{"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":"/method/linear-regression/papers/14","list_of":"/method/linear-regression","method":"Linear Regression","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":14,"pages_in_order":17,"rows_per_page":100,"rows":[1301,1400],"of":1657,"counts":{"archive_papers_tagged":1657,"with_a_code_link":323,"where_syntology_ran_a_sample":50,"not_listed_spam_title":0,"listed":1657,"listed_where_code_ran":50,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":39,"every_run_a_failure_of_syntologys_instrument":11,"listed_with_a_run_with_no_instrument_failure":39,"listed_every_run_a_failure_of_syntologys_instrument":11,"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":"/method/linear-regression","prev":"/method/linear-regression/papers/13","next":"/method/linear-regression/papers/15","papers":[{"paper":null,"slug":"q-gadmm-quantized-group-admm-for","title":"Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning","date":"2019-10-23","arxiv_id":"1910.10453","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-reaction-time-and-vigilance","title":"Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task","date":"2019-10-21","arxiv_id":"1910.10076","n_code_links":0,"syntology":null},{"paper":null,"slug":"first-order-expansion-of-convex-regularized","title":"First order expansion of convex regularized estimators","date":"2019-10-12","arxiv_id":"1910.05480","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-symmetric-norm-regression-via","title":"Efficient Symmetric Norm Regression via Linear Sketching","date":"2019-10-04","arxiv_id":"1910.01788","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-pseudo-likelihood-approach-to-linear","title":"A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data","date":"2019-10-03","arxiv_id":"1910.01623","n_code_links":0,"syntology":null},{"paper":null,"slug":"expertocoder-capturing-divergent-brain","title":"Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts","date":"2019-09-26","arxiv_id":"1909.12299","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-column-measure-and-gradient-free-gradient","title":"The column measure and Gradient-Free Gradient Boosting","date":"2019-09-24","arxiv_id":"1909.10960","n_code_links":0,"syntology":null},{"paper":"/paper/tuning-parameter-calibration-for-prediction","slug":"tuning-parameter-calibration-for-prediction","title":"Tuning parameter calibration for prediction in personalized medicine","date":"2019-09-23","arxiv_id":"1909.10635","n_code_links":1,"syntology":null},{"paper":null,"slug":"190910072","title":"A generalization of regularized dual averaging and its dynamics","date":"2019-09-22","arxiv_id":"1909.10072","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-slope-outperform-bridge-regression","title":"Does SLOPE outperform bridge regression?","date":"2019-09-20","arxiv_id":"1909.09345","n_code_links":0,"syntology":null},{"paper":"/paper/weighted-linear-bandits-for-non-stationary","slug":"weighted-linear-bandits-for-non-stationary","title":"Weighted Linear Bandits for Non-Stationary Environments","date":"2019-09-19","arxiv_id":"1909.09146","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/reproducibility-of-an-airway-tapering","slug":"reproducibility-of-an-airway-tapering","title":"Reproducibility of an airway tapering measurement in CT with application to bronchiectasis","date":"2019-09-16","arxiv_id":"1909.07454","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-knowledge-transfer-framework-for","title":"A Knowledge Transfer Framework for Differentially Private Sparse Learning","date":"2019-09-13","arxiv_id":"1909.06322","n_code_links":0,"syntology":null},{"paper":null,"slug":"super-learning-for-daily-streamflow","title":"Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms","date":"2019-09-09","arxiv_id":"1909.04131","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-greedy-constructive-algorithm-for-the","title":"A scalable constructive algorithm for the optimization of neural network architectures","date":"2019-09-07","arxiv_id":"1909.03306","n_code_links":0,"syntology":null},{"paper":null,"slug":"regression-clustering-for-improved-accuracy","title":"Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning","date":"2019-09-04","arxiv_id":"1909.02041","n_code_links":0,"syntology":null},{"paper":null,"slug":"rewarding-high-quality-data-via-influence","title":"Rewarding High-Quality Data via Influence Functions","date":"2019-08-30","arxiv_id":"1908.11598","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-concert-planning-tool-for-independent","title":"A Concert-planning Tool for Independent Musicians by Machine Learning Models","date":"2019-08-29","arxiv_id":"1908.11200","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-three-feature-model-to-predict-colour","title":"A Three-Feature Model to Predict Colour Change Blindness","date":"2019-08-25","arxiv_id":"1909.04147","n_code_links":0,"syntology":null},{"paper":"/paper/generalizing-psychological-similarity-spaces","slug":"generalizing-psychological-similarity-spaces","title":"Generalizing Psychological Similarity Spaces to Unseen Stimuli","date":"2019-08-25","arxiv_id":"1908.09260","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-learning-model-for-segmentation-of","title":"A deep learning model for segmentation of geographic atrophy to study its long-term natural history","date":"2019-08-15","arxiv_id":"1908.05621","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-generalization-error-of-random-features","title":"The generalization error of random features regression: Precise asymptotics and double descent curve","date":"2019-08-14","arxiv_id":"1908.05355","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicted-disease-compositions-of-human","title":"Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival","date":"2019-08-06","arxiv_id":"1908.02334","n_code_links":0,"syntology":null},{"paper":"/paper/dueling-posterior-sampling-for-preference","slug":"dueling-posterior-sampling-for-preference","title":"Dueling Posterior Sampling for Preference-Based Reinforcement Learning","date":"2019-08-04","arxiv_id":"1908.01289","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["ernovoseller/DuelingPosteriorSampling"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"method-of-contraction-expansion-moce-for","title":"Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models","date":"2019-08-04","arxiv_id":"1908.01253","n_code_links":0,"syntology":null},{"paper":null,"slug":"risk-management-via-anomaly-circumvent","title":"Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction","date":"2019-08-03","arxiv_id":"1908.01112","n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-recognition-for-tigrinya-language","title":"Speech Recognition for Tigrinya language Using Deep Neural Network Approach","date":"2019-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/airbnb-price-prediction-using-machine","slug":"airbnb-price-prediction-using-machine","title":"Airbnb Price Prediction Using Machine Learning and Sentiment Analysis","date":"2019-07-29","arxiv_id":"1907.12665","n_code_links":1,"syntology":null},{"paper":"/paper/effective-and-efficient-roi-wise-visual","slug":"effective-and-efficient-roi-wise-visual","title":"Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization","date":"2019-07-27","arxiv_id":"1907.11885","n_code_links":1,"syntology":null},{"paper":"/paper/algorithmic-analysis-and-statistical","slug":"algorithmic-analysis-and-statistical","title":"Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing","date":"2019-07-17","arxiv_id":"1907.07502","n_code_links":1,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":"/paper/warfarin-dose-estimation-on-multiple-datasets","slug":"warfarin-dose-estimation-on-multiple-datasets","title":"Warfarin dose estimation on multiple datasets with automated hyperparameter optimisation and a novel software framework","date":"2019-07-11","arxiv_id":"1907.05363","n_code_links":1,"syntology":null},{"paper":"/paper/identifying-linear-models-in-multi-resolution","slug":"identifying-linear-models-in-multi-resolution","title":"Identifying Linear Models in Multi-Resolution Population Data using Minimum Description Length Principle to Predict Household Income","date":"2019-07-10","arxiv_id":"1907.05234","n_code_links":1,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":null,"slug":"unbiased-estimators-for-random-design","title":"Unbiased estimators for random design regression","date":"2019-07-08","arxiv_id":"1907.03411","n_code_links":0,"syntology":null},{"paper":"/paper/on-a-randomized-multi-block-admm-for-solving","slug":"on-a-randomized-multi-block-admm-for-solving","title":"On a Randomized Multi-Block ADMM for Solving Selected Machine Learning Problems","date":"2019-07-03","arxiv_id":"1907.01995","n_code_links":1,"syntology":null},{"paper":null,"slug":"protecting-privacy-of-users-in-brain-computer","title":"Protecting Privacy of Users in Brain-Computer Interface Applications","date":"2019-07-02","arxiv_id":"1907.01586","n_code_links":0,"syntology":null},{"paper":null,"slug":"benign-overfitting-in-linear-regression","title":"Benign Overfitting in Linear Regression","date":"2019-06-26","arxiv_id":"1906.11300","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":null,"slug":"active-linear-regression","title":"Online A-Optimal Design and Active Linear Regression","date":"2019-06-20","arxiv_id":"1906.08509","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":"/paper/bayesian-experimental-design-using","slug":"bayesian-experimental-design-using","title":"Bayesian experimental design using regularized determinantal point processes","date":"2019-06-10","arxiv_id":"1906.04133","n_code_links":1,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":null,"slug":"complex-evolution-recurrent-neural-networks","title":"Complex Evolution Recurrent Neural Networks (ceRNNs)","date":"2019-06-05","arxiv_id":"1906.02246","n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":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,"n_code_links":0,"syntology":null},{"paper":null,"slug":"parallel-and-communication-avoiding-least","title":"Parallel and Communication Avoiding Least Angle Regression","date":"2019-05-27","arxiv_id":"1905.11340","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-gknock-nonlinear-group-feature-selection","title":"Deep-gKnock: nonlinear group-feature selection with deep neural network","date":"2019-05-24","arxiv_id":"1905.10013","n_code_links":0,"syntology":null},{"paper":null,"slug":"what-can-resnet-learn-efficiently-going","title":"What