{"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/72","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":72,"pages_in_order":95,"rows_per_page":100,"rows":[7101,7200],"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/71","next":"/task/regression-1/papers/73","papers":[{"url":null,"slug":"direct-quantification-for-coronary-artery","title":"Direct Quantification for Coronary Artery Stenosis Using Multiview Learning","date":"2019-07-20","arxiv_id":"1907.10032","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-quantile-regression","title":"Fair quantile regression","date":"2019-07-19","arxiv_id":"1907.08646","repositories_listed":0,"syntology":null},{"url":null,"slug":"least-angle-regression-in-tangent-space-and","title":"Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models","date":"2019-07-18","arxiv_id":"1907.08100","repositories_listed":0,"syntology":null},{"url":null,"slug":"output-weighted-optimal-sampling-for-bayesian","title":"Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples","date":"2019-07-17","arxiv_id":"1907.07552","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-data-driven-discovery-of-governing","title":"SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations","date":"2019-07-17","arxiv_id":"1907.07788","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-stage-approach-to-multivariate-linear","title":"A Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data","date":"2019-07-16","arxiv_id":"1907.07148","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-assessment-of-kron-reduction-in","title":"Performance Assessment of Kron Reduction in the Numerical Analysis of Polyphase Power Systems","date":"2019-07-16","arxiv_id":"1907.06930","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-concept-representation-learning-from-1","title":"Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment","date":"2019-07-15","arxiv_id":"1907.06600","repositories_listed":0,"syntology":null},{"url":null,"slug":"haar-transforms-for-graph-neural-networks","title":"Fast Haar Transforms for Graph Neural Networks","date":"2019-07-10","arxiv_id":"1907.04786","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathrank-a-multi-task-learning-framework-to","title":"PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks","date":"2019-07-09","arxiv_id":"1907.04028","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrap-model-ensemble-and-rank-loss-for","title":"Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression","date":"2019-07-08","arxiv_id":"1907.03422","repositories_listed":0,"syntology":null},{"url":null,"slug":"copula-representations-and-error-surface","title":"Copula Representations and Error Surface Projections for the Exclusive Or Problem","date":"2019-07-08","arxiv_id":"1907.04483","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-estimators-for-random-design","title":"Unbiased estimators for random design regression","date":"2019-07-08","arxiv_id":"1907.03411","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-location-parameters-in-entangled","title":"Estimating location parameters in entangled single-sample distributions","date":"2019-07-06","arxiv_id":"1907.03087","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-architecture-search-with","title":"Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression","date":"2019-07-03","arxiv_id":"1907.01939","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-pricing-in-insurance-generalized","title":"Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches","date":"2019-07-02","arxiv_id":"1907.05381","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attention-modeling-for-multi","title":"Adversarial Attention Modeling for Multi-dimensional Emotion Regression","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-financial","title":"Convolutional Neural Networks for Financial Text Regression","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"propagation-of-emotions-arousal-and-polarity","title":"Propagation of emotions, arousal and polarity in WordNet using Heterogeneous Structured Synset Embeddings","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"single-document-summarization-using-sentence","title":"Single-Document Summarization Using Sentence Embeddings and K-Means Clustering","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-deep-learning-to-predict-plant-growth","title":"Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments","date":"2019-07-01","arxiv_id":"1907.00624","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-regularized-piecewise-linear","title":"Efficient Regularized Piecewise-Linear Regression Trees","date":"2019-06-29","arxiv_id":"1907.00275","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-identify-patients-at-risk-of","title":"Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data","date":"2019-06-28","arxiv_id":"1907.00089","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-semi-parametric-model-learning","title":"Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics","date":"2019-06-27","arxiv_id":"1906.11909","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantile-regression-deep-reinforcement","title":"Learning