| A Constructive Approach to $L_0$ Penalized Regression |
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3 |
| A Direct Approach for Sparse Quadratic Discriminant Analysis |
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4 |
| A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference |
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4 |
| A New and Flexible Approach to the Analysis of Paired Comparison Data |
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3 |
| A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation |
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5 |
| A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data |
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4 |
| A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization |
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2 |
| A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms |
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3 |
| A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations |
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4 |
| Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization |
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7 |
| An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach |
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3 |
| An efficient distributed learning algorithm based on effective local functional approximations |
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4 |
| Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection |
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2 |
| Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models |
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1 |
| Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points |
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4 |
| Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling |
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❌ |
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5 |
| Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models |
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✅ |
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❌ |
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2 |
| Covariances, Robustness, and Variational Bayes |
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✅ |
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4 |
| DALEX: Explainers for Complex Predictive Models in R |
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✅ |
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2 |
| Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations |
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4 |
| Design and Analysis of the NIPS 2016 Review Process |
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2 |
| Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters |
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3 |
| Distribution-Specific Hardness of Learning Neural Networks |
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0 |
| Dual Principal Component Pursuit |
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5 |
| ELFI: Engine for Likelihood-Free Inference |
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2 |
| Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes |
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4 |
| Emergence of Invariance and Disentanglement in Deep Representations |
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3 |
| Experience Selection in Deep Reinforcement Learning for Control |
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3 |
| Extrapolating Expected Accuracies for Large Multi-Class Problems |
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4 |
| Fast MCMC Sampling Algorithms on Polytopes |
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3 |
| Generalized Rank-Breaking: Computational and Statistical Tradeoffs |
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3 |
| Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials |
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1 |
| Gradient Descent Learns Linear Dynamical Systems |
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2 |
| Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery |
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4 |
| Hinge-Minimax Learner for the Ensemble of Hyperplanes |
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4 |
| How Deep Are Deep Gaussian Processes? |
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2 |
| Importance Sampling for Minibatches |
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4 |
| Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods |
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6 |
| Inference via Low-Dimensional Couplings |
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4 |
| Invariant Models for Causal Transfer Learning |
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5 |
| Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling |
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0 |
| Kernel Density Estimation for Dynamical Systems |
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1 |
| Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions |
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0 |
| Learning from Comparisons and Choices |
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4 |
| Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning |
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0 |
| Markov Blanket and Markov Boundary of Multiple Variables |
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6 |
| Maximum Selection and Sorting with Adversarial Comparators |
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1 |
| Model-Free Trajectory-based Policy Optimization with Monotonic Improvement |
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2 |
| Modular Proximal Optimization for Multidimensional Total-Variation Regularization |
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5 |
| Multivariate Bayesian Structural Time Series Model |
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2 |
| Numerical Analysis near Singularities in RBF Networks |
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2 |
| On Generalized Bellman Equations and Temporal-Difference Learning |
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2 |
| On Semiparametric Exponential Family Graphical Models |
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3 |
| On Tight Bounds for the Lasso |
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0 |
| Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator |
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2 |
| OpenEnsembles: A Python Resource for Ensemble Clustering |
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4 |
| Optimal Bounds for Johnson-Lindenstrauss Transformations |
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0 |
| Optimal Quantum Sample Complexity of Learning Algorithms |
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0 |
| Parallelizing Spectrally Regularized Kernel Algorithms |
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1 |
| Patchwork Kriging for Large-scale Gaussian Process Regression |
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5 |
| Profile-Based Bandit with Unknown Profiles |
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2 |
| RSG: Beating Subgradient Method without Smoothness and Strong Convexity |
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3 |
| Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem |
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❌ |
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2 |
| Refining the Confidence Level for Optimistic Bandit Strategies |
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1 |
| Regularized Optimal Transport and the Rot Mover's Distance |
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5 |
| Reverse Iterative Volume Sampling for Linear Regression |
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2 |
| Robust PCA by Manifold Optimization |
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4 |
| Robust Synthetic Control |
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4 |
| Scalable Bayes via Barycenter in Wasserstein Space |
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7 |
| Scaling up Data Augmentation MCMC via Calibration |
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4 |
| Scikit-Multiflow: A Multi-output Streaming Framework |
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2 |
| Seglearn: A Python Package for Learning Sequences and Time Series |
❌ |
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6 |
| Short-term Sparse Portfolio Optimization Based on Alternating Direction Method of Multipliers |
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4 |
| Simple Classification Using Binary Data |
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4 |
| Sparse Estimation in Ising Model via Penalized Monte Carlo Methods |
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4 |
| State-by-state Minimax Adaptive Estimation for Nonparametric Hidden {M}arkov Models |
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4 |
| Statistical Analysis and Parameter Selection for Mapper |
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3 |
| Streaming kernel regression with provably adaptive mean, variance, and regularization |
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2 |
| The Implicit Bias of Gradient Descent on Separable Data |
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3 |
| The xyz algorithm for fast interaction search in high-dimensional data |
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5 |
| Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier |
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1 |
| ThunderSVM: A Fast SVM Library on GPUs and CPUs |
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4 |
| Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems |
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❌ |
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0 |
| Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations |
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❌ |
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4 |