Can ResNet Learn Efficiently, Going Beyond Kernels?","date":"2019-05-24","arxiv_id":"1905.10337","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-helical-dynamos-with-machine","slug":"exploring-helical-dynamos-with-machine","title":"Exploring helical dynamos with machine learning","date":"2019-05-20","arxiv_id":"1905.08193","n_code_links":1,"syntology":null},{"paper":"/paper/merging-versus-ensembling-in-multi-study","slug":"merging-versus-ensembling-in-multi-study","title":"Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects","date":"2019-05-17","arxiv_id":"1905.07382","n_code_links":1,"syntology":null},{"paper":"/paper/a-new-look-at-an-old-problem-a-universal","slug":"a-new-look-at-an-old-problem-a-universal","title":"A New Look at an Old Problem: A Universal Learning Approach to Linear Regression","date":"2019-05-12","arxiv_id":"1905.04708","n_code_links":3,"syntology":null},{"paper":null,"slug":"190506256","title":"A Scalable Learned Index Scheme in Storage Systems","date":"2019-05-08","arxiv_id":"1905.06256","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-the-invariances-of-learning","title":"Characterizing the invariances of learning algorithms using category theory","date":"2019-05-06","arxiv_id":"1905.02072","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-some-popular-gaussian-graphical","title":"Learning Some Popular Gaussian Graphical Models without Condition Number Bounds","date":"2019-05-03","arxiv_id":"1905.01282","n_code_links":0,"syntology":null},{"paper":null,"slug":"meta-learners-learning-dynamics-are-unlike","title":"Meta-learners' learning dynamics are unlike learners'","date":"2019-05-03","arxiv_id":"1905.01320","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-exhaustive-analysis-of-lazy-vs-eager","title":"An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-learning-rate-schedules-for","title":"Rethinking learning rate schedules for stochastic optimization","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"horseshoe-regularization-for-machine-learning","title":"Horseshoe Regularization for Machine Learning in Complex and Deep Models","date":"2019-04-24","arxiv_id":"1904.10939","n_code_links":0,"syntology":null},{"paper":null,"slug":"objective-assessment-of-social-skills-using","title":"Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder","date":"2019-04-24","arxiv_id":"1904.10622","n_code_links":0,"syntology":null},{"paper":null,"slug":"memory-sample-tradeoffs-for-linear-regression","title":"Memory-Sample Tradeoffs for Linear Regression with Small Error","date":"2019-04-18","arxiv_id":"1904.08544","n_code_links":0,"syntology":null},{"paper":"/paper/symbolic-regression-for-constructing-analytic","slug":"symbolic-regression-for-constructing-analytic","title":"Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression","date":"2019-03-27","arxiv_id":"1903.11483","n_code_links":1,"syntology":null},{"paper":"/paper/localized-linear-regression-in-networked-data","slug":"localized-linear-regression-in-networked-data","title":"Localized Linear Regression in Networked Data","date":"2019-03-26","arxiv_id":"1903.11178","n_code_links":1,"syntology":null},{"paper":null,"slug":"short-term-load-forecasting-at-different","title":"Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis","date":"2019-03-26","arxiv_id":"1903.10679","n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-regularization-via-hadamard-product","title":"High-Dimensional Linear Regression via Implicit Regularization","date":"2019-03-22","arxiv_id":"1903.09367","n_code_links":0,"syntology":null},{"paper":null,"slug":"convergence-of-parameter-estimates-for","title":"Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models","date":"2019-03-21","arxiv_id":"1903.09235","n_code_links":0,"syntology":null},{"paper":null,"slug":"byzantine-fault-tolerant-distributed-linear","title":"Byzantine Fault Tolerant Distributed Linear Regression","date":"2019-03-20","arxiv_id":"1903.08752","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-hard-thresholding-for-near-optimal","title":"Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression","date":"2019-03-19","arxiv_id":"1903.08192","n_code_links":0,"syntology":null},{"paper":null,"slug":"mutual-linear-regression-based-discrete","title":"Mutual Linear Regression-based Discrete Hashing","date":"2019-03-15","arxiv_id":"1904.00744","n_code_links":0,"syntology":null},{"paper":null,"slug":"personal-dynamic-cost-aware-sensing-for","title":"Personal Dynamic Cost-Aware Sensing for Latent Context Detection","date":"2019-03-13","arxiv_id":"1903.05376","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-learning-for-regression-in-big-data","title":"Multiple Learning for Regression in big data","date":"2019-03-03","arxiv_id":"1903.00843","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-agnostic-high-dimensional-error-in","title":"On Robustness of Principal Component Regression","date":"2019-02-28","arxiv_id":"1902.10920","n_code_links":0,"syntology":null},{"paper":"/paper/cross-validation-in-sparse-linear-regression","slug":"cross-validation-in-sparse-linear-regression","title":"Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration","date":"2019-02-27","arxiv_id":"1902.10375","n_code_links":1,"syntology":null},{"paper":null,"slug":"provable-approximations-for-constrained-ell_p","title":"Provable Approximations for Constrained $\\ell_p$ Regression","date":"2019-02-27","arxiv_id":"1902.10407","n_code_links":0,"syntology":null},{"paper":"/paper/nonlinear-generalization-of-the-single-index","slug":"nonlinear-generalization-of-the-single-index","title":"Nonlinear generalization of the monotone single index model","date":"2019-02-24","arxiv_id":"1902.09024","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-of-porosity-and-permeability","title":"Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms","date":"2019-02-18","arxiv_id":"1902.06525","n_code_links":0,"syntology":null},{"paper":null,"slug":"exponentially-modified-gaussian-mixture-model","title":"Exponentially-Modified Gaussian Mixture Model: Applications in Spectroscopy","date":"2019-02-14","arxiv_id":"1902.05601","n_code_links":0,"syntology":null},{"paper":null,"slug":"differential-description-length-for","title":"Differential Description Length for Hyperparameter Selection in Machine Learning","date":"2019-02-13","arxiv_id":"1902.04699","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-online-linear-regression","title":"Distributed Online Linear Regression","date":"2019-02-13","arxiv_id":"1902.04774","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-cost-of-privacy-optimal-rates-of","title":"The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy","date":"2019-02-12","arxiv_id":"1902.04495","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-least-trimmed-squares-for-mixed","title":"Iterative Least Trimmed Squares for Mixed Linear Regression","date":"2019-02-10","arxiv_id":"1902.03653","n_code_links":0,"syntology":null},{"paper":null,"slug":"accounting-for-significance-and","title":"Scalable Holistic Linear Regression","date":"2019-02-08","arxiv_id":"1902.03272","n_code_links":0,"syntology":null},{"paper":"/paper/secure-multi-party-linear-regression-at","slug":"secure-multi-party-linear-regression-at","title":"Secure multi-party linear regression at plaintext speed","date":"2019-01-28","arxiv_id":"1901.09531","n_code_links":1,"syntology":null},{"paper":"/paper/the-autofeat-python-library-for-automatic","slug":"the-autofeat-python-library-for-automatic","title":"The autofeat Python Library for Automated Feature Engineering and Selection","date":"2019-01-22","arxiv_id":"1901.07329","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["cod3licious/autofeat"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-direct-and-inverse-transmission","title":"Learning Direct and Inverse Transmission Matrices","date":"2019-01-15","arxiv_id":"1901.04816","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-individual-responses-to-vasoactive","title":"Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock","date":"2019-01-15","arxiv_id":"1901.10400","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-filtering-pipeline-evaluation-of","title":"Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation","date":"2019-01-15","arxiv_id":"1901.04618","n_code_links":0,"syntology":null},{"paper":"/paper/variational-bayesian-complex-network","slug":"variational-bayesian-complex-network","title":"Variational Bayesian Weighted Complex Network Reconstruction","date":"2018-12-11","arxiv_id":"1812.04369","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-improved-analysis-of-alternating","title":"An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"doubly-robust-bayesian-inference-for-non","title":"Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \\beta-Divergences","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ell_1-regression-with-heavy-tailed-1","title":"\\ell_1-regression with Heavy-tailed Distributions","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"model-based-targeted-dimensionality-reduction","title":"Model-based targeted dimensionality reduction for neuronal population data","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-subsampling-with-influence-functions","title":"Optimal Subsampling with Influence Functions","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-hyperparameter-transfer-learning","title":"Scalable Hyperparameter Transfer Learning","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"recovery-guarantees-for-polynomial","title":"Recovery guarantees for polynomial approximation from dependent data with outliers","date":"2018-11-25","arxiv_id":"1811.10115","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-pca-from-sparse-linear-regression","title":"Sparse PCA from Sparse Linear Regression","date":"2018-11-25","arxiv_id":"1811.10106","n_code_links":0,"syntology":null},{"paper":null,"slug":"steerable-wavelet-scattering-for-3d-atomic","title":"Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction","date":"2018-11-21","arxiv_id":"1812.02320","n_code_links":0,"syntology":null},{"paper":null,"slug":"optical-flow-based-background-subtraction","title":"Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving","date":"2018-11-16","arxiv_id":"1811.06660","n_code_links":0,"syntology":null}],"record_sha256":"a9ba6055f621b48ad418cebcd32fdf5848f540b7c9a0e15589e265c2a4631fc9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}