Policies through Quantile Regression","date":"2019-06-27","arxiv_id":"1906.11941","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-global-approach-for-learning-sparse-ising","title":"A global approach for learning sparse Ising models","date":"2019-06-26","arxiv_id":"1906.11641","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-discovery-of-families-of-network","title":"Automatic Discovery of Families of Network Generative Processes","date":"2019-06-26","arxiv_id":"1906.12332","repositories_listed":0,"syntology":null},{"url":null,"slug":"benign-overfitting-in-linear-regression","title":"Benign Overfitting in Linear Regression","date":"2019-06-26","arxiv_id":"1906.11300","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-apartment-rent-price","title":"A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN","date":"2019-06-25","arxiv_id":"1906.11099","repositories_listed":0,"syntology":null},{"url":null,"slug":"keep-soft-robots-soft-a-data-driven-based","title":"Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control","date":"2019-06-25","arxiv_id":"1906.10489","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-regression-model-for-seed","title":"A Deep Regression Model for Seed Identification in Prostate Brachytherapy","date":"2019-06-24","arxiv_id":"1906.10183","repositories_listed":0,"syntology":null},{"url":null,"slug":"best-split-nodes-for-regression-trees","title":"Analyzing CART","date":"2019-06-24","arxiv_id":"1906.10086","repositories_listed":0,"syntology":null},{"url":null,"slug":"coral8-concurrent-object-regression-for-area","title":"CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels","date":"2019-06-24","arxiv_id":"1906.09676","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-of-uncertainty-quantification-for","title":"Quality of Uncertainty Quantification for Bayesian Neural Network Inference","date":"2019-06-24","arxiv_id":"1906.09686","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-support-vector-regression-based-multi","title":"A support vector regression-based multi-fidelity surrogate model","date":"2019-06-22","arxiv_id":"1906.09439","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-random-projections","title":"Asymmetric Random Projections","date":"2019-06-22","arxiv_id":"1906.09489","repositories_listed":0,"syntology":null},{"url":null,"slug":"max-affine-regression-provable-tractable-and","title":"Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation","date":"2019-06-21","arxiv_id":"1906.09255","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-linear-regression","title":"Online A-Optimal Design and Active Linear Regression","date":"2019-06-20","arxiv_id":"1906.08509","repositories_listed":0,"syntology":null},{"url":null,"slug":"lets-take-this-online-adapting-scene","title":"Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation","date":"2019-06-20","arxiv_id":"1906.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-action-recognition-network-for-specific","title":"An Action Recognition network for specific target based on rMC and RPN","date":"2019-06-19","arxiv_id":"1906.07944","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inverse-regression-for-supervised","title":"Bayesian inverse regression for dimension reduction with small datasets","date":"2019-06-19","arxiv_id":"1906.08018","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-logistic-learning-based-on","title":"Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models","date":"2019-06-19","arxiv_id":"1906.07882","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-selection-for-high-dimensional-linear","title":"Model selection for high-dimensional linear regression with dependent observations","date":"2019-06-18","arxiv_id":"1906.07395","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attention-guided-deep-regression-model-for","title":"An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms","date":"2019-06-17","arxiv_id":"1906.07549","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-correcting-algorithms-for-sparsely","title":"Error Correcting Algorithms for Sparsely Correlated Regressors","date":"2019-06-17","arxiv_id":"1906.07291","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-unsupervised-pre-training-and","title":"Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish","date":"2019-06-17","arxiv_id":"1906.09325","repositories_listed":0,"syntology":null},{"url":"/paper/efficient-and-accurate-face-alignment-by","slug":"efficient-and-accurate-face-alignment-by","title":"Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement","date":"2019-06-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"agriculture-commodity-arrival-prediction","title":"Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond","date":"2019-06-14","arxiv_id":"1906.07573","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectrally-truncated-kernel-ridge-regression","title":"Spectrally-truncated