| A Bayesian Framework for Learning Rule Sets for Interpretable Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Cluster Elastic Net for Multivariate Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| A Robust-Equitable Measure for Feature Ranking and Selection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Spectral Algorithm for Inference in Hidden semi-Markov Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Survey of Preference-Based Reinforcement Learning Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Theory of Learning with Corrupted Labels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Tight Bound of Hard Thresholding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A distributed block coordinate descent method for training l1 regularized linear classifiers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A survey of Algorithms and Analysis for Adaptive Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Accelerating Stochastic Composition Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Achieving Optimal Misclassification Proportion in Stochastic Block Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Active Nearest-Neighbor Learning in Metric Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Active-set Methods for Submodular Minimization Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive Randomized Dimension Reduction on Massive Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analyzing Tensor Power Method Dynamics in Overcomplete Regime |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Angle-based Multicategory Distance-weighted SVM |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Approximation Vector Machines for Large-scale Online Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asymptotic behavior of Support Vector Machine for spiked population model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Automatic Differentiation Variational Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Automatic Differentiation in Machine Learning: a Survey |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Averaged Collapsed Variational Bayes Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Inference for Spatio-temporal Spike-and-Slab Priors |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Bayesian Learning of Dynamic Multilayer Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Network Learning via Topological Order |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Bayesian Tensor Regression |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Breaking the Curse of Dimensionality with Convex Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bridging Supervised Learning and Test-Based Co-optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Certifiably Optimal Low Rank Factor Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Characteristic and Universal Tensor Product Kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Classification of Time Sequences using Graphs of Temporal Constraints |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Clustering from General Pairwise Observations with Applications to Time-varying Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Clustering with Hidden Markov Model on Variable Blocks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| CoCoA: A General Framework for Communication-Efficient Distributed Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Communication-efficient Sparse Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Community Detection and Stochastic Block Models: Recent Developments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Community Extraction in Multilayer Networks with Heterogeneous Community Structure |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Compact Convex Projections |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Computational Limits of A Distributed Algorithm for Smoothing Spline |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Concentration inequalities for empirical processes of linear time series |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Confidence Sets with Expected Sizes for Multiclass Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence of Unregularized Online Learning Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convolutional Neural Networks Analyzed via Convolutional Sparse Coding |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cost-Sensitive Learning with Noisy Labels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Learning the Ising Model Near Criticality |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Density Estimation in Infinite Dimensional Exponential Families |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Differential Privacy for Bayesian Inference through Posterior Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dimension Estimation Using Random Connection Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Distributed Learning with Regularized Least Squares |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed Semi-supervised Learning with Kernel Ridge Regression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Document Neural Autoregressive Distribution Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Sampling from Time-Varying Log-Concave Distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Identification of Causal Effects by Pruning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Estimation of Graphical Models through Structured Norm Minimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Exact Learning of Lightweight Description Logic Ontologies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Faithfulness of Probability Distributions and Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fisher Consistency for Prior Probability Shift |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| From Predictive Methods to Missing Data Imputation: An Optimization Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fundamental Conditions for Low-CP-Rank Tensor Completion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GPflow: A Gaussian Process Library using TensorFlow |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Gap Safe Screening Rules for Sparsity Enforcing Penalties |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gaussian Lower Bound for the Information Bottleneck Limit |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Conditional Gradient for Sparse Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Estimation with Simultaneous Perturbation and Compressive Sensing |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Hard Thresholding Pursuit |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Group Sparse Optimization via lp,q Regularization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hierarchical Clustering via Spreading Metrics |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Hierarchically Compositional Kernels for Scalable Nonparametric Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hinge-Loss Markov Random Fields and Probabilistic Soft Logic |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Identifying a Minimal Class of Models for High--dimensional Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved spectral community detection in large heterogeneous networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Variational Methods via Pairwise Linear Response Identities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interactive Algorithms: Pool, Stream and Precognitive Stream |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| JSAT: Java Statistical Analysis Tool, a Library for Machine Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Joint Label Inference in Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| KELP: a Kernel-based Learning Platform |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Katyusha: The First Direct Acceleration of Stochastic Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Kernel Partial Least Squares for Stationary Data |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Knowledge Graph Completion via Complex Tensor Factorization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Certifiably Optimal Rule Lists for Categorical Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Local Dependence In Ordered Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS) |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Scalable Deep Kernels with Recurrent Structure |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Local algorithms for interactive clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matrix Completion with Noisy Entries and Outliers |
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4 |
| Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard |
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0 |
| Maximum Principle Based Algorithms for Deep Learning |
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4 |
| Memory Efficient Kernel Approximation |
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6 |
| Minimax Estimation of Kernel Mean Embeddings |
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0 |
| Minimax Filter: Learning to Preserve Privacy from Inference Attacks |
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5 |
| Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios |
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4 |
| Multiscale Strategies for Computing Optimal Transport |
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4 |
| Nearly optimal classification for semimetrics |
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2 |
| Non-parametric Policy Search with Limited Information Loss |
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3 |
| Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization |
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4 |
| Nonparametric Risk Bounds for Time-Series Forecasting |
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2 |
| Normal Bandits of Unknown Means and Variances |
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2 |
| On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data |
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2 |
| On Binary Embedding using Circulant Matrices |
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4 |
| On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models |
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2 |
| On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization |
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1 |
| On Markov chain Monte Carlo methods for tall data |
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5 |
| On Perturbed Proximal Gradient Algorithms |
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2 |
| On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness |
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1 |
| On the Consistency of Ordinal Regression Methods |
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2 |
| On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions |
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2 |
| On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks |
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0 |
| On the Stability of Feature Selection Algorithms |
❌ |
✅ |
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❌ |
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4 |
| Online Bayesian Passive-Aggressive Learning |
