| (1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Convex Parametrization of a New Class of Universal Kernel Functions |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A General System of Differential Equations to Model First-Order Adaptive Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Group-Theoretic Framework for Data Augmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Model of Fake Data in Data-driven Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A New Class of Time Dependent Latent Factor Models with Applications |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Numerical Measure of the Instability of Mapper-Type Algorithms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Regularization-Based Adaptive Test for High-Dimensional GLMs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Statistical Learning Approach to Modal Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Unified Framework for Structured Graph Learning via Spectral Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Unified q-Memorization Framework for Asynchronous Stochastic Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A determinantal point process for column subset selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings) |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| AdaGrad stepsizes: Sharp convergence over nonconvex landscapes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Rates for Total Variation Image Denoising |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Smoothing for Path Integral Control |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Agnostic Estimation for Phase Retrieval |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Apache Mahout: Machine Learning on Distributed Dataflow Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
3 |
| Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Asymptotic Consistency of $\alpha$-{R}\'enyi-Approximate Posteriors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bayesian Closed Surface Fitting Through Tensor Products |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bayesian Model Selection with Graph Structured Sparsity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Best Practices for Scientific Research on Neural Architecture Search |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Beyond Trees: Classification with Sparse Pairwise Dependencies |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Branch and Bound for Piecewise Linear Neural Network Verification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Causal Discovery Toolbox: Uncovering causal relationships in Python |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Causal Discovery from Heterogeneous/Nonstationary Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Change Point Estimation in a Dynamic Stochastic Block Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Community-Based Group Graphical Lasso |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conic Optimization for Quadratic Regression Under Sparse Noise |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Conjugate Gradients for Kernel Machines |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Connecting Spectral Clustering to Maximum Margins and Level Sets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Explanation Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence of Sparse Variational Inference in Gaussian Processes Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convex Programming for Estimation in Nonlinear Recurrent Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Cornac: A Comparative Framework for Multimodal Recommender Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Cramer-Wold Auto-Encoder |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| DESlib: A Dynamic ensemble selection library in Python |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Diffeomorphic Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributed Feature Screening via Componentwise Debiasing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distributed High-dimensional Regression Under a Quantile Loss Function |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributed Kernel Ridge Regression with Communications |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Distributed Minimum Error Entropy Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributionally Ambiguous Optimization for Batch Bayesian Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dual Extrapolation for Sparse GLMs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dual Iterative Hard Thresholding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamic Assortment Optimization with Changing Contextual Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Dynamical Systems as Temporal