{"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/42","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":42,"pages_in_order":95,"rows_per_page":100,"rows":[4101,4200],"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/41","next":"/task/regression-1/papers/43","papers":[{"url":null,"slug":"learning-residual-model-of-model-predictive","title":"Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving","date":"2023-04-10","arxiv_id":"2304.04366","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-characterization-of-the-1","title":"Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning","date":"2023-04-09","arxiv_id":"2304.04312","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-anti-regularized-ensembles-provide","title":"Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification","date":"2023-04-08","arxiv_id":"2304.04042","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-regression-over-adaptive-networks","title":"Compressed Regression over Adaptive Networks","date":"2023-04-07","arxiv_id":"2304.03638","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-active-learning-framework-for","title":"A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction","date":"2023-04-06","arxiv_id":"2304.13032","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-calibration-of-mechatronic-systems","title":"Cascaded Calibration of Mechatronic Systems via Bayesian Inference","date":"2023-04-06","arxiv_id":"2304.03136","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-regression-in-calorie-prediction","title":"Conformal Regression in Calorie Prediction for Team Jumbo-Visma","date":"2023-04-06","arxiv_id":"2304.03778","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-hvac-control-using-symbolic","title":"Data-driven HVAC Control Using Symbolic Regression: Design and Implementation","date":"2023-04-06","arxiv_id":"2304.03078","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-linear-kernel-regression-and-imputation","title":"Multi-Linear Kernel Regression and Imputation in Data Manifolds","date":"2023-04-06","arxiv_id":"2304.03041","repositories_listed":0,"syntology":null},{"url":null,"slug":"spintronic-physical-reservoir-for-autonomous","title":"Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting","date":"2023-04-06","arxiv_id":"2304.03343","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-regression-via-approximate-message","title":"Mixed Regression via Approximate Message Passing","date":"2023-04-05","arxiv_id":"2304.02229","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sketching-bounds-for-sparse-linear","title":"Optimal Sketching Bounds for Sparse Linear Regression","date":"2023-04-05","arxiv_id":"2304.02261","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformalized-unconditional-quantile","title":"Conformalized Unconditional Quantile Regression","date":"2023-04-04","arxiv_id":"2304.01426","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-rates-of-approximation-by-shallow","title":"Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression","date":"2023-04-04","arxiv_id":"2304.01561","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-symbolic-regression-for-data","title":"Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition","date":"2023-04-03","arxiv_id":"2304.01117","repositories_listed":0,"syntology":null},{"url":null,"slug":"endogenous-linear-regressions-with-included","title":"IV Regressions without Exclusion Restrictions","date":"2023-04-02","arxiv_id":"2304.00626","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-reachability-calibration-with","title":"Multi-Agent Reachability Calibration with Conformal Prediction","date":"2023-04-02","arxiv_id":"2304.00432","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-covariance-unfolding-regression-a","title":"Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data","date":"2023-03-31","arxiv_id":"2303.17852","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-nonlinear-regression-under-l2-loss","title":"A Note On Nonlinear Regression Under L2 Loss","date":"2023-03-30","arxiv_id":"2303.17745","repositories_listed":0,"syntology":null},{"url":null,"slug":"steered-mixture-of-experts-regression-for","title":"Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference","date":"2023-03-30","arxiv_id":"2303.17409","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-over-parameterized-exponential-regression","title":"An Over-parameterized Exponential Regression","date":"2023-03-29","arxiv_id":"2303.16504","repositories_listed":0,"syntology":null},{"url":null,"slug":"futures-quantitative-investment-with","title":"Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network","date":"2023-03-29","arxiv_id":"2303.16532","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-interpolation-generalizes-poorly","title":"Kernel interpolation generalizes poorly","date":"2023-03-28","arxiv_id":"2303.15809","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnability-sample-complexity-and-hypothesis","title":"Learnability, Sample Complexity, and Hypothesis Class Complexity for Regression Models","date":"2023-03-28","arxiv_id":"2303.16091","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-regularized-exp-cosh-and-sinh","title":"Solving Regularized Exp, Cosh and Sinh Regression Problems","date":"2023-03-28","arxiv_id":"2303.15725","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-quantifying-calibrated-uncertainty","title":"Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task","date":"2023-03-28","arxiv_id":"2303.16210","repositories_listed":0,"syntology":null},{"url":null,"slug":"adjusted-wasserstein-distributionally-robust","title":"Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning","date":"2023-03-27","arxiv_id":"2303.15579","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-convergence-in-quantum-neural","title":"Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels","date":"2023-03-26","arxiv_id":"2303.14844","repositories_listed":0,"syntology":null},{"url":null,"slug":"crrs-concentric-rectangles-regression","title":"CRRS: Concentric Rectangles Regression Strategy for Multi-point Representation on Fisheye Images","date":"2023-03-26","arxiv_id":"2303.14639","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-space-sketching-for-logistic","title":"Feature Space Sketching for Logistic Regression","date":"2023-03-24","arxiv_id":"2303.14284","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-exact-sample-complexity-gain-from","title":"The Exact Sample Complexity Gain from Invariances for Kernel Regression","date":"2023-03-24","arxiv_id":"2303.14269","repositories_listed":0,"syntology":null},{"url":null,"slug":"functional-coefficient-quantile-regression","title":"Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure","date":"2023-03-23","arxiv_id":"2303.13218","repositories_listed":0,"syntology":null},{"url":null,"slug":"logistic-regression-equivalence-a-framework","title":"Logistic Regression Equivalence: A Framework for Comparing Logistic Regression Models Across Populations","date":"2023-03-23","arxiv_id":"2303.13330","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-symbolic-learner-for-discovering","title":"Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database","date":"2023-03-23","arxiv_id":"2303.14179","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensorless-adaptive-vibration-suppression-in","title":"Sensorless Adaptive Vibration Suppression in Two-Mass Systems via Joint Estimation of Controller Parameters and System States","date":"2023-03-23","arxiv_id":"2303.13054","repositories_listed":0,"syntology":null},{"url":null,"slug":"siamthn-siamese-target-highlight-network-for","title":"SiamTHN: Siamese Target Highlight Network for Visual Tracking","date":"2023-03-22","arxiv_id":"2303.12304","repositories_listed":0,"syntology":null},{"url":null,"slug":"anchor-free-remote-sensing-detector-based-on","title":"Anchor Free remote sensing detector based on solving discrete polar coordinate equation","date":"2023-03-21","arxiv_id":"2303.11694","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactually-fair-regression-with-double","title":"Counterfactually Fair Regression with Double Machine Learning","date":"2023-03-21","arxiv_id":"2303.11529","repositories_listed":0,"syntology":null},{"url":null,"slug":"covrecon-combining-genome-scale-metabolic","title":"COVRECON: Combining Genome-scale Metabolic Network Reconstruction and Data-driven Inverse Modeling to Reveal Changes in Metabolic Interaction Networks","date":"2023-03-21","arxiv_id":"2303.12526","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-techniques-for-estimating","title":"Machine Learning Techniques for Estimating Soil Moisture from Mobile Captured Images","date":"2023-03-21","arxiv_id":"2303.11527","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-table-structure-recognition-with","title":"Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer","date":"2023-03-21","arxiv_id":"2303.11615","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-latent-space-regression-of-diffusion","title":"Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading","date":"2023-03-21","arxiv_id":"2303.12031","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-admm-approach-for-multi-response","title":"An ADMM approach for multi-response regression with overlapping groups and interaction effects","date":"2023-03-20","arxiv_id":"2303.11155","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-trip-generation-with-graph-neural","title":"Deep trip generation with graph neural networks for bike sharing system expansion","date":"2023-03-20","arxiv_id":"2303.11977","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-implicit-regularization-of-relu-neural","title":"How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer","date":"2023-03-20","arxiv_id":"2303.11454","repositories_listed":0,"syntology":null},{"url":null,"slug":"skeleton-regression-a-graph-based-approach-to","title":"Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure","date":"2023-03-19","arxiv_id":"2303.11786","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-semantic-interactive-learning-for","title":"Multi-Semantic Interactive Learning for Object Detection","date":"2023-03-18","arxiv_id":"2303.10411","repositories_listed":0,"syntology":null},{"url":null,"slug":"socs-semantically-aware-object-coordinate","title":"SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations","date":"2023-03-18","arxiv_id":"2303.10346","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-challenge-of-differentially-private","title":"The Challenge of Differentially Private Screening