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| 会议/期刊 | 论文 |
|---|---|
| sigmod2020 | PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models. |
| neurips2021 | BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain. |
| neurips2021 | Efficient Truncated Linear Regression with Unknown Noise Variance. |
| neurips2021 | Improving Conditional Coverage via Orthogonal Quantile Regression. |
| neurips2021 | UCB-based Algorithms for Multinomial Logistic Regression Bandits. |
| neurips2021 | Statistical Query Lower Bounds for List-Decodable Linear Regression. |
| neurips2021 | Robust Regression Revisited: Acceleration and Improved Estimation Rates. |
| neurips2021 | Support vector machines and linear regression coincide with very high-dimensional features. |
| neurips2021 | Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex. |
| neurips2021 | Ising Model Selection Using ℓ 1 \ell_{1} ℓ1-Regularized Linear Regression: A Statistical Mechanics Analysis. |
| neurips2021 | ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. |
| neurips2021 | Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions. |
| neurips2021 | Distribution-free inference for regression: discrete, continuous, and in between. |
| neurips2021 | How Data Augmentation affects Optimization for Linear Regression. |
| neurips2021 | Parameter-free HE-friendly Logistic Regression. |
| neurips2021 | A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression. |
| neurips2021 | Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics. |
| neurips2021 | Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. |
| neurips2021 | Non-Gaussian Gaussian Processes for Few-Shot Regression. |
| neurips2021 | Scalable Quasi-Bayesian Inference for Instrumental Variable Regression. |
| neurips2021 | Out-of-Distribution Generalization in Kernel Regression. |
| neurips2021 | Adversarial Regression with Doubly Non-negative Weighting Matrices. |
| neurips2021 | α \alpha α-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression. |
| neurips2021 | Generic Neural Architecture Search via Regression. |
| neurips2021 | Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. |
| neurips2021 | Unbalanced Optimal Transport through Non-negative Penalized Linear Regression. |
| neurips2021 | Mixability made efficient: Fast online multiclass logistic regression. |
| neurips2021 | Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. |
| neurips2021 | Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. |
| neurips2021 | Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging. |
| neurips2021 | Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. |
| neurips2021 | Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers. |
| neurips2021 | ReLU Regression with Massart Noise. |
| neurips2021 | Stateful Strategic Regression. |
| neurips2021 | On Optimal Interpolation in Linear Regression. |
| neurips2021 | SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. |
| neurips2021 | A Regression Approach to Learning-Augmented Online Algorithms. |
| neurips2020 | Coresets for Regressions with Panel Data. |
| neurips2020 | Detection as Regression: Certified Object Detection with Median Smoothing. |
| neurips2020 | Online Robust Regression via SGD on the l1 loss. |
| neurips2020 | Dual Instrumental Variable Regression. |
| neurips2020 | Adaptive Reduced Rank Regression. |
| neurips2020 | Robust Meta-learning for Mixed Linear Regression with Small Batches. |
| neurips2020 | Randomized tests for high-dimensional regression: A more efficient and powerful solution. |
| neurips2020 | AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. |
| neurips2020 | RepPoints v2: Verification Meets Regression for Object Detection. |
| neurips2020 | Neuronal Gaussian Process Regression. |
| neurips2020 | Fair regression with Wasserstein barycenters. |
| neurips2020 | Critic Regularized Regression. |
| neurips2020 | Myersonian Regression. |
| neurips2020 | On the Optimal Weighted ℓ 2 \ell_2 ℓ2 Regularization in Overparameterized Linear Regression. |
| neurips2020 | Truncated Linear Regression in High Dimensions. |
| neurips2020 | Smooth And Consistent Probabilistic Regression Trees. |
| neurips2020 | An implicit function learning approach for parametric modal regression. |
| neurips2020 | Sample complexity and effective dimension for regression on manifolds. |
| neurips2020 | Spike and slab variational Bayes for high dimensional logistic regression. |
| neurips2020 | Deep Evidential Regression. |
| neurips2020 | Non-Crossing Quantile Regression for Distributional Reinforcement Learning. |
| neurips2020 | Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. |
| neurips2020 | Calibrated Reliable Regression using Maximum Mean Discrepancy. |
| neurips2020 | A convex optimization formulation for multivariate regression. |
| neurips2020 | Fair regression via plug-in estimator and recalibration with statistical guarantees. |
| neurips2020 | Regression with reject option and application to kNN. |
| neurips2020 | LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond. |
| kdd2021 | Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. |
| kdd2021 | When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control. |
