-11-17","arxiv_id":"1911.07135","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/the-secret-revealer-generative-model-1#ran","syntology_url":"https://syntology.ai/paper/1911.07135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.07135"}},"official":{"repos":["AI-secure/GMI-Attack"],"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/causal-inference-using-bayesian-non","slug":"causal-inference-using-bayesian-non","title":"Bayesian nonparametric discontinuity design","date":"2019-11-15","arxiv_id":"1911.06722","repositories_listed":1,"syntology":null},{"url":"/paper/imputing-missing-values-with-unsupervised","slug":"imputing-missing-values-with-unsupervised","title":"Imputing missing values with unsupervised random trees","date":"2019-11-15","arxiv_id":"1911.06646","repositories_listed":1,"syntology":null},{"url":"/paper/regression-via-arbitrary-quantile-modeling","slug":"regression-via-arbitrary-quantile-modeling","title":"Regression via Arbitrary Quantile Modeling","date":"2019-11-13","arxiv_id":"1911.05441","repositories_listed":1,"syntology":null},{"url":"/paper/tsk-streams-learning-tsk-fuzzy-systems-on","slug":"tsk-streams-learning-tsk-fuzzy-systems-on","title":"TSK-Streams: Learning TSK Fuzzy Systems on Data Streams","date":"2019-11-10","arxiv_id":"1911.03951","repositories_listed":1,"syntology":null},{"url":"/paper/how-implicit-regularization-of-neural","slug":"how-implicit-regularization-of-neural","title":"How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer","date":"2019-11-07","arxiv_id":"1911.02903","repositories_listed":1,"syntology":null},{"url":"/paper/linear-support-vector-regression-with-linear","slug":"linear-support-vector-regression-with-linear","title":"Linear Support Vector Regression with Linear Constraints","date":"2019-11-06","arxiv_id":"1911.02306","repositories_listed":1,"syntology":null},{"url":"/paper/randomization-as-regularization-a-degrees-of","slug":"randomization-as-regularization-a-degrees-of","title":"Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success","date":"2019-11-01","arxiv_id":"1911.00190","repositories_listed":1,"syntology":null},{"url":"/paper/chirality-nets-for-human-pose-regression","slug":"chirality-nets-for-human-pose-regression","title":"Chirality Nets for Human Pose Regression","date":"2019-10-31","arxiv_id":"1911.00029","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/chirality-nets-for-human-pose-regression#ran","syntology_url":"https://syntology.ai/paper/1911.00029","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00029"}},"official":null}},{"url":"/paper/scaling-structural-learning-with-no-bears-to","slug":"scaling-structural-learning-with-no-bears-to","title":"Scaling structural learning with NO-BEARS to infer causal transcriptome networks","date":"2019-10-31","arxiv_id":"1911.00081","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scaling-structural-learning-with-no-bears-to#ran","syntology_url":"https://syntology.ai/paper/1911.00081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00081"}},"official":{"repos":["howchihlee/BNGPU"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/energystar-towards-more-accurate-and","slug":"energystar-towards-more-accurate-and","title":"EnergyStar++: Towards more accurate and explanatory building energy benchmarking","date":"2019-10-30","arxiv_id":"1910.14563","repositories_listed":1,"syntology":null},{"url":"/paper/fast-acoustic-scattering-using-convolutional","slug":"fast-acoustic-scattering-using-convolutional","title":"Fast acoustic scattering using convolutional neural networks","date":"2019-10-30","arxiv_id":"1911.01802","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-hessian-sketch-in-input-sparsity","slug":"iterative-hessian-sketch-in-input-sparsity","title":"Iterative Hessian Sketch in Input Sparsity Time","date":"2019-10-30","arxiv_id":"1910.14166","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-bayesian-linear","slug":"differentially-private-bayesian-linear","title":"Differentially Private Bayesian Linear Regression","date":"2019-10-29","arxiv_id":"1910.13153","repositories_listed":1,"syntology":null},{"url":"/paper/a-first-order-algorithmic-framework-for","slug":"a-first-order-algorithmic-framework-for","title":"A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression","date":"2019-10-28","arxiv_id":"1910.12778","repositories_listed":1,"syntology":null},{"url":"/paper/missing-not-at-random-in-matrix-completion","slug":"missing-not-at-random-in-matrix-completion","title":"Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption","date":"2019-10-28","arxiv_id":"1910.12774","repositories_listed":1,"syntology":null},{"url":"/paper/dual-iv-a-single-stage-instrumental-variable","slug":"dual-iv-a-single-stage-instrumental-variable","title":"Dual Instrumental Variable Regression","date":"2019-10-27","arxiv_id":"1910.12358","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":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) · 2 unverified","sample_list":"/paper/dual-iv-a-single-stage-instrumental-variable#ran","syntology_url":"https://syntology.ai/paper/1910.12358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12358"}},"official":{"repos":["krikamol/DualIV-NeurIPS2020"],"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/modelling-heterogeneous-distributions-with-an","slug":"modelling-heterogeneous-distributions-with-an","title":"Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians","date":"2019-10-27","arxiv_id":"1910.12288","repositories_listed":1,"syntology":null},{"url":"/paper/the-quo-vadis-submission-at-traffic4cast-2019","slug":"the-quo-vadis-submission-at-traffic4cast-2019","title":"The Quo Vadis submission at Traffic4cast 2019","date":"2019-10-27","arxiv_id":"1910.12363","repositories_listed":1,"syntology":null},{"url":"/paper/high-dimensional-regression-for-regenerative","slug":"high-dimensional-regression-for-regenerative","title":"High dimensional regression for regenerative time-series: an application to road traffic modeling","date":"2019-10-24","arxiv_id":"1910.11095","repositories_listed":1,"syntology":null},{"url":"/paper/nested-conformal-prediction-and-the","slug":"nested-conformal-prediction-and-the","title":"Nested conformal prediction and quantile out-of-bag ensemble methods","date":"2019-10-23","arxiv_id":"1910.10562","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-orthogonal-variational-inference-for","slug":"sparse-orthogonal-variational-inference-for","title":"Sparse Orthogonal Variational Inference for Gaussian Processes","date":"2019-10-23","arxiv_id":"1910.10596","repositories_listed":1,"syntology":null}],"record_sha256":"3eed9f6c2ff8adf120f073f98d11c98bd3be088ca7919d0dae5083f72e1f5b34","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}