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

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