| kdd2020 | Residual Correlation in Graph Neural Network Regression. |
| kdd2020 | Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications. |
| ACMMM2021 | Multiple Object Tracking by Trajectory Map Regression with Temporal Priors Embedding. |
| ACMMM2021 | Knowing When to Quit: Selective Cascaded Regression with Patch Attention for Real-Time Face Alignment. |
| ACMMM2021 | Decoupled IoU Regression for Object Detection. |
| ACMMM2020 | Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer. |
| ACMMM2020 | Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking. |
| ACMMM2020 | Region of Interest Based Graph Convolution: A Heatmap Regression Approach for Action Unit Detection. |
| AAAI2021 | Adversarial Pose Regression Network for Pose-Invariant Face Recognitions. |
| AAAI2021 | Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression. |
| AAAI2021 | Longitudinal Deep Kernel Gaussian Process Regression. |
| AAAI2021 | A General Class of Transfer Learning Regression without Implementation Cost. |
| AAAI2020 | OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation. |
| AAAI2020 | Regression under Human Assistance. |
| AAAI2020 | Privacy-Preserving Gaussian Process Regression - A Modular Approach to the Application of Homomorphic Encryption. |
| AAAI2020 | Projective Quadratic Regression for Online Learning. |
| AAAI2020 | Pairwise Fairness for Ranking and Regression. |
| AAAI2020 | Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces. |
| AAAI2020 | Improved PAC-Bayesian Bounds for Linear Regression. |
| AAAI2020 | Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization. |
| AAAI2020 | Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. |
| AAAI2020 | Estimating Stochastic Linear Combination of Non-Linear Regressions. |
| AAAI2020 | ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. |
| AAAI2020 | Causally Denoise Word Embeddings Using Half-Sibling Regression. |
| AAAI2020 | AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation. |
| AAAI2020 | Age Progression and Regression with Spatial Attention Modules. |
| AAAI2020 | Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume. |
| AAAI2020 | Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. |
| ICML2021 | Sparse Bayesian Learning via Stepwise Regression. |
| ICML2021 | Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. |
| ICML2021 | Neural Symbolic Regression that scales. |
| ICML2021 | Representation Subspace Distance for Domain Adaptation Regression. |
| ICML2021 | Understanding and Mitigating Accuracy Disparity in Regression. |
| ICML2021 | Consistent regression when oblivious outliers overwhelm. |
| ICML2021 | A Wasserstein Minimax Framework for Mixed Linear Regression. |
| ICML2021 | Online A-Optimal Design and Active Linear Regression. |
| ICML2021 | In-Database Regression in Input Sparsity Time. |
| ICML2021 | Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. |
| ICML2021 | Adapting to misspecification in contextual bandits with offline regression oracles. |
| ICML2021 | Near-Optimal Linear Regression under Distribution Shift. |
| ICML2021 | The Earth Mover’s Pinball Loss: Quantiles for Histogram-Valued Regression. |
| ICML2021 | A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions. |
| ICML2021 | Oblivious Sketching for Logistic Regression. |
| ICML2021 | Inference for Network Regression Models with Community Structure. |
| ICML2021 | Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. |
| ICML2021 | Training Data Subset Selection for Regression with Controlled Generalization Error. |
| ICML2021 | Asymptotics of Ridge Regression in Convolutional Models. |
| ICML2021 | A Precise Performance Analysis of Support Vector Regression. |
| ICML2021 | Delving into Deep Imbalanced Regression. |
| ICML2020 | Safe screening rules for L0-regression from Perspective Relaxations. |
| ICML2020 | Model-Based Reinforcement Learning with Value-Targeted Regression. |
| ICML2020 | Fast OSCAR and OWL Regression via Safe Screening Rules. |
| ICML2020 | Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. |
| ICML2020 | Boosted Histogram Transform for Regression. |
| ICML2020 | On Coresets for Regularized Regression. |
| ICML2020 | Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. |
| ICML2020 | Randomly Projected Additive Gaussian Processes for Regression. |
| ICML2020 | Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. |
| ICML2020 | Partial Trace Regression and Low-Rank Kraus Decomposition. |
| ICML2020 | Meta-learning for Mixed Linear Regression. |
| ICML2020 | Nearly Linear Row Sampling Algorithm for Quantile Regression. |
| ICML2020 | Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. |
| ICML2020 | Causal Strategic Linear Regression. |
| ICML2020 | One-shot Distributed Ridge Regression in High Dimensions. |
| ICML2020 | Piecewise Linear Regression via a Difference of Convex Functions. |
| ICML2020 | Logistic Regression for Massive Data with Rare Events. |
| ICML2020 | Optimal Estimator for Unlabeled Linear Regression. |
| ICML2020 | Smaller, more accurate regression forests using tree alternating optimization. |
| ICML2019 | AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. |
| ICML2019 | Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. |
| ICML2019 | Rates of Convergence for Sparse Variational Gaussian Process Regression. |
| ICML2019 | Dimensionality Reduction for Tukey Regression. |
| ICML2019 | Improved Convergence for ℓ 1 \ell_1 ℓ1 and ℓ a ^ ˆ z ˇ \ell_∞ ℓa^ˆzˇ Regression via Iteratively Reweighted Least Squares. |
| ICML2019 | Distribution calibration for regression. |
| ICML2019 | On Sparse Linear Regression in the Local Differential Privacy Model. |
| ICML2019 | Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k k k-means Clustering. |
| ICLR2020 | Learning Disentangled Representations for CounterFactual Regression. |
| ICLR2020 | Dynamic Time Lag Regression: Predicting What & When. |
| ICLR2020 | Ridge Regression: Structure, Cross-Validation, and Sketching. |
| ICLR2021 | Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. |
| ICLR2021 | Knowledge distillation via softmax regression representation learning. |
| ICLR2021 | Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors. |
| ICLR2021 | On the Universality of the Double Descent Peak in Ridgeless Regression. |
| ICLR2021 | Using latent space regression to analyze and leverage compositionality in GANs. |
| ICLR2021 | Learning Deep Features in Instrumental Variable Regression. |
| ICLR2021 | A Hypergradient Approach to Robust Regression without Correspondence. |
| ICLR2021 | Dataset Meta-Learning from Kernel Ridge-Regression. |
| CVPR2021 | Progressive Contour Regression for Arbitrary-Shape Scene Text Detection. |
| CVPR2021 | CapsuleRRT: Relationships-Aware Regression Tracking via Capsules. |
| CVPR2021 | Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-Shape Responses in Statistical Shape Analysis. |
| CVPR2021 | Rethinking the Heatmap Regression for Bottom-Up Human Pose Estimation. |
| CVPR2021 | AGORA: Avatars in Geography Optimized for Regression Analysis. |
| CVPR2021 | Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression. |
| CVPR2021 | Positive-Congruent Training: Towards Regression-Free Model Updates. |
| CVPR2021 | Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression. |
| CVPR2021 | GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. |
| CVPR2020 | Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution. |
| CVPR2020 | SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. |
| CVPR2020 | 3D Human Mesh Regression With Dense Correspondence. |
| CVPR2020 | Probabilistic Regression for Visual Tracking. |
| CVPR2020 | Dense Regression Network for Video Grounding. |
| CVPR2020 | Hierarchical Scene Coordinate Classification and Regression for Visual Localization. |
| CVPR2020 | Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection. |
| CVPR2020 | Mixture Dense Regression for Object Detection and Human Pose Estimation. |
| CVPR2020 | Regularizing CNN Transfer Learning With Randomised Regression. |
| CVPRW2021 | A Mathematical Analysis of Learning Loss for Active Learning in Regression. |
| CVPRW2021 | 3D Fiber Segmentation With Deep Center Regression and Geometric Clustering. |
| CVPRW2020 | Extending Absolute Pose Regression to Multiple Scenes. |
| CVPRW2020 | ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization. |
| CVPRW2020 | Deep Regression for Imaging Solar Magnetograms using Pyramid Generative Adversarial Networks. |
| CVPRW2020 | Hierarchical Regression Network for Spectral Reconstruction from RGB Images. |
| ICCV2021 | Learning Multi-Scene Absolute Pose Regression with Transformers. |
| ICCV2021 | Generalized Shuffled Linear Regression. |
| ICCV2021 | Group-aware Contrastive Regression for Action Quality Assessment. |
| ICCV2021 | Human Pose Regression with Residual Log-likelihood Estimation. |
| ICCV2021 | Removing the Bias of Integral Pose Regression. |
| ICCV2021 | Monocular, One-stage, Regression of Multiple 3D People. |
| ICCV2021 | PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop. |
| ICCV2021 | H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression. |
| ICCV2021 | An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives. |
| ICCV2021 | FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration. |
| ICCV2019 | DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks. |
| ICCV2019 | Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression. |
| ICCV2019 | A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image. |
| ICCV2019 | Image Aesthetic Assessment Based on Pairwise Comparison  A Unified Approach to Score Regression, Binary Classification, and Personalization. |
| ICCV2019 | Probabilistic Deep Ordinal Regression Based on Gaussian Processes. |
| ICCV2019 | Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression. |
| ICCV2019 | Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation. |
| ACL2021 | Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates. |
| ACL2021 | Survival text regression for time-to-event prediction in conversations. |
| IJCAI2021 | AgeFlow: Conditional Age Progression and Regression with Normalizing Flows. |
| IJCAI2021 | SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking. |
| IJCAI2021 | Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic. |
| IJCAI2021 | Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning. |
| IJCAI2021-aisafety | An Adversarial Attacker for Neural Networks in Regression Problems. |
| IJCAI2020 | Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression. |
| IJCAI2020 | Scalable Gaussian Process Regression Networks. |
| IJCAI2020 | Sinkhorn Regression. |
| IJCAI2020 | Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation. |
| IJCAI2020 | An Interactive Visualization Platform for Deep Symbolic Regression. |
| TPAMI2022 | Learning Meta-Distance for Sequences by Learning a Ground Metric via Virtual Sequence Regression. |
| TPAMI2022 | Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition. |
| TPAMI2021 | Nonlinear Regression via Deep Negative Correlation Learning. |
| TPAMI2021 | Correction to “Nonlinear Regression via Deep Negative Correlation Learning”. |
| TPAMI2021 | Acceleration of Non-Rigid Point Set Registration With Downsampling and Gaussian Process Regression. |
| TPAMI2020 | Approximate Sparse Multinomial Logistic Regression for Classification. |
| TPAMI2020 | Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning. |
| TPAMI2020 | Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. |
| TPAMI2020 | A Comprehensive Analysis of Deep Regression. |
| TPAMI2020 | Confidence Propagation through CNNs for Guided Sparse Depth Regression. |
| IJCV2022 | Robust Geodesic Regression. |
| IJCV2022 | Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks. |
| IJCV2021 | Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography. |
| IJCV2021 | Learning Regression and Verification Networks for Robust Long-term Tracking. |
| IJCV2020 | Masked Linear Regression for Learning Local Receptive Fields for Facial Expression Synthesis. |
| IJCV2020 | CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis. |
| JMLR2022 | Interpolating Predictors in High-Dimensional Factor Regression. |
| JMLR2022 | Spatial Multivariate Trees for Big Data Bayesian Regression. |
| JMLR2022 | An improper estimator with optimal excess risk in misspecified density estimation and logistic regression. |
| JMLR2021 | Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression. |
| JMLR2021 | Sparse Tensor Additive Regression. |
| JMLR2021 | Testing Conditional Independence via Quantile Regression Based Partial Copulas. |
| JMLR2021 | Histogram Transform Ensembles for Large-scale Regression. |
| JMLR2021 | Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression. |
| JMLR2021 | Non-parametric Quantile Regression via the K-NN Fused Lasso. |
| JMLR2021 | Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model. |
| JMLR2021 | Optimal Rates of Distributed Regression with Imperfect Kernels. |
| JMLR2021 | Unlinked Monotone Regression. |
| JMLR2021 | Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond. |
| JMLR2021 | Differentially Private Regression and Classification with Sparse Gaussian Processes. |
| JMLR2021 | Soft Tensor Regression. |
| JMLR2021 | Classification vs regression in overparameterized regimes: Does the loss function matter? |
| JMLR2021 | Consistency of Gaussian Process Regression in Metric Spaces. |
| JMLR2021 | Statistically and Computationally Efficient Change Point Localization in Regression Settings. |
| JMLR2021 | Inference for the Case Probability in High-dimensional Logistic Regression. |
| JMLR2020 | A Statistical Learning Approach to Modal Regression. |
| JMLR2020 | Online Sufficient Dimension Reduction Through Sliced Inverse Regression. |
| JMLR2020 | Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables. |
| JMLR2020 | WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions. |
| JMLR2020 | Distributed Kernel Ridge Regression with Communications. |
| JMLR2020 | Prediction regions through Inverse Regression. |
| JMLR2020 | Tensor Regression Networks. |
| JMLR2020 | Convergence of Sparse Variational Inference in Gaussian Processes Regression. |
| JMLR2020 | Empirical Priors for Prediction in Sparse High-dimensional Linear Regression. |
| JMLR2020 | Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. |
| JMLR2020 | High Dimensional Forecasting via Interpretable Vector Autoregression. |
| JMLR2020 | Distributed High-dimensional Regression Under a Quantile Loss Function. |
| JMLR2020 | Conic Optimization for Quadratic Regression Under Sparse Noise. |
| JMLR2020 | Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models. |
| JMLR2020 | Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data. |
| JMLR2020 | Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging. |
| TOIS2020 | Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models. |
| TKDE2022 | An Online Robust Support Vector Regression for Data Streams. |
| TKDE2022 | Missing Value Imputation via Clusterwise Linear Regression. |
| TKDE2021 | MTBR: Multi-Target Boosting for Regression. |
| TKDE2021 | An Improved Quantum Algorithm for Ridge Regression. |
| TKDE2021 | Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression. |
| TKDE2020 | Semi-Supervised Feature Selection via Sparse Rescaled Linear Square Regression. |
| TKDE2020 | Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression. |
| AI2020 | Regression and progression in stochastic domains. |