| A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Kernel Multiple Change-point Algorithm via Model Selection |
N/A |
N/A |
N/A |
N/A |
N/A |
N/A |
N/A |
0 |
| A New Approach to Laplacian Solvers and Flow Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Representer Theorem for Deep Kernel Learning |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| A Representer Theorem for Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Well-Tempered Landscape for Non-convex Robust Subspace Recovery |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accelerated Alternating Projections for Robust Principal Component Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Active Learning for Cost-Sensitive Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptation Based on Generalized Discrepancy |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AffectiveTweets: a Weka Package for Analyzing Affect in Tweets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| An asymptotic analysis of distributed nonparametric methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analysis of Langevin Monte Carlo via Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Analysis of spectral clustering algorithms for community detection: the general bipartite setting |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate Profile Maximum Likelihood |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximation Algorithms for Stochastic Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximation Hardness for A Class of Sparse Optimization Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Approximations of the Restless Bandit Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Automated Scalable Bayesian Inference via Hilbert Coresets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization for Policy Search via Online-Offline Experimentation |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Best Arm Identification for Contaminated Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Binarsity: a penalization for one-hot encoded features in linear supervised learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Causal Learning via Manifold Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Characterizing the Sample Complexity of Pure Private Learners |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Collective Matrix Completion |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Complete Search for Feature Selection in Decision Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DBSCAN: Optimal Rates For Density-Based Cluster Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DPPy: DPP Sampling with Python |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DataWig: Missing Value Imputation for Tables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Decentralized Dictionary Learning Over Time-Varying Digraphs |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Decontamination of Mutual Contamination Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Exploration via Randomized Value Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Optimal Stopping |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Deep Reinforcement Learning for Swarm Systems |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Delay and Cooperation in Nonstochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dependent relevance determination for smooth and structured sparse regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Determinantal Point Processes for Coresets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Determining the Number of Latent Factors in Statistical Multi-Relational Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Differentiable Game Mechanics |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable reservoir computing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed Inference for Linear Support Vector Machine |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamic Pricing in High-dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient augmentation and relaxation learning for individualized treatment rules using observational data |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Embarrassingly Parallel Inference for Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exact Clustering of Weighted Graphs via Semidefinite Programming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fairness Constraints: A Flexible Approach for Fair Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fast Automatic Smoothing for Generalized Additive Models |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Forward-Backward Selection with Early Dropping |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Gaussian Processes with Linear Operator Inequality Constraints |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalized Maximum Entropy Estimation |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Generalized Score Matching for Non-Negative Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generic Inference in Latent Gaussian Process Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GraSPy: Graph Statistics in Python |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Graph Reduction with Spectral and Cut Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hamiltonian Monte Carlo with Energy Conserving Subsampling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| High-dimensional Varying Index Coefficient Models via Stein's Identity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Iterated Learning in Dynamic Social Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Joint PLDA for Simultaneous Modeling of Two Factors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernel Approximation Methods for Speech Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernels for Sequentially Ordered Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lazifying Conditional Gradient Algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learnability of Solutions to Conjunctive Queries |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Optimized Risk Scores |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Overcomplete, Low Coherence Dictionaries with Linear Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Representations of Persistence Barcodes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Unfaithful $K$-separable Gaussian Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning by Unsupervised Nonlinear Diffusion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Match via Inverse Optimal Transport |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Log-concave sampling: Metropolis-Hastings algorithms are fast |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Logical Explanations for Deep Relational Machines Using Relevance Information |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Low Permutation-rank Matrices: Structural Properties and Noisy Completion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Matched Bipartite Block Model with Covariates |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Measuring the Effects of Data Parallelism on Neural Network Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimal Sample Subspace Learning: Theory and Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Selection in Bayesian Neural Networks via Horseshoe Priors |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model Selection via the VC Dimension |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Monotone Learning with Rectified Wire Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| More Efficient Estimation for Logistic Regression with Optimal Subsamples |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-class Heterogeneous Domain Adaptation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multiclass Boosting: Margins, Codewords, Losses, and Algorithms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multiplicative local linear hazard estimation and best one-sided cross-validation |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| NetSDM: Semantic Data Mining with Network Analysis |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Architecture Search: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Empirical Bayes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| New Convergence Aspects of Stochastic Gradient Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| No-Regret Bayesian Optimization with Unknown Hyperparameters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Convex Matrix Completion and Related Problems via Strong Duality |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonparametric Bayesian Aggregation for Massive Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Nonuniformity of P-values Can Occur Early in Diverging Dimensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| ORCA: A Matlab/Octave Toolbox for Ordinal Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| On Asymptotic and Finite-Time Optimality of Bayesian Predictors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Consistent Vertex Nomination Schemes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the optimality of the Hedge algorithm in the stochastic regime |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Convergence Rates for Convex Distributed Optimization in Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Policies for Observing Time Series and Related Restless Bandit Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Transport: Fast Probabilistic Approximation with Exact Solvers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Parsimonious Online Learning with Kernels via Sparse Projections in Function Space |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Prediction Risk for the Horseshoe Regression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Provably Accurate Double-Sparse Coding |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Proximal Distance Algorithms: Theory and Practice |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PyOD: A Python Toolbox for Scalable Outlier Detection |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Pyro: Deep Universal Probabilistic Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Quantifying Uncertainty in Online Regression Forests |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Regularization via Mass Transportation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Robust Frequent Directions with Application in Online Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SMART: An Open Source Data Labeling Platform for Supervised Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable Approximations for Generalized Linear Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Interpretable Multi-Response Regression via SEED |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-Analytic Resampling in Lasso |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Shared Subspace Models for Multi-Group Covariance Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming |
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✅ |
❌ |
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❌ |
✅ |
2 |
| Simultaneous Private Learning of Multiple Concepts |
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❌ |
❌ |
❌ |
1 |
| Smooth neighborhood recommender systems |
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✅ |
❌ |
✅ |
5 |
| Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method |
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❌ |
✅ |
❌ |
✅ |
4 |
| Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Spectrum Estimation from a Few Entries |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Canonical Correlation Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Variance-Reduced Cubic Regularization Methods |
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✅ |
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❌ |
❌ |
✅ |
3 |
| Streaming Principal Component Analysis From Incomplete Data |
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❌ |
❌ |
❌ |
✅ |
2 |
| TensorLy: Tensor Learning in Python |
❌ |
✅ |
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✅ |
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3 |
| The Common-directions Method for Regularized Empirical Risk Minimization |
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✅ |
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✅ |
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5 |
| The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks |
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✅ |
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❌ |
✅ |
3 |
| The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient |
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1 |
| The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising |
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❌ |
✅ |
2 |
| Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems |
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❌ |
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❌ |
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1 |
| Tight Lower Bounds on the VC-dimension of Geometric Set Systems |
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❌ |
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0 |
| Time-to-Event Prediction with Neural Networks and Cox Regression |
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❌ |
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4 |
| Train and Test Tightness of LP Relaxations in Structured Prediction |
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2 |
| Transport Analysis of Infinitely Deep Neural Network |
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0 |
| Tunability: Importance of Hyperparameters of Machine Learning Algorithms |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets |
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5 |
| Unsupervised Basis Function Adaptation for Reinforcement Learning |
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3 |
| Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions |
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5 |
| Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots |
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❌ |
❌ |
✅ |
1 |
| Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations |
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❌ |
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3 |
| Variance-based Regularization with Convex Objectives |
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❌ |
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4 |
| Why do deep convolutional networks generalize so poorly to small image transformations? |
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✅ |
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3 |
| iNNvestigate Neural Networks! |
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❌ |
❌ |
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3 |
| scikit-multilearn: A Python library for Multi-Label Classification |
❌ |
✅ |
❌ |
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✅ |
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3 |
| spark-crowd: A Spark Package for Learning from Crowdsourced Big Data |
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3 |