kernel ridge regression and its free lunch","date":"2019-06-14","arxiv_id":"1906.06276","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-high-dimensional-regression-under","title":"Distributed High-dimensional Regression Under a Quantile Loss Function","date":"2019-06-13","arxiv_id":"1906.05741","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-distillation-learning","title":"Interpretable Few-Shot Learning via Linear Distillation","date":"2019-06-13","arxiv_id":"1906.05431","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-regression-for-safe-exploration-in","title":"Robust Regression for Safe Exploration in Control","date":"2019-06-13","arxiv_id":"1906.05819","repositories_listed":0,"syntology":null},{"url":null,"slug":"variance-estimation-for-online-regression-via","title":"Finite Sample Analysis Of Dynamic Regression Parameter Learning","date":"2019-06-13","arxiv_id":"1906.05591","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-identification-and-estimation","title":"Nonparametric Identification and Estimation with Independent, Discrete Instruments","date":"2019-06-12","arxiv_id":"1906.05231","repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-aware-inference-in-fuzzy-regression","title":"Bias-Aware Inference in Fuzzy Regression Discontinuity Designs","date":"2019-06-11","arxiv_id":"1906.04631","repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-deep-learning-models-for-limit","title":"Extending Deep Learning Models for Limit Order Books to Quantile Regression","date":"2019-06-11","arxiv_id":"1906.04404","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-network-approach-to-predict","title":"Heterogeneous network approach to predict individuals' mental health","date":"2019-06-11","arxiv_id":"1906.04346","repositories_listed":0,"syntology":null},{"url":null,"slug":"medium-term-load-forecasting-using-support","title":"Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization","date":"2019-06-11","arxiv_id":"1906.04818","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-regression-approach-to-certain-information","title":"A Regression Approach to Certain Information Transmission Problems","date":"2019-06-10","arxiv_id":"1906.03777","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-and-exploitation-in-symbolic","title":"Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms","date":"2019-06-10","arxiv_id":"1906.03959","repositories_listed":0,"syntology":null},{"url":null,"slug":"mean-estimation-and-regression-under-heavy","title":"Mean estimation and regression under heavy-tailed distributions--a survey","date":"2019-06-10","arxiv_id":"1906.04280","repositories_listed":0,"syntology":null},{"url":null,"slug":"selection-consistency-of-lasso-based","title":"Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors","date":"2019-06-10","arxiv_id":"1906.04175","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-regularization-on-high","title":"The Impact of Regularization on High-dimensional Logistic Regression","date":"2019-06-10","arxiv_id":"1906.03761","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-overfitting-peaks-in","title":"Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation","date":"2019-06-09","arxiv_id":"1906.03667","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-state-space-representations","title":"Disentangled State Space Representations","date":"2019-06-07","arxiv_id":"1906.03255","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-backpropagation","title":"Visual Backpropagation","date":"2019-06-06","arxiv_id":"1906.04011","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-linear-rule-models","title":"Generalized Linear Rule Models","date":"2019-06-05","arxiv_id":"1906.01761","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-contrastive-meta-learning-for","title":"Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings","date":"2019-06-05","arxiv_id":"1906.02236","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-object-detection-with-2d","title":"Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks","date":"2019-06-05","arxiv_id":"1906.01891","repositories_listed":0,"syntology":null},{"url":null,"slug":"frechet-random-forests","title":"Fréchet random forests for metric space valued regression with non euclidean predictors","date":"2019-06-04","arxiv_id":"1906.01741","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-many-variables-should-be-entered-in-a","title":"On the number of variables to use in principal component regression","date":"2019-06-04","arxiv_id":"1906.01139","repositories_listed":0,"syntology":null},{"url":null,"slug":"uniform-error-bounds-for-gaussian-process","title":"Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control","date":"2019-06-04","arxiv_id":"1906.01376","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-gaussian-process-regression-for-real","title":"Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement","date":"2019-06-03","arxiv_id":"1906.01095","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fact-taming-latent-factor-models-for","title":"The FacT: Taming Latent Factor Models for Explainability with Factorization Trees","date":"2019-06-03","arxiv_id":"1906.02037","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600273","title":"Robust approximate linear regression without correspondence","date":"2019-06-01","arxiv_id":"1906.00273","repositories_listed":0,"syntology":null},{"url":null,"slug":"at-what-level-should-one-cluster-standard","title":"At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?","date":"2019-06-01","arxiv_id":"1906.00288","repositories_listed":0,"syntology":null},{"url":null,"slug":"density-map-regression-guided-detection","title":"Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"duluth-at-semeval-2019-task-4-the-pioquinto","title":"Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-networks-for-exploring","title":"Graph convolutional networks for exploring authorship hypotheses","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incivility-detection-in-online-comments","title":"Incivility Detection in Online Comments","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jtml-at-semeval-2019-task-6-offensive-tweets","title":"JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitre-at-semeval-2019-task-5-transfer","title":"MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mmface-a-multi-metric-regression-network-for","title":"MMFace: A Multi-Metric Regression Network for Unconstrained Face Reconstruction","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-the-bottleneck-in-traditional","title":"Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/soft-labels-for-ordinal-regression","slug":"soft-labels-for-ordinal-regression","title":"Soft Labels for Ordinal Regression","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-based-estimation-of-bulbar-regression","title":"Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tuvd-team-at-semeval-2019-task-6-offense","title":"TUVD team at SemEval-2019 Task 6: Offense Target Identification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/visual-localization-by-learning-objects-of","slug":"visual-localization-by-learning-objects-of","title":"Visual Localization by Learning Objects-Of-Interest Dense Match Regression","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ordinal-regression-as-structured","title":"Ordinal Regression as Structured Classification","date":"2019-05-31","arxiv_id":"1905.13658","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneity-in-demand-and-optimal-price","title":"Heterogeneity in demand and optimal price conditioning for local rail transport","date":"2019-05-30","arxiv_id":"1905.12859","repositories_listed":0,"syntology":null},{"url":null,"slug":"threshold-regression-with-nonparametric","title":"Threshold Regression with Nonparametric Sample Splitting","date":"2019-05-30","arxiv_id":"1905.13140","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-aspect-of-optimal-regression-design-for","title":"An Aspect of Optimal Regression Design for LSMC","date":"2019-05-29","arxiv_id":"1811.08509","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-polya-inverse-gamma","title":"Data Augementation with Polya Inverse Gamma","date":"2019-05-29","arxiv_id":"1905.12141","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-cost-free-nature-of-optimally-tuning","title":"The cost-free nature of optimally tuning Tikhonov regularizers and other ordered smoothers","date":"2019-05-29","arxiv_id":"1905.12517","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gram-gauss-newton-method-learning","title":"Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems","date":"2019-05-28","arxiv_id":"1905.11675","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-deep-kernel-learning","title":"Adaptive Deep Kernel Learning","date":"2019-05-28","arxiv_id":"1905.12131","repositories_listed":0,"syntology":null},{"url":null,"slug":"em-converges-for-a-mixture-of-many-linear","title":"EM Converges for a Mixture of Many Linear Regressions","date":"2019-05-28","arxiv_id":"1905.12106","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-calibrating-uncertainty","title":"Evaluating and Calibrating Uncertainty Prediction in Regression Tasks","date":"2019-05-28","arxiv_id":"1905.11659","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-via-kirszbraun-extension-with","title":"Efficient Kirszbraun Extension with Applications to Regression","date":"2019-05-28","arxiv_id":"1905.11930","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-based-randomized-algorithms-for","title":"Sublinear Update Time Randomized Algorithms for Dynamic Graph Regression","date":"2019-05-28","arxiv_id":"1905.11963","repositories_listed":0,"syntology":null}],"record_sha256":"37cfef94da6f242fb2bd3dd3548dd44aed5250eff5ee8a4b6b7e41b943a9bfe9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}