Rules","date":"2023-03-18","arxiv_id":"2303.10303","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-reference-gaussian-process-regression-1","title":"Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller","date":"2023-03-17","arxiv_id":"2303.09828","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-model-personalization-for","title":"Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks","date":"2023-03-17","arxiv_id":"2303.10254","repositories_listed":0,"syntology":null},{"url":null,"slug":"qubo-decision-tree-annealing-machine-extends","title":"QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting","date":"2023-03-17","arxiv_id":"2303.09772","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dual-branch-network-for-emotional-reaction","title":"A Dual Branch Network for Emotional Reaction Intensity Estimation","date":"2023-03-16","arxiv_id":"2303.09210","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-biomedical-image","title":"Machine learning based biomedical image processing for echocardiographic images","date":"2023-03-16","arxiv_id":"2303.09103","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-particle-accelerator","title":"Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression","date":"2023-03-15","arxiv_id":"2303.08984","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-optical-kernel-regression","title":"Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields","date":"2023-03-15","arxiv_id":"2303.08435","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-on-the-product-of","title":"Gaussian Process on the Product of Directional Manifolds","date":"2023-03-13","arxiv_id":"2303.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-for-pdes-using-pruned","title":"Symbolic Regression for PDEs using Pruned Differentiable Programs","date":"2023-03-13","arxiv_id":"2303.07009","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-hurricane-evacuation-decisions","title":"Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models","date":"2023-03-12","arxiv_id":"2303.06557","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-friction-coefficient-using","title":"Estimating friction coefficient using generative modelling","date":"2023-03-10","arxiv_id":"2303.05927","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-regression-with-infinite-width-neural","title":"Kernel Regression with Infinite-Width Neural Networks on Millions of Examples","date":"2023-03-09","arxiv_id":"2303.05420","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-tools-to-improve-nonlinear","title":"Machine learning tools to improve nonlinear modeling parameters of RC columns","date":"2023-03-09","arxiv_id":"2303.16140","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-3d-regression-with-projected","title":"Probabilistic 3d regression with projected huber distribution","date":"2023-03-09","arxiv_id":"2303.05245","repositories_listed":0,"syntology":null},{"url":null,"slug":"variance-aware-robust-reinforcement-learning","title":"Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards","date":"2023-03-09","arxiv_id":"2303.05606","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-path-in-regression-random-forest-looking","title":"A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review","date":"2023-03-08","arxiv_id":"2303.04693","repositories_listed":0,"syntology":null},{"url":null,"slug":"agnostic-pac-learning-of-k-juntas-using-l2","title":"Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression","date":"2023-03-08","arxiv_id":"2303.04859","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-causal-forests-for-multivariate","title":"Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment","date":"2023-03-08","arxiv_id":"2303.04874","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-numerical-algorithms-that-can","title":"A Survey of Numerical Algorithms that can Solve the Lasso Problems","date":"2023-03-07","arxiv_id":"2303.03576","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-interpolation-of-acoustic-transfer","title":"Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations","date":"2023-03-07","arxiv_id":"2303.03869","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicted-embedding-power-regression-for","title":"Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection","date":"2023-03-07","arxiv_id":"2303.04115","repositories_listed":0,"syntology":null},{"url":null,"slug":"primo-private-regression-in-multiple-outcomes","title":"PRIMO: Private Regression in Multiple Outcomes","date":"2023-03-07","arxiv_id":"2303.04195","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensembleiv-creating-instrumental-variables","title":"EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference","date":"2023-03-06","arxiv_id":"2303.02820","repositories_listed":0,"syntology":null},{"url":null,"slug":"evcenternet-uncertainty-estimation-for-object","title":"EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning","date":"2023-03-06","arxiv_id":"2303.03037","repositories_listed":0,"syntology":null},{"url":null,"slug":"censored-quantile-regression-with-many","title":"Censored Quantile Regression with Many Controls","date":"2023-03-05","arxiv_id":"2303.02784","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperpose-camera-pose-localization-using","title":"HyperPose: Camera Pose Localization using Attention Hypernetworks","date":"2023-03-05","arxiv_id":"2303.02610","repositories_listed":0,"syntology":null},{"url":null,"slug":"wader-at-semeval-2023-task-9-a-weak-labelling","title":"WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks","date":"2023-03-05","arxiv_id":"2303.02758","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-studies-of-unsupervised-and","title":"Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications","date":"2023-03-04","arxiv_id":"2303.02446","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-feature-selection-techniques","title":"Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms","date":"2023-03-04","arxiv_id":"2303.02467","repositories_listed":0,"syntology":null},{"url":null,"slug":"rweetminer-automatic-identification-and","title":"RweetMiner: Automatic identification and categorization of help requests on twitter during disasters","date":"2023-03-04","arxiv_id":"2303.02399","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensorized-lssvms-for-multitask-regression","title":"Tensorized LSSVMs for Multitask Regression","date":"2023-03-04","arxiv_id":"2303.02451","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-laplace-inspired-distribution-on-so-3-for","title":"A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation","date":"2023-03-03","arxiv_id":"2303.01743","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-driven-bounding-box-localization","title":"Confidence-driven Bounding Box Localization for Small Object Detection","date":"2023-03-03","arxiv_id":"2303.01803","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-for-forecasting","title":"Feature Selection with Annealing for Forecasting Financial Time Series","date":"2023-03-03","arxiv_id":"2303.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-high-dimensional-single-neuron-relu","title":"Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron","date":"2023-03-03","arxiv_id":"2303.02255","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-computational-tradeoffs-in-mixed","title":"Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression","date":"2023-03-03","arxiv_id":"2303.02118","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-notion-of-feature-importance-by","title":"A Notion of Feature Importance by Decorrelation and Detection of Trends by Random Forest Regression","date":"2023-03-02","arxiv_id":"2303.01156","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-estimation-of-intersection-bounds-a","title":"Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters","date":"2023-03-02","arxiv_id":"2303.00982","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-rate-optimal-regret-for-adversarial","title":"Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation","date":"2023-03-02","arxiv_id":"2303.01464","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-analysis-of-double-descent","title":"High-dimensional analysis of double descent for linear regression with random projections","date":"2023-03-02","arxiv_id":"2303.01372","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-penalized-deep-neural-networks","title":"Sparse-penalized deep neural networks estimator under weak dependence","date":"2023-03-02","arxiv_id":"2303.01406","repositories_listed":0,"syntology":null},{"url":null,"slug":"uzh-clyp-at-semeval-2023-task-9-head-first","title":"UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction","date":"2023-03-02","arxiv_id":"2303.01194","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-ep-with-probabilistic","title":"Variational EP with Probabilistic Backpropagation for Bayesian Neural Networks","date":"2023-03-02","arxiv_id":"2303.01540","repositories_listed":0,"syntology":null},{"url":null,"slug":"d2q-detr-decoupling-and-dynamic-queries-for","title":"D2Q-DETR: Decoupling and Dynamic Queries for Oriented Object Detection with Transformers","date":"2023-03-01","arxiv_id":"2303.00542","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-asymptotic-analysis-of-adaptive","title":"Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications","date":"2023-03-01","arxiv_id":"2303.01370","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-slam-ate-using-an-ensemble","title":"Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization","date":"2023-03-01","arxiv_id":"2303.00616","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sensor-placement-from-regression","title":"Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces","date":"2023-02-28","arxiv_id":"2303.00028","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluacion-del-efecto-del-pami-en-la","title":"Evaluación del efecto del PAMI en la cobertura en salud de los adultos mayores en Argentina","date":"2023-02-28","arxiv_id":"2302.14784","repositories_listed":0,"syntology":null},{"url":null,"slug":"disease-severity-regression-with-continuous","title":"Disease Severity Regression with Continuous Data Augmentation","date":"2023-02-24","arxiv_id":"2302.12482","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-training-instability-of-shuffling-sgd","title":"On the Training Instability of Shuffling SGD with Batch Normalization","date":"2023-02-24","arxiv_id":"2302.12444","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotically-unbiased-off-policy-policy","title":"Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments","date":"2023-02-23","arxiv_id":"2302.11725","repositories_listed":0,"syntology":null}],"record_sha256":"cbb2b9f0c0ce3728b1d91b86f1d1bfc80c94b3593efe2f8c719b12d4b54bf09e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}