{"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/5","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":5,"pages_in_order":17,"rows_per_page":100,"rows":[401,500],"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/4","next":"/method/linear-regression/papers/6","papers":[{"paper":null,"slug":"effects-of-daily-exercise-time-on-the","title":"Effects of Daily Exercise Time on the Academic Performance of Students: An Empirical Analysis Based on CEPS Data","date":"2023-11-30","arxiv_id":"2312.11484","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-convergence-of-online-identification","title":"Global Convergence of Online Identification for Mixed Linear Regression","date":"2023-11-30","arxiv_id":"2311.18506","n_code_links":0,"syntology":null},{"paper":null,"slug":"positional-information-matters-for-invariant","title":"Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes","date":"2023-11-30","arxiv_id":"2311.18194","n_code_links":0,"syntology":null},{"paper":"/paper/vrem-fl-mobility-aware-computation-scheduling","slug":"vrem-fl-mobility-aware-computation-scheduling","title":"VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning","date":"2023-11-30","arxiv_id":"2311.18741","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-personalized-uncertainty-quantification","title":"A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth","date":"2023-11-28","arxiv_id":"2311.17173","n_code_links":0,"syntology":null},{"paper":"/paper/modelling-wildland-fire-burn-severity-in","slug":"modelling-wildland-fire-burn-severity-in","title":"Modelling wildland fire burn severity in California using a spatial Super Learner approach","date":"2023-11-25","arxiv_id":"2311.16187","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-quantum-algorithms-for-linear","title":"Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters","date":"2023-11-24","arxiv_id":"2311.14823","n_code_links":0,"syntology":null},{"paper":"/paper/gradient-based-bilevel-optimization-for-multi","slug":"gradient-based-bilevel-optimization-for-multi","title":"Gradient-based bilevel optimization for multi-penalty Ridge regression through matrix differential calculus","date":"2023-11-23","arxiv_id":"2311.14182","n_code_links":1,"syntology":null},{"paper":null,"slug":"risk-bounds-of-accelerated-sgd-for","title":"Risk Bounds of Accelerated SGD for Overparameterized Linear Regression","date":"2023-11-23","arxiv_id":"2311.14222","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-linear-regression","title":"Comparative Analysis of Linear Regression, Gaussian Elimination, and LU Decomposition for CT Real Estate Purchase Decisions","date":"2023-11-22","arxiv_id":"2311.13471","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-linear-regression-with-constraints-a","title":"Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework","date":"2023-11-14","arxiv_id":"2311.08342","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-hamilton-jacobi-pdes-with-time","slug":"leveraging-hamilton-jacobi-pdes-with-time","title":"Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning","date":"2023-11-13","arxiv_id":"2311.07790","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-ground-reaction-force-from","title":"Predicting Ground Reaction Force from Inertial Sensors","date":"2023-11-04","arxiv_id":"2311.02287","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-fragility-of-sparsity","title":"The Fragility of Sparsity","date":"2023-11-04","arxiv_id":"2311.02299","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-linear-regression-for-binary","title":"Regularized Linear Regression for Binary Classification","date":"2023-11-03","arxiv_id":"2311.02270","n_code_links":0,"syntology":null},{"paper":null,"slug":"impact-of-investing-characteristics-on","title":"Impact of Investing Characteristics on Financial Performance of Individual Investors: An Exploratory Study","date":"2023-11-01","arxiv_id":"2311.00384","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-are-efficient-in-context","slug":"transformers-are-efficient-in-context","title":"Transformers are Provably Optimal In-context Estimators for Wireless Communications","date":"2023-11-01","arxiv_id":"2311.00226","n_code_links":1,"syntology":null},{"paper":null,"slug":"stochastic-time-optimal-trajectory-planning","title":"Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios","date":"2023-10-31","arxiv_id":"2311.00126","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-alterations-of-brain-effective","title":"Effective connectivity signatures in major depressive disorder: fMRI study using a multi-site dataset","date":"2023-10-31","arxiv_id":"2310.20231","n_code_links":0,"syntology":null},{"paper":null,"slug":"scaling-up-differentially-private-lasso","title":"Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations","date":"2023-10-30","arxiv_id":"2310.19978","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-microservice-call-rate","title":"Comparison of Microservice Call Rate Predictions for Replication in the Cloud","date":"2023-10-29","arxiv_id":"2401.03319","n_code_links":0,"syntology":null},{"paper":"/paper/grokking-beyond-neural-networks-an-empirical","slug":"grokking-beyond-neural-networks-an-empirical","title":"Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity","date":"2023-10-26","arxiv_id":"2310.17247","n_code_links":1,"syntology":null},{"paper":null,"slug":"cate-lasso-conditional-average-treatment","title":"CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression","date":"2023-10-25","arxiv_id":"2310.16819","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-aware-feature-attribution-through","title":"Context-aware feature attribution through argumentation","date":"2023-10-24","arxiv_id":"2310.16157","n_code_links":0,"syntology":null},{"paper":null,"slug":"mid-long-term-daily-electricity-consumption","title":"Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN","date":"2023-10-23","arxiv_id":"2310.15204","n_code_links":0,"syntology":null},{"paper":null,"slug":"exact-asymptotic-estimation-of-unknown","title":"Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation","date":"2023-10-21","arxiv_id":"2310.14073","n_code_links":0,"syntology":null},{"paper":"/paper/arniqa-learning-distortion-manifold-for-image","slug":"arniqa-learning-distortion-manifold-for-image","title":"ARNIQA: Learning Distortion Manifold for Image Quality Assessment","date":"2023-10-20","arxiv_id":"2310.14918","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["miccunifi/arniqa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/conditional-density-estimations-from-privacy","slug":"conditional-density-estimations-from-privacy","title":"Simulation-based Bayesian Inference from Privacy Protected Data","date":"2023-10-19","arxiv_id":"2310.12781","n_code_links":1,"syntology":null},{"paper":"/paper/fuel-consumption-prediction-for-a-passenger","slug":"fuel-consumption-prediction-for-a-passenger","title":"Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study","date":"2023-10-19","arxiv_id":"2310.13123","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimal-excess-risk-bounds-for-empirical-risk","title":"Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression","date":"2023-10-19","arxiv_id":"2310.12437","n_code_links":0,"syntology":null},{"paper":null,"slug":"channel-estimation-via-loss-field-accurate","title":"Channel Estimation via Loss Field: Accurate Site-Trained Modeling for Shadowing Prediction","date":"2023-10-18","arxiv_id":"2310.12284","n_code_links":0,"syntology":null},{"paper":"/paper/regularization-properties-of-adversarially-1","slug":"regularization-properties-of-adversarially-1","title":"Regularization properties of adversarially-trained linear regression","date":"2023-10-16","arxiv_id":"2310.10807","n_code_links":1,"syntology":{"ran":8,"of":11,"n_ran_checked":8,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["antonior92/advtrain-linreg"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/fast-screening-rules-for-optimal-design-via","slug":"fast-screening-rules-for-optimal-design-via","title":"Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation","date":"2023-10-13","arxiv_id":"2310.08939","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-many-pretraining-tasks-are-needed-for-in","title":"How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?","date":"2023-10-12","arxiv_id":"2310.08391","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-quantization-for-key-generation-in","title":"Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks","date":"2023-10-11","arxiv_id":"2310.07853","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-analysis-of-sparse-linear-regression","title":"Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model","date":"2023-10-11","arxiv_id":"2310.07367","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-computational-complexity-of-private","slug":"on-the-computational-complexity-of-private","title":"On the Computational Complexity of Private High-dimensional Model Selection","date":"2023-10-11","arxiv_id":"2310.07852","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["roysaptaumich/dp-bss"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"correlated-noise-provably-beats-independent","title":"Correlated Noise Provably Beats Independent Noise for Differentially Private Learning","date":"2023-10-10","arxiv_id":"2310.06771","n_code_links":0,"syntology":null},{"paper":null,"slug":"sharing-information-between-machine-tools-to","title":"Sharing Information Between Machine Tools to Improve Surface Finish Forecasting","date":"2023-10-09","arxiv_id":"2310.05807","n_code_links":0,"syntology":null},{"paper":null,"slug":"oracle-efficient-algorithms-for-groupwise","title":"Oracle Efficient Algorithms for Groupwise Regret","date":"2023-10-07","arxiv_id":"2310.04652","n_code_links":0,"syntology":null},{"paper":"/paper/robust-network-pruning-with-sparse-entropic","slug":"robust-network-pruning-with-sparse-entropic","title":"SWAP: Sparse Entropic Wasserstein Regression for Robust Network Pruning","date":"2023-10-07","arxiv_id":"2310.04918","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":5,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["youlei202/entropic-wasserstein-pruning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"moran-s-i-lasso-for-models-with-spatially","title":"Moran's I Lasso for models with spatially correlated data","date":"2023-10-04","arxiv_id":"2310.02773","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-estimator-for-linear-regression-with","title":"Optimal Estimator for Linear Regression with Shuffled Labels","date":"2023-10-02","arxiv_id":"2310.01326","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-context-learning-in-large-language-models-1","title":"Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models","date":"2023-09-30","arxiv_id":"2310.00313","n_code_links":0,"syntology":null},{"paper":null,"slug":"handling-missing-data-in-burundian-sovereign","title":"Handling missing data in Burundian sovereign bond market","date":"2023-09-29","arxiv_id":"2309.17379","n_code_links":0,"syntology":null},{"paper":null,"slug":"convergence-guarantees-for-forward-gradient","title":"Convergence guarantees for forward gradient descent in the linear regression model","date":"2023-09-26","arxiv_id":"2309.15001","n_code_links":0,"syntology":null},{"paper":null,"slug":"hebbian-learning-inspired-estimation-of-the","title":"Hebbian learning inspired estimation of the linear regression parameters from queries","date":"2023-09-26","arxiv_id":"2311.03483","n_code_links":0,"syntology":null},{"paper":"/paper/investigation-of-factors-regarding-the","slug":"investigation-of-factors-regarding-the","title":"Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer","date":"2023-09-26","arxiv_id":"2310.00018","n_code_links":1,"syntology":null},{"paper":"/paper/imbalanced-mixed-linear-regression","slug":"imbalanced-mixed-linear-regression","title":"Imbalanced Mixed Linear Regression","date":"2023-09-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"label-robust-and-differentially-private","title":"Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency","date":"2023-09-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-a-1-layer-conditional-generative","title":"Learning a 1-layer conditional generative model in total variation","date":"2023-09-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-exponential-families-from-truncated","title":"Learning Exponential Families from Truncated Samples","date":"2023-09-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"new-bounds-for-hyperparameter-tuning-of","title":"New Bounds for Hyperparameter Tuning of Regression Problems Across Instances","date":"2023-09-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/multi-dimensional-domain-generalization-with","slug":"multi-dimensional-domain-generalization-with","title":"Multi-dimensional domain generalization with low-rank structures","date":"2023-09-18","arxiv_id":"2309.09555","n_code_links":1,"syntology":null},{"paper":null,"slug":"sex-based-disparities-in-brain-aging-a-focus","title":"Sex-based Disparities in Brain Aging: A Focus on Parkinson's Disease","date":"2023-09-18","arxiv_id":"2309.10069","n_code_links":0,"syntology":null},{"paper":"/paper/heteroscedastic-sparse-high-dimensional","slug":"heteroscedastic-sparse-high-dimensional","title":"Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression","date":"2023-09-15","arxiv_id":"2309.08783","n_code_links":1,"syntology":null},{"paper":"/paper/market-gan-adding-control-to-financial-market","slug":"market-gan-adding-control-to-financial-market","title":"Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context","date":"2023-09-14","arxiv_id":"2309.07708","n_code_links":1,"syntology":null},{"paper":null,"slug":"spectrum-aware-adjustment-a-new-debiasing","title":"Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression","date":"2023-09-14","arxiv_id":"2309.07810","n_code_links":0,"syntology":null},{"paper":null,"slug":"remote-inference-of-cognitive-scores-in-als","title":"Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description","date":"2023-09-13","arxiv_id":"2309.06989","n_code_links":0,"syntology":null},{"paper":"/paper/epistemic-modeling-uncertainty-of-rapid","slug":"epistemic-modeling-uncertainty-of-rapid","title":"Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning","date":"2023-09-12","arxiv_id":"2309.06628","n_code_links":1,"syntology":null},{"paper":null,"slug":"streamlined-data-fusion-unleashing-the-power","title":"Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments","date":"2023-09-10","arxiv_id":"2309.04981","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-drawdown-realistic-financial-price","title":"Generating drawdown-realistic financial price paths using path signatures","date":"2023-09-08","arxiv_id":"2309.04507","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-as-optimizers","slug":"large-language-models-as-optimizers","title":"Large Language Models as Optimizers","date":"2023-09-07","arxiv_id":"2309.03409","n_code_links":4,"syntology":{"ran":14,"of":14,"n_ran_checked":10,"n_instrument":4,"unverified":0,"pointer_only":6,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-deepmind/opro"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"generalized-information-criteria-for","title":"Generalized Information Criteria for Structured Sparse Models","date":"2023-09-04","arxiv_id":"2309.01764","n_code_links":0,"syntology":null},{"paper":"/paper/interpretation-of-high-dimensional-linear","slug":"interpretation-of-high-dimensional-linear","title":"Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data","date":"2023-09-01","arxiv_id":"2309.00564","n_code_links":1,"syntology":null},{"paper":null,"slug":"intelligent-system-for-assessing-university","title":"Intelligent System for Assessing University Student Personality Development and Career Readiness","date":"2023-08-29","arxiv_id":"2308.15620","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-steganographic-capacity-of-selected","title":"On the Steganographic Capacity of Selected Learning Models","date":"2023-08-29","arxiv_id":"2308.15502","n_code_links":0,"syntology":null},{"paper":null,"slug":"delelstm-decomposition-based-linear","title":"DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series","date":"2023-08-26","arxiv_id":"2308.13797","n_code_links":0,"syntology":null},{"paper":null,"slug":"eeatc-a-novel-calibration-approach-for-low","title":"EEATC: A Novel Calibration Approach for Low-cost Sensors","date":"2023-08-25","arxiv_id":"2308.13572","n_code_links":0,"syntology":null},{"paper":null,"slug":"six-lectures-on-linearized-neural-networks","title":"Six Lectures on Linearized Neural Networks","date":"2023-08-25","arxiv_id":"2308.13431","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-data-analysis-method-for-big","title":"An Efficient Data Analysis Method for Big Data using Multiple-Model Linear Regression","date":"2023-08-24","arxiv_id":"2308.12691","n_code_links":0,"syntology":null},{"paper":null,"slug":"extended-linear-regression-a-kalman-filter","title":"Extended Linear Regression: A Kalman Filter Approach for Minimizing Loss via Area Under the Curve","date":"2023-08-23","arxiv_id":"2308.12280","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-drug-solubility-using-different","title":"Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network","date":"2023-08-23","arxiv_id":"2308.12325","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-warming-in-ghana-s-major-cities-based","title":"Global Warming In Ghana's Major Cities Based on Statistical Analysis of NASA's POWER Over 3-Decades","date":"2023-08-20","arxiv_id":"2308.10909","n_code_links":0,"syntology":null},{"paper":null,"slug":"polynomial-bounds-for-learning-noisy-optical","title":"Polynomial Bounds for Learning Noisy Optical Physical Unclonable Functions and Connections to Learning With Errors","date":"2023-08-17","arxiv_id":"2308.09199","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-framework-for-spleen-volume","title":"Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views","date":"2023-08-15","arxiv_id":"2308.08038","n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-zca-whitening-effects-of-linear","title":"Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation","date":"2023-08-15","arxiv_id":"2308.13536","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-offensive-gameplan-in-the-national","title":"Optimizing Offensive Gameplan in the National Basketball Association with Machine Learning","date":"2023-08-13","arxiv_id":"2308.06851","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-approach-to-overcoming-zero-trade-in","title":"A New Approach to Overcoming Zero Trade in Gravity Models to Avoid Indefinite Values in Linear Logarithmic Equations and Parameter Verification Using Machine Learning","date":"2023-08-11","arxiv_id":"2308.06303","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-interpretation-of-linear-social","title":"Causal Interpretation of Linear Social Interaction Models with Endogenous Networks","date":"2023-08-08","arxiv_id":"2308.04276","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-sketching-for-secure-coded","title":"Iterative Sketching for Secure Coded Regression","date":"2023-08-08","arxiv_id":"2308.04185","n_code_links":0,"syntology":null},{"paper":"/paper/how-to-forecast-power-generation-in-wind","slug":"how-to-forecast-power-generation-in-wind","title":"Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure","date":"2023-08-07","arxiv_id":"2308.03472","n_code_links":1,"syntology":null},{"paper":null,"slug":"gradient-coding-through-iterative-block","title":"Gradient Coding with Iterative Block Leverage Score Sampling","date":"2023-08-06","arxiv_id":"2308.03096","n_code_links":0,"syntology":null},{"paper":null,"slug":"obeseye-interpretable-diet-recommender-for","title":"OBESEYE: Interpretable Diet Recommender for Obesity Management using Machine Learning and Explainable AI","date":"2023-08-05","arxiv_id":"2308.02796","n_code_links":0,"syntology":null},{"paper":null,"slug":"sabre-robust-bayesian-peer-to-peer-federated","title":"SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection","date":"2023-08-04","arxiv_id":"2308.02747","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-linear-regression-phase-transitions","title":"Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms","date":"2023-08-01","arxiv_id":"2308.00556","n_code_links":0,"syntology":null},{"paper":null,"slug":"pupil-learning-mechanism","title":"Pupil Learning Mechanism","date":"2023-07-30","arxiv_id":"2307.16141","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-practical-robustness-auditing-for","title":"Towards Practical Robustness Auditing for Linear Regression","date":"2023-07-30","arxiv_id":"2307.16315","n_code_links":0,"syntology":null},{"paper":null,"slug":"characteristics-and-predictive-modeling-of","title":"Characteristics and Predictive Modeling of Short-term Impacts of Hurricanes on the US Employment","date":"2023-07-25","arxiv_id":"2307.13686","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrating-processed-based-models-and","title":"Integrating processed-based models and machine learning for crop yield prediction","date":"2023-07-25","arxiv_id":"2307.13466","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-debiased-machine-learning-using-data","title":"Adaptive debiased machine learning using data-driven model selection techniques","date":"2023-07-24","arxiv_id":"2307.12544","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-predictive-control-mpc-of-an-artificial","title":"Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors","date":"2023-07-22","arxiv_id":"2307.12015","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-linear-regression-with-cauchy-prior","title":"Bayesian Linear Regression with Cauchy Prior and Its Application in Sparse MIMO Radar","date":"2023-07-20","arxiv_id":"2307.11233","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-linear-estimating-equations","slug":"adaptive-linear-estimating-equations","title":"Adaptive Linear Estimating Equations","date":"2023-07-14","arxiv_id":"2307.07320","n_code_links":1,"syntology":null},{"paper":null,"slug":"power-consumption-prediction-for-steel","title":"Power consumption prediction for steel industry","date":"2023-07-14","arxiv_id":"2307.07597","n_code_links":0,"syntology":null},{"paper":null,"slug":"metal-oxide-based-gas-sensor-array-for-the","title":"Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning","date":"2023-07-13","arxiv_id":"2307.06556","n_code_links":0,"syntology":null},{"paper":null,"slug":"outlier-detection-in-regression-conic","title":"Outlier detection in regression: conic quadratic formulations","date":"2023-07-12","arxiv_id":"2307.05975","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-linear-regression-for-iteratively","title":"Using Linear Regression for Iteratively Training Neural Networks","date":"2023-07-11","arxiv_id":"2307.05189","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-estimation-to-sampling-for-bayesian","title":"From Estimation to Sampling for Bayesian Linear Regression with Spike-and-Slab Prior","date":"2023-07-09","arxiv_id":"2307.05558","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-step-of-gradient-descent-is-provably-the","title":"One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention","date":"2023-07-07","arxiv_id":"2307.03576","n_code_links":0,"syntology":null}],"record_sha256":"78095533bb11164f80875c5cbf730089938bb15be06b1b713bc1c8fba924422d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}