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5 |
| Online Learning to Rank with Top-k Feedback |
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3 |
| Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling |
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3 |
| Optimal Dictionary for Least Squares Representation |
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❌ |
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2 |
| Optimal Rates for Multi-pass Stochastic Gradient Methods |
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❌ |
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❌ |
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3 |
| POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty |
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✅ |
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1 |
| Parallel Symmetric Class Expression Learning |
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✅ |
❌ |
❌ |
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4 |
| Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification |
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❌ |
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❌ |
❌ |
❌ |
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2 |
| Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models |
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❌ |
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5 |
| Perishability of Data: Dynamic Pricing under Varying-Coefficient Models |
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2 |
| Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression |
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3 |
| Persistence Images: A Stable Vector Representation of Persistent Homology |
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4 |
| Poisson Random Fields for Dynamic Feature Models |
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4 |
| Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models |
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2 |
| Preference-based Teaching |
❌ |
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❌ |
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0 |
| Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model |
✅ |
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❌ |
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5 |
| Probabilistic Line Searches for Stochastic Optimization |
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5 |
| Probabilistic preference learning with the Mallows rank model |
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4 |
| Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data |
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3 |
| Pycobra: A Python Toolbox for Ensemble Learning and Visualisation |
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❌ |
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2 |
| Quantifying the Informativeness of Similarity Measurements |
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7 |
| Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations |
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❌ |
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6 |
| Rank Determination for Low-Rank Data Completion |
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0 |
| Rate of Convergence of $k$-Nearest-Neighbor Classification Rule |
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0 |
| Reconstructing Undirected Graphs from Eigenspaces |
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3 |
| Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements |
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3 |
| Refinery: An Open Source Topic Modeling Web Platform |
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1 |
| Regularization and the small-ball method II: complexity dependent error rates |
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0 |
| Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models |
✅ |
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✅ |
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3 |
| Relational Reinforcement Learning for Planning with Exogenous Effects |
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3 |
| Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks |
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4 |
| Risk-Constrained Reinforcement Learning with Percentile Risk Criteria |
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2 |
| Robust Discriminative Clustering with Sparse Regularizers |
✅ |
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❌ |
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4 |
| Robust Topological Inference: Distance To a Measure and Kernel Distance |
❌ |
✅ |
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❌ |
❌ |
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2 |
| Robust and Scalable Bayes via a Median of Subset Posterior Measures |
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❌ |
❌ |
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4 |
| SGDLibrary: A MATLAB library for stochastic optimization algorithms |
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❌ |
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3 |
| STORE: Sparse Tensor Response Regression and Neuroimaging Analysis |
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4 |
| Saturating Splines and Feature Selection |
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5 |
| Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks |
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✅ |
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❌ |
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5 |
| Second-Order Stochastic Optimization for Machine Learning in Linear Time |
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4 |
| Sharp Oracle Inequalities for Square Root Regularization |
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✅ |
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3 |
| Significance-based community detection in weighted networks |
✅ |
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❌ |
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❌ |
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4 |
| Simple, Robust and Optimal Ranking from Pairwise Comparisons |
❌ |
❌ |
✅ |
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❌ |
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1 |
| Simplifying Probabilistic Expressions in Causal Inference |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Simultaneous Clustering and Estimation of Heterogeneous Graphical Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| SnapVX: A Network-Based Convex Optimization Solver |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
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3 |
| Soft Margin Support Vector Classification as Buffered Probability Minimization |
✅ |
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1 |
| Sparse Concordance-assisted Learning for Optimal Treatment Decision |
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4 |
| Sparse Exchangeable Graphs and Their Limits via Graphon Processes |
❌ |
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❌ |
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0 |
| Spectral Clustering Based on Local PCA |
✅ |
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❌ |
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❌ |
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4 |
| Stability of Controllers for Gaussian Process Dynamics |
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❌ |
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3 |
| Stabilized Sparse Online Learning for Sparse Data |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Statistical Inference on Random Dot Product Graphs: a Survey |
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✅ |
❌ |
❌ |
❌ |
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4 |
| Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Statistical and Computational Guarantees for the Baum-Welch Algorithm |
❌ |
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❌ |
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1 |
| Steering Social Activity: A Stochastic Optimal Control Point Of View |
✅ |
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4 |
| Stochastic Gradient Descent as Approximate Bayesian Inference |
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4 |
| Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization |
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3 |
| Submatrix localization via message passing |
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1 |
| Surprising properties of dropout in deep networks |
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2 |
| Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks |
✅ |
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✅ |
❌ |
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5 |
| Tests of Mutual or Serial Independence of Random Vectors with Applications |
❌ |
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5 |
| The DFS Fused Lasso: Linear-Time Denoising over General Graphs |
❌ |
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✅ |
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❌ |
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3 |
| The Impact of Random Models on Clustering Similarity |
❌ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems |
❌ |
✅ |
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❌ |
❌ |
❌ |
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1 |
| The Search Problem in Mixture Models |
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3 |
| Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis |
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4 |
| Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality |
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5 |
| To Tune or Not to Tune the Number of Trees in Random Forest |
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4 |
| Training Gaussian Mixture Models at Scale via Coresets |
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4 |
| Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Uncovering Causality from Multivariate Hawkes Integrated Cumulants |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques |
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✅ |
✅ |
❌ |
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5 |
| Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns |
❌ |
✅ |
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❌ |
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❌ |
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3 |
| Variational Fourier Features for Gaussian Processes |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Variational Particle Approximations |
✅ |
❌ |
✅ |
✅ |
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❌ |
✅ |
4 |
| Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning |
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✅ |
❌ |
❌ |
❌ |
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3 |
| auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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3 |
| openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| pomegranate: Fast and Flexible Probabilistic Modeling in Python |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |