| A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors |
✅ |
✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| A Characterization of Linkage-Based Hierarchical Clustering |
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0 |
| A Closer Look at Adaptive Regret |
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1 |
| A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces |
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4 |
| A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights |
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2 |
| A General Framework for Consistency of Principal Component Analysis |
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❌ |
0 |
| A General Framework for Constrained Bayesian Optimization using Information-based Search |
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❌ |
✅ |
6 |
| A Gibbs Sampler for Learning DAGs |
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3 |
| A Network That Learns Strassen Multiplication |
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2 |
| A New Algorithm and Theory for Penalized Regression-based Clustering |
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4 |
| A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries |
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1 |
| A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees |
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3 |
| A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares |
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1 |
| A Unified View on Multi-class Support Vector Classification |
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❌ |
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✅ |
4 |
| A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning |
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✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes |
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✅ |
❌ |
✅ |
3 |
| A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty |
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✅ |
✅ |
6 |
| Adaptive Lasso and group-Lasso for functional Poisson regression |
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4 |
| Addressing Environment Non-Stationarity by Repeating Q-learning Updates |
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✅ |
2 |
| Adjusting for Chance Clustering Comparison Measures |
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✅ |
2 |
| An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning |
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✅ |
1 |
| An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension |
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✅ |
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✅ |
2 |
| An Information-Theoretic Analysis of Thompson Sampling |
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1 |
| An Online Convex Optimization Approach to Blackwell's Approachability |
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1 |
| Analysis of Classification-based Policy Iteration Algorithms |
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1 |
| Approximate Newton Methods for Policy Search in Markov Decision Processes |
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✅ |
2 |
| Are Random Forests Truly the Best Classifiers? |
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4 |
| Augmentable Gamma Belief Networks |
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❌ |
✅ |
6 |
| BayesPy: Variational Bayesian Inference in Python |
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3 |
| Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing |
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3 |
| Bayesian Graphical Models for Multivariate Functional Data |
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3 |
| Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models |
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❌ |
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5 |
| Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models |
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2 |
| Bayesian Policy Gradient and Actor-Critic Algorithms |
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4 |
| Bayesian group factor analysis with structured sparsity |
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4 |
| Bipartite Ranking: a Risk-Theoretic Perspective |
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3 |
| Blending Learning and Inference in Conditional Random Fields |
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5 |
| Bootstrap-Based Regularization for Low-Rank Matrix Estimation |
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✅ |
5 |
| Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach |
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4 |
| CVXPY: A Python-Embedded Modeling Language for Convex Optimization |
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1 |
| Causal Inference through a Witness Protection Program |
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5 |
| Cells in Multidimensional Recurrent Neural Networks |
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3 |
| Challenges in multimodal gesture recognition |
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3 |
| Characteristic Kernels and Infinitely Divisible Distributions |
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1 |
| Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation |
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3 |
| Classification of Imbalanced Data with a Geometric Digraph Family |
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❌ |
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6 |
| Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms |
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1 |
| Complexity of Representation and Inference in Compositional Models with Part Sharing |
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0 |
| Composite Multiclass Losses |
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0 |
| Compressed Gaussian Process for Manifold Regression |
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4 |
| Conditional Independencies under the Algorithmic Independence of Conditionals |
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0 |
| Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics |
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1 |
| Consistency of Cheeger and Ratio Graph Cuts |
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1 |
| Consistent Algorithms for Clustering Time Series |
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3 |
| Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables |
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3 |
| Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models |
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3 |
| Convergence of an Alternating Maximization Procedure |
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0 |
| Convex Calibration Dimension for Multiclass Loss Matrices |
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❌ |
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0 |
| Convex Regression with Interpretable Sharp Partitions |
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✅ |
✅ |
✅ |
✅ |
6 |
| Covariance-based Clustering in Multivariate and Functional Data Analysis |
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2 |
| Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders |
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2 |
| CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data |
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4 |
| DSA: Decentralized Double Stochastic Averaging Gradient Algorithm |
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3 |
| Data-driven Rank Breaking for Efficient Rank Aggregation |
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✅ |
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2 |
| Decrypting “Cryptogenic” Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients |
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❌ |
❌ |
✅ |
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2 |
| Differentially Private Data Releasing for Smooth Queries |
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✅ |
✅ |
❌ |
✅ |
6 |
| Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning |
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✅ |
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❌ |
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2 |
| Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Distributed Coordinate Descent Method for Learning with Big Data |
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4 |
| Distributed Submodular Maximization |
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3 |
| Distribution-Matching Embedding for Visual Domain Adaptation |
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4 |
| Domain-Adversarial Training of Neural Networks |
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✅ |
❌ |
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✅ |
5 |
| Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing |
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3 |
| Dual Control for Approximate Bayesian Reinforcement Learning |
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✅ |
2 |
| ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes |
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❌ |
✅ |
✅ |
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❌ |
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4 |
| Electronic Health Record Analysis via Deep Poisson Factor Models |
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❌ |
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4 |
| End-to-End Training of Deep Visuomotor Policies |
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✅ |
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3 |
| Equivalence of Graphical Lasso and Thresholding for Sparse Graphs |
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✅ |
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❌ |
❌ |
✅ |
2 |
| Estimating Causal Structure Using Conditional DAG Models |
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✅ |
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❌ |
✅ |
2 |
| Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm |
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2 |
| Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence |
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✅ |
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3 |
| Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums |
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❌ |
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❌ |
❌ |
❌ |
0 |
| Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics |
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✅ |
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✅ |
2 |
| Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision |
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4 |
| Extremal Mechanisms for Local Differential Privacy |
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1 |
| Feature-Level Domain Adaptation |
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4 |
| Fused Lasso Approach in Regression Coefficients Clustering -- Learning Parameter Heterogeneity in Data Integration |
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❌ |
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✅ |
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4 |
| Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case |
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❌ |
1 |
| GenSVM: A Generalized Multiclass Support Vector Machine |
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✅ |
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✅ |
✅ |
✅ |
7 |
| Gradients Weights improve Regression and Classification |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Guarding against Spurious Discoveries in High Dimensions |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Harry: A Tool for Measuring String Similarity |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Herded Gibbs Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Relative Entropy Policy Search |
✅ |
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❌ |
✅ |
❌ |
✅ |
3 |
| How to Center Deep Boltzmann Machines |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling |
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✅ |
❌ |
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4 |
| Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Integrated Common Sense Learning and Planning in POMDPs |
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1 |
| Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses |
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❌ |
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3 |
| Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Iterative Regularization for Learning with Convex Loss Functions |
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✅ |
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❌ |
✅ |
2 |
| JCLAL: A Java Framework for Active Learning |
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✅ |
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✅ |
❌ |
✅ |
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4 |
| Joint Structural Estimation of Multiple Graphical Models |
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✅ |
3 |
| Jointly Informative Feature Selection Made Tractable by Gaussian Modeling |
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❌ |
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2 |
| Kernel Estimation and Model Combination in A Bandit Problem with Covariates |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernel Mean Shrinkage Estimators |
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✅ |
✅ |
❌ |
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✅ |
3 |
| Knowledge Matters: Importance of Prior Information for Optimization |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs |
✅ |
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❌ |
❌ |
❌ |
✅ |
2 |
| LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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4 |
| LLORMA: Local Low-Rank Matrix Approximation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Large Scale Online Kernel Learning |
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❌ |
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6 |
| Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning |
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3 |
| Latent Space Inference of Internet-Scale Networks |
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4 |
| Learning Algorithms for Second-Price Auctions with Reserve |
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❌ |
✅ |
4 |
| Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview |
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3 |
| Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation |
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4 |
| Learning Planar Ising Models |
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4 |
| Learning Taxonomy Adaptation in Large-scale Classification |
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4 |
| Learning Theory for Distribution Regression |
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1 |
| Learning Using Anti-Training with Sacrificial Data |
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❌ |
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6 |
| Learning the Variance of the Reward-To-Go |
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2 |
| Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle |
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1 |
| Lenient Learning in Independent-Learner Stochastic Cooperative Games |
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3 |
| Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Loss Minimization and Parameter Estimation with Heavy Tails |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MEKA: A Multi-label/Multi-target Extension to WEKA |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| MLlib: Machine Learning in Apache Spark |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Machine Learning in an Auction Environment |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Measuring Dependence Powerfully and Equitably |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Megaman: Scalable Manifold Learning in Python |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Minimax Adaptive Estimation of Nonparametric Hidden Markov Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimax Rates in Permutation Estimation for Feature Matching |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Minimum Density Hyperplanes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-free Variable Selection in Reproducing Kernel Hilbert Space |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modelling Interactions in High-dimensional Data with Backtracking |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Monotonic Calibrated Interpolated Look-Up Tables |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-Objective Markov Decision Processes for Data-Driven Decision Support |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Multi-scale Classification using Localized Spatial Depth |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multi-task Sparse Structure Learning with Gaussian Copula Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multiple Output Regression with Latent Noise |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiple-Instance Learning from Distributions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiplicative Multitask Feature Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiscale Adaptive Representation of Signals: I. The Basic Framework |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multivariate Spearman's $\rho$ for Aggregating Ranks Using Copulas |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mutual Information Based Matching for Causal Inference with Observational Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Autoregressive Distribution Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| New Perspectives on k-Support and Cluster Norms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neyman-Pearson Classification under High-Dimensional Settings |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Noisy Sparse Subspace Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-linear Causal Inference using Gaussianity Measures |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Nonparametric Network Models for Link Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| OLPS: A Toolbox for On-Line Portfolio Selection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Bayes Risk Lower Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Lower and Upper Bounds in Smooth and Strongly Convex Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm |
N/A |
N/A |
N/A |
N/A |
N/A |
N/A |
N/A |
0 |
| On the Influence of Momentum Acceleration on Online Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the properties of variational approximations of Gibbs posteriors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| One-class classification of point patterns of extremes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Online PCA with Optimal Regret |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Operator-valued Kernels for Learning from Functional Response Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Optimal Estimation and Completion of Matrices with Biclustering Structures |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Estimation of Derivatives in Nonparametric Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Optimal Learning Rates for Localized SVMs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Practical Kernel-Based Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Low-Rank Matrix Completion from Quantized Measurements |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RLScore: Regularized Least-Squares Learners |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
5 |
| Random Rotation Ensembles |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Refined Error Bounds for Several Learning Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regularized Policy Iteration with Nonparametric Function Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revisiting the Nyström Method for Improved Large-scale Machine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Rounding-based Moves for Semi-Metric Labeling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scalable Learning of Bayesian Network Classifiers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Should We Really Use Post-Hoc Tests Based on Mean-Ranks? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Sparse PCA via Covariance Thresholding |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Ranking using Seriation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stability and Generalization in Structured Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stable Graphical Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| String and Membrane Gaussian Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| StructED: Risk Minimization in Structured Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Structure Discovery in Bayesian Networks by Sampling Partial Orders |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Structure-Leveraged Methods in Breast Cancer Risk Prediction |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Subspace Learning with Partial Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Synergy of Monotonic Rules |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| The Asymptotic Performance of Linear Echo State Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Benefit of Multitask Representation Learning |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Constrained Dantzig Selector with Enhanced Consistency |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The LRP Toolbox for Artificial Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| The Optimal Sample Complexity of PAC Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Statistical Performance of Collaborative Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Teaching Dimension of Linear Learners |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Trend Filtering on Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| True Online Temporal-Difference Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Variational Dependent Multi-output Gaussian Process Dynamical Systems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Volumetric Spanners: An Efficient Exploration Basis for Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| bandicoot: a Python Toolbox for Mobile Phone Metadata |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| fastFM: A Library for Factorization Machines |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| mlr: Machine Learning in R |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |