Expected Policy Gradients for Reinforcement Learning.
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.
Learning Causal Networks via Additive Faithfulness.
Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach.
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction.
apricot: Submodular selection for data summarization in Python.
Cornac: A Comparative Framework for Multimodal Recommender Systems.
Monte Carlo Gradient Estimation in Machine Learning.
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms.
Importance Sampling Techniques for Policy Optimization.
Sparse and low-rank multivariate Hawkes processes.
Local Causal Network Learning for Finding Pairs of Total and Direct Effects.
Topology of Deep Neural Networks.
A Unified Framework for Structured Graph Learning via Spectral Constraints.
Streamlined Variational Inference with Higher Level Random Effects.
A Unified q-Memorization Framework for Asynchronous Stochastic Optimization.
Memoryless Sequences for General Losses.
On lp-Support Vector Machines and Multidimensional Kernels.
Ensemble Learning for Relational Data.
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality.
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data.
Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space.
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality.
Spectral Algorithms for Community Detection in Directed Networks.
A Regularization-Based Adaptive Test for High-Dimensional GLMs.
New Insights and Perspectives on the Natural Gradient Method.
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions.
Lower Bounds for Parallel and Randomized Convex Optimization.
Optimal Bipartite Network Clustering.
Bayesian Model Selection with Graph Structured Sparsity.
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification.
The weight function in the subtree kernel is decisive.
Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes.
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms.
Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models.
Distributed Feature Screening via Componentwise Debiasing.
Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods.
A determinantal point process for column subset selection.
Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination.
Change Point Estimation in a Dynamic Stochastic Block Model.
Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance.
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning.
Stochastic Nested Variance Reduction for Nonconvex Optimization.
Provable Convex Co-clustering of Tensors.
Practical Locally Private Heavy Hitters.
Causal Discovery from Heterogeneous/Nonstationary Data.
GluonTS: Probabilistic and Neural Time Series Modeling in Python.
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.
Probabilistic Symmetries and Invariant Neural Networks.
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning.
Representation Learning for Dynamic Graphs: A Survey.
Tslearn, A Machine Learning Toolkit for Time Series Data.
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks.
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success.
NEVAE: A Deep Generative Model for Molecular Graphs.
Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions.
Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data.
Target Propagation in Recurrent Neural Networks.
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms.
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond.
Convergence of Sparse Variational Inference in Gaussian Processes Regression.
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning.
GraKeL: A Graph Kernel Library in Python.
Mining Topological Structure in Graphs through Forest Representations.
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems.
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics.
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization.
Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms.
Quantile Graphical Models: a Bayesian Approach.
pyts: A Python Package for Time Series Classification.
Trust-Region Variational Inference with Gaussian Mixture Models.
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning.
Distributed Minimum Error Entropy Algorithms.
High-dimensional Gaussian graphical models on network-linked data.
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise.
Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions.
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models.
Multiclass Anomaly Detector: the CS++ Support Vector Machine.
Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory.
The Kalai-Smorodinsky solution for many-objective Bayesian optimization.
ThunderGBM: Fast GBDTs and Random Forests on GPUs.
Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients.
Community-Based Group Graphical Lasso.
Learning and Interpreting Multi-Multi-Instance Learning Networks.
Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes.
Union of Low-Rank Tensor Spaces: Clustering and Completion.
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes.
metric-learn: Metric Learning Algorithms in Python.
Universal Latent Space Model Fitting for Large Networks with Edge Covariates.
Multiparameter Persistence Landscapes.
Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models.
A Numerical Measure of the Instability of Mapper-Type Algorithms.
Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group.
A Statistical Learning Approach to Modal Regression.
Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs.
Tensor Regression Networks.
Agnostic Estimation for Phase Retrieval.
(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets.
Robust Reinforcement Learning with Bayesian Optimisation and Quadrature.
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization.
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms.
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting.
Distributed Kernel Ridge Regression with Communications.
MFE: Towards reproducible meta-feature extraction.
Estimation of a Low-rank Topic-Based Model for Information Cascades.
High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix.
Cramer-Wold Auto-Encoder.
AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings).
High-Dimensional Inference for Cluster-Based Graphical Models.
Optimal Estimation of Sparse Topic Models.
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels.
High Dimensional Forecasting via Interpretable Vector Autoregression.
General Latent Feature Models for Heterogeneous Datasets.
Self-paced Multi-view Co-training.
Sequential change-point detection in high-dimensional Gaussian graphical models.
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks.
pyDML: A Python Library for Distance Metric Learning.
Scalable Approximate MCMC Algorithms for the Horseshoe Prior.
Dynamical Systems as Temporal Feature Spaces.
Conjugate Gradients for Kernel Machines.
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement.
Semi-parametric Learning of Structured Temporal Point Processes.
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers.
Prediction regions through Inverse Regression.
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.
Asymptotic Consistency of α-Rényi-Approximate Posteriors.
A New Class of Time Dependent Latent Factor Models with Applications.
A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints.
Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis.
Identifiability of Additive Noise Models Using Conditional Variances.
High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model.
Effective Ways to Build and Evaluate Individual Survival Distributions.
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering.
ProtoAttend: Attention-Based Prototypical Learning.
A General System of Differential Equations to Model First-Order Adaptive Algorithms.
Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion.
Joint Causal Inference from Multiple Contexts.
Branch and Bound for Piecewise Linear Neural Network Verification.
Learning Data-adaptive Non-parametric Kernels.
Contextual Explanation Networks.
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables.
Breaking the Curse of Nonregularity with Subagging - Inference of the Mean Outcome under Optimal Treatment Regimes.
Generalized probabilistic principal component analysis of correlated data.
Distributionally Ambiguous Optimization for Batch Bayesian Optimization.
Distributed High-dimensional Regression Under a Quantile Loss Function.
Nesterov's Acceleration for Approximate Newton.
Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning.
Sparse Projection Oblique Randomer Forests.
Connecting Spectral Clustering to Maximum Margins and Level Sets.
DESlib: A Dynamic ensemble selection library in Python.
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn.
Near-optimal Individualized Treatment Recommendations.
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients.
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy.
Generalized Optimal Matching Methods for Causal Inference.
Multi-Player Bandits: The Adversarial Case.
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes.
On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent.
Adaptive Smoothing for Path Integral Control.
Empirical Priors for Prediction in Sparse High-dimensional Linear Regression.
On Mahalanobis Distance in Functional Settings.
Learning with Fenchel-Young losses.
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data.
Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators.
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions.
Orlicz Random Fourier Features.
Neyman-Pearson classification: parametrics and sample size requirement.
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization.
Scikit-network: Graph Analysis in Python.
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information.
A Model of Fake Data in Data-driven Analysis.
Noise Accumulation in High Dimensional Classification and Total Signal Index.
Dynamic Assortment Optimization with Changing Contextual Information.
The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response.
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching.
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization.
Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization.
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly.
Conic Optimization for Quadratic Regression Under Sparse Noise.
Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis.
Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure.
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping.
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms.
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections.
A Convex Parametrization of a New Class of Universal Kernel Functions.
Generalized Nonbacktracking Bounds on the Influence.
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings.
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective.
Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study.
Causal Discovery Toolbox: Uncovering causal relationships in Python.
Tensor Train Decomposition on TensorFlow (T3F).
Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models.
Probabilistic Learning on Graphs via Contextual Architectures.
Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data.
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.
Quadratic Decomposable Submodular Function Minimization: Theory and Practice.
Bayesian Closed Surface Fitting Through Tensor Products.
Variational Inference for Computational Imaging Inverse Problems.
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise.
Dual Iterative Hard Thresholding.
Online Sufficient Dimension Reduction Through Sliced Inverse Regression.
Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging.
Apache Mahout: Machine Learning on Distributed Dataflow Systems.
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent.
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation.
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information.
Kymatio: Scattering Transforms in Python.
Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size.
Beyond Trees: Classification with Sparse Pairwise Dependencies.
Minimax Nonparametric Parallelism Test.
