{"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/34","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":34,"pages_in_order":95,"rows_per_page":100,"rows":[3301,3400],"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/33","next":"/task/regression-1/papers/35","papers":[{"url":null,"slug":"fusing-pretrained-vits-with-tcnet-for","title":"Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression","date":"2024-04-02","arxiv_id":"2404.15311","repositories_listed":0,"syntology":null},{"url":null,"slug":"postprocessing-of-point-predictions-for","title":"Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression","date":"2024-04-02","arxiv_id":"2404.02270","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-integration-distillation-for-object","title":"Task Integration Distillation for Object Detectors","date":"2024-04-02","arxiv_id":"2404.01699","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ai-integrated-feature-engineering","title":"Explainable AI Integrated Feature Engineering for Wildfire Prediction","date":"2024-04-01","arxiv_id":"2404.01487","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-content-for-hdr-deghosting-from","title":"Generating Content for HDR Deghosting from Frequency View","date":"2024-04-01","arxiv_id":"2404.00849","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimum-norm-interpolation-under-covariate","title":"Minimum-Norm Interpolation Under Covariate Shift","date":"2024-03-31","arxiv_id":"2404.00522","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-omp-for-exact-recovery-and-sparse","title":"Fast Orthogonal Matching Pursuit through Successive Regression","date":"2024-03-29","arxiv_id":"2404.00146","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-multimodal-fusion-with-modal-channel","title":"Sparsely Multimodal Data Fusion","date":"2024-03-29","arxiv_id":"2403.20280","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsample-dnn-sampling-based-testing-for","title":"DeepSample: DNN sampling-based testing for operational accuracy assessment","date":"2024-03-28","arxiv_id":"2403.19271","repositories_listed":0,"syntology":null},{"url":null,"slug":"h-consistency-guarantees-for-regression","title":"$H$-Consistency Guarantees for Regression","date":"2024-03-28","arxiv_id":"2403.19480","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-with-multi-expert-deferral","title":"Regression with Multi-Expert Deferral","date":"2024-03-28","arxiv_id":"2403.19494","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastcar-fast-classification-and-regression","title":"FastCAR: Fast Classification And Regression Multi-Task Learning via Task Consolidation for Modelling a Continuous Property Variable of Object Classes","date":"2024-03-26","arxiv_id":"2403.17926","repositories_listed":0,"syntology":null},{"url":null,"slug":"hawk-accurate-and-fast-privacy-preserving","title":"Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation","date":"2024-03-26","arxiv_id":"2403.17296","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-are-an-expert-annotator-automatic-best","title":"\"You are an expert annotator\": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling","date":"2024-03-26","arxiv_id":"2403.17612","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-attention-associate-prediction-network","title":"Multi-attention Associate Prediction Network for Visual Tracking","date":"2024-03-25","arxiv_id":"2403.16395","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-convex-m-estimation-via-score","title":"Optimal convex $M$-estimation via score matching","date":"2024-03-25","arxiv_id":"2403.16688","repositories_listed":0,"syntology":null},{"url":null,"slug":"ann-based-adaptive-nmpc-for-uranium","title":"ANN-Based Adaptive NMPC for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process","date":"2024-03-24","arxiv_id":"2403.16307","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-optimal-differentially-private-low-rank","title":"Near-Optimal differentially private low-rank trace regression with guaranteed private initialization","date":"2024-03-24","arxiv_id":"2403.15999","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-regression-based-on","title":"Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks","date":"2024-03-22","arxiv_id":"2403.15108","repositories_listed":0,"syntology":null},{"url":null,"slug":"dor3d-net-dense-ordinal-regression-network","title":"DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation","date":"2024-03-20","arxiv_id":"2403.13405","repositories_listed":0,"syntology":null},{"url":null,"slug":"fused-lasso-as-non-crossing-quantile","title":"Fused LASSO as Non-Crossing Quantile Regression","date":"2024-03-20","arxiv_id":"2403.14036","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-multigrid-accelerate-back-fitting-via","title":"Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression","date":"2024-03-20","arxiv_id":"2403.13300","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-alternative-graphical-lasso-algorithm-for","title":"An Alternative Graphical Lasso Algorithm for Precision Matrices","date":"2024-03-19","arxiv_id":"2403.12357","repositories_listed":0,"syntology":null},{"url":null,"slug":"inflation-target-at-risk-a-time-varying","title":"Inflation Target at Risk: A Time-varying Parameter Distributional Regression","date":"2024-03-19","arxiv_id":"2403.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"modal-analysis-of-spatiotemporal-data-via","title":"Modal Analysis of Spatiotemporal Data via Multivariate Gaussian Process Regression","date":"2024-03-19","arxiv_id":"2403.13118","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-parameter-regression-for-explicit","title":"Neural Parameter Regression for Explicit Representations of PDE Solution Operators","date":"2024-03-19","arxiv_id":"2403.12764","repositories_listed":0,"syntology":null},{"url":null,"slug":"tighter-confidence-bounds-for-sequential","title":"Tighter Confidence Bounds for Sequential Kernel Regression","date":"2024-03-19","arxiv_id":"2403.12732","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximation-of-rkhs-functionals-by-neural","title":"Approximation of RKHS Functionals by Neural Networks","date":"2024-03-18","arxiv_id":"2403.12187","repositories_listed":0,"syntology":null},{"url":null,"slug":"normalized-validity-scores-for-dnns-in","title":"Normalized Validity Scores for DNNs in Regression based Eye Feature Extraction","date":"2024-03-18","arxiv_id":"2403.11665","repositories_listed":0,"syntology":null},{"url":null,"slug":"petscml-second-order-solvers-for-training","title":"PETScML: Second-order solvers for training regression problems in Scientific Machine Learning","date":"2024-03-18","arxiv_id":"2403.12188","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-informative-conformal-prediction","title":"Selecting informative conformal prediction sets with false coverage rate control","date":"2024-03-18","arxiv_id":"2403.12295","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-transfer-learning-with-differential","title":"Federated Transfer Learning with Differential Privacy","date":"2024-03-17","arxiv_id":"2403.11343","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-system-reliability","title":"Machine learning-based system reliability analysis with Gaussian Process Regression","date":"2024-03-17","arxiv_id":"2403.11125","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-identification-and-estimation-2","title":"Nonparametric Identification and Estimation with Non-Classical Errors-in-Variables","date":"2024-03-17","arxiv_id":"2403.11309","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structure-preserving-kernel-method-for","title":"A Structure-Preserving Kernel Method for Learning Hamiltonian Systems","date":"2024-03-15","arxiv_id":"2403.10070","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-study-of-predictive-maintenance","title":"Comprehensive Study Of Predictive Maintenance In Industries Using Classification Models And LSTM Model","date":"2024-03-15","arxiv_id":"2403.10259","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-forgetting-online-data-stream","title":"Iterative Forgetting: Online Data Stream Regression Using Database-Inspired Adaptive Granulation","date":"2024-03-14","arxiv_id":"2403.09588","repositories_listed":0,"syntology":null},{"url":null,"slug":"outlier-robust-multivariate-polynomial","title":"Outlier Robust Multivariate Polynomial Regression","date":"2024-03-14","arxiv_id":"2403.09465","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-blood-flow-in-data-poor","title":"Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression","date":"2024-03-14","arxiv_id":"2403.09758","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-nerfect-match-exploring-nerf-features-for","title":"The NeRFect Match: Exploring NeRF Features for Visual Localization","date":"2024-03-14","arxiv_id":"2403.09577","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-estimation-in-spatial","title":"Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products","date":"2024-03-14","arxiv_id":"2403.10567","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotics-of-random-feature-regression","title":"Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime","date":"2024-03-13","arxiv_id":"2403.08160","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-that-limits-search","title":"Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPR","date":"2024-03-13","arxiv_id":"2403.08331","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-implicit-regularization-of-sgd-with","title":"Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems","date":"2024-03-13","arxiv_id":"2403.08585","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifidelity-linear-regression-for","title":"Multifidelity linear regression for scientific machine learning from scarce data","date":"2024-03-13","arxiv_id":"2403.08627","repositories_listed":0,"syntology":null},{"url":null,"slug":"weak-collocation-regression-for-inferring","title":"Weak Collocation Regression for Inferring Stochastic Dynamics with Lévy Noise","date":"2024-03-13","arxiv_id":"2403.08292","repositories_listed":0,"syntology":null},{"url":null,"slug":"xpertai-uncovering-model-strategies-for-sub","title":"XpertAI: uncovering model strategies for sub-manifolds","date":"2024-03-12","arxiv_id":"2403.07486","repositories_listed":0,"syntology":null},{"url":null,"slug":"semiparametric-inference-for-regression","title":"Semiparametric Inference for Regression-Discontinuity Designs","date":"2024-03-09","arxiv_id":"2403.05803","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-electronic-band-gap-energy-from","title":"Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning","date":"2024-03-08","arxiv_id":"2403.05119","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-links-between-the-fundamental","title":"Exploring the Links between the Fundamental Lemma and Kernel Regression","date":"2024-03-08","arxiv_id":"2403.05368","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-generalization-ability-of-deep-wide","title":"Improve Generalization Ability of Deep Wide Residual Network with A Suitable Scaling Factor","date":"2024-03-07","arxiv_id":"2403.04545","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-aware-llm-inference","title":"Regression-aware Inference with LLMs","date":"2024-03-07","arxiv_id":"2403.04182","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-regression-under-cluster","title":"Nonparametric Regression under Cluster Sampling","date":"2024-03-07","arxiv_id":"2403.04766","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-deepiv-with-model-selection","title":"Regularized DeepIV with Model Selection","date":"2024-03-07","arxiv_id":"2403.04236","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-self-supervised-learning-of-visual","title":"On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors","date":"2024-03-06","arxiv_id":"2403.04071","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-with-unknown-constraints","title":"Online Learning with Unknown Constraints","date":"2024-03-06","arxiv_id":"2403.04033","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-cryptocurrency-prices-using","title":"Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression","date":"2024-03-06","arxiv_id":"2403.03410","repositories_listed":0,"syntology":null},{"url":null,"slug":"probsaint-probabilistic-tabular-regression","title":"ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing","date":"2024-03-06","arxiv_id":"2403.03812","repositories_listed":0,"syntology":null},{"url":null,"slug":"stop-regressing-training-value-functions-via","title":"Stop Regressing: Training Value Functions via Classification for Scalable Deep RL","date":"2024-03-06","arxiv_id":"2403.03950","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-well-can-transformers-emulate-in-context","title":"How Well Can Transformers Emulate In-context Newton's Method?","date":"2024-03-05","arxiv_id":"2403.03183","repositories_listed":0,"syntology":null},{"url":null,"slug":"triple-debiased-lasso-for-statistical","title":"Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects","date":"2024-03-05","arxiv_id":"2403.03240","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-prediction-rigidity-formalism-for-low-cost","title":"A prediction rigidity formalism for low-cost uncertainties in trained neural networks","date":"2024-03-04","arxiv_id":"2403.02251","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-horseshoe-gaussian-processes","title":"Deep Horseshoe Gaussian Processes","date":"2024-03-04","arxiv_id":"2403.01737","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-generalisation-via-anchor","title":"Out-of-distribution robustness for multivariate analysis via causal regularisation","date":"2024-03-04","arxiv_id":"2403.01865","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-implicit-bias-of-heterogeneity-towards","title":"The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing","date":"2024-03-03","arxiv_id":"2403.01420","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-field-classifiers-via-target-encoding","title":"Neural Field Classifiers via Target Encoding and Classification Loss","date":"2024-03-02","arxiv_id":"2403.01058","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-gradient-descent-for-streaming","title":"Stochastic gradient descent for streaming linear and rectified linear systems with adversarial corruptions","date":"2024-03-02","arxiv_id":"2403.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-beats-a-recipe-to-song-popularity-a","title":"Beyond Beats: A Recipe to Song Popularity? A machine learning approach","date":"2024-03-01","arxiv_id":"2403.12079","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-multi-label-feature-selection-via","title":"Embedded Multi-label Feature Selection via Orthogonal Regression","date":"2024-03-01","arxiv_id":"2403.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"substitute-adjustment-via-recovery-of-latent","title":"Substitute adjustment via recovery of latent variables","date":"2024-03-01","arxiv_id":"2403.00202","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-testing-environment-generation-for","title":"Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning","date":"2024-02-29","arxiv_id":"2402.19275","repositories_listed":0,"syntology":null},{"url":null,"slug":"dose-prediction-driven-radiotherapy-paramters","title":"Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling","date":"2024-02-29","arxiv_id":"2402.18879","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-modular-multiplication","title":"Machine learning for modular multiplication","date":"2024-02-29","arxiv_id":"2402.19254","repositories_listed":0,"syntology":null},{"url":null,"slug":"prognostic-covariate-adjustment-for-logistic","title":"Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials","date":"2024-02-29","arxiv_id":"2402.18900","repositories_listed":0,"syntology":null},{"url":null,"slug":"material-microstructure-design-using-vae","title":"Material Microstructure Design Using VAE-Regression with Multimodal Prior","date":"2024-02-27","arxiv_id":"2402.17806","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustness-congruent-adversarial-training-for","title":"Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates","date":"2024-02-27","arxiv_id":"2402.17390","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-variational-contaminated-noise","title":"Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting","date":"2024-02-27","arxiv_id":"2402.17570","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformalized-selective-regression","title":"Conformalized Selective Regression","date":"2024-02-26","arxiv_id":"2402.16300","repositories_listed":0,"syntology":null},{"url":null,"slug":"failures-and-successes-of-cross-validation","title":"Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent","date":"2024-02-26","arxiv_id":"2402.16793","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-algorithms-for-quantile-regression-with","title":"Fast Algorithms for Quantile Regression with Selection","date":"2024-02-26","arxiv_id":"2402.16693","repositories_listed":0,"syntology":null},{"url":null,"slug":"valuing-insurance-against-small-probability","title":"Valuing insurance against small probability risks: A meta-analysis","date":"2024-02-26","arxiv_id":"2402.16375","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-duality-analysis-of-kernel-ridge-regression","title":"Optimal Rates and Saturation for Noiseless Kernel Ridge Regression","date":"2024-02-24","arxiv_id":"2402.15718","repositories_listed":0,"syntology":null},{"url":null,"slug":"frustratingly-simple-prompting-based-text","title":"Frustratingly Simple Prompting-based Text Denoising","date":"2024-02-24","arxiv_id":"2402.15931","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformalized-deeponet-a-distribution-free","title":"Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks","date":"2024-02-23","arxiv_id":"2402.15406","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-semi-supervised-inference-for","title":"Efficient semi-supervised inference for logistic regression under case-control studies","date":"2024-02-23","arxiv_id":"2402.15365","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-multivariate-adaptive-regression-splines","title":"Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency","date":"2024-02-23","arxiv_id":"2402.15561","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-for-regression-with-variables","title":"Inference for Regression with Variables Generated by AI or Machine Learning","date":"2024-02-23","arxiv_id":"2402.15585","repositories_listed":0,"syntology":null},{"url":null,"slug":"lasso-with-latents-efficient-estimation","title":"Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps","date":"2024-02-23","arxiv_id":"2402.15409","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-agnostic-regression-a-machine","title":"Statistical Agnostic Regression: a machine learning method to validate regression models","date":"2024-02-23","arxiv_id":"2402.15213","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-data-preprocessing","title":"Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data","date":"2024-02-22","arxiv_id":"2402.14980","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-online-linear-regression-for","title":"Multivariate Online Linear Regression for Hierarchical Forecasting","date":"2024-02-22","arxiv_id":"2402.14578","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-contact-acquisition-of-ppg-signal-using","title":"Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals","date":"2024-02-22","arxiv_id":"2402.14565","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonsmooth-nonparametric-regression-via","title":"Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps","date":"2024-02-22","arxiv_id":"2402.14985","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-neural-network-uncertainty-under","title":"Quantifying neural network uncertainty under volatility clustering","date":"2024-02-22","arxiv_id":"2402.14476","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-efficient-linear-regression-with-self","title":"Sample-Efficient Linear Regression with Self-Selection Bias","date":"2024-02-22","arxiv_id":"2402.14229","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-linear-regression-and-lattice-problems","title":"Sparse Linear Regression and Lattice Problems","date":"2024-02-22","arxiv_id":"2402.14645","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-powered-predictions-for-electricity-load","title":"AI-Powered Predictions for Electricity Load in Prosumer Communities","date":"2024-02-21","arxiv_id":"2402.13752","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-statistical-gaps-for-improper","title":"Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression","date":"2024-02-21","arxiv_id":"2402.14103","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-normalized-conformal-prediction-and","title":"Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests","date":"2024-02-21","arxiv_id":"2402.14080","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-neural-networks-pnns-for","title":"Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning","date":"2024-02-21","arxiv_id":"2402.13945","repositories_listed":0,"syntology":null}],"record_sha256":"02a65e46ad2242f4504ec38416400f9bc63a63ede4ede11f7797b67059619c2a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}