GIBBON: General-purpose Information-Based Bayesian Optimisation

2021-10-8

Differentially private regression and classification with sparse Gaussian processes

2021-10-8

sklvq : Scikit Learning Vector Quantization

2021-10-1

A Unified Convergence Analysis for Shuffling-Type Gradient Methods

2021-9-21

The ensmallen library for flexible numerical optimization

2021-8-30

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

2021-7-1

Convolutional Neural Networks are not invariant to translation, but they can learn to be

2021-5-4

Contrastive estimation reveals topic posterior information to linear models

2021-5-4

AN ONLINE SEQUENTIAL TEST FOR QUALITATIVE TREATMENT EFFECTS

2021-5-4

Benchmarking Unsupervised Object Representations for Video Sequences

2021-5-4

Edge Sampling Using Local Network Information

2021-5-1

Consistent estimation of small masses in feature sampling

2021-1-31

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions

2021-1-1

Interpretable Deep Generative Recommendation Models

2021-1-1

Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model

2021-1-1

Improved Shrinkage Prediction under a Spiked Covariance Structure

2021-1-1

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

2021-1-1

Sparse Popularity Adjusted Stochastic Block Model

2021-1-1

Is SGD a Bayesian sampler? Well, almost

2021-1-1

Bifurcation Spiking Neural Network

2021-1-1

Finite Time LTI System Identification.

2021-1-1

Integrated Principal Components Analysis

2021-1-1

Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples

2021-1-1

Predictive Learning on Hidden Tree-Structured Ising Models

2021-1-1

Bayesian Text Classification and Summarization via A Class-Specified Topic Model

2021-1-1

The decoupled extended Kalman filter for dynamic exponential-family factorization models

2021-1-1

Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity

2021-1-1

Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks

2021-1-1

On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests

2021-1-1

On Solving Probabilistic Linear Diophantine Equations

2021-1-1

A Lyapunov Analysis of Accelerated Methods in Optimization

2021-1-1

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

2021-1-1

A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables

2021-1-1

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning

2021-1-1

Gaussian Approximation for Bias Reduction in Q-Learning

2021-1-1

Testing Conditional Independence via Quantile Regression Based Partial Copulas

2021-1-1

Learning partial correlation graphs and graphical models by covariance queries

2021-1-1

Failures of Model-dependent Generalization Bounds for Least-norm Interpolation

2021-1-1

Bayesian time-aligned factor analysis of paired multivariate time series

2021-1-1

Empirical Bayes Matrix Factorization

2021-1-1

River: Machine learning for streaming data in Python

2021-1-1

Multi-class Gaussian Process Classification with Noisy Inputs

2021-1-1

Quasi-Monte Carlo Quasi-Newton in Variational Bayes

2021-1-1

Aggregated Hold-Out

2021-1-1

Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning

2021-1-1

Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction

2021-1-1

Approximate Newton Methods

2021-1-1

A General Framework for Adversarial Label Learning

2021-1-1

Locally Private k-Means Clustering

2021-1-1

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit

2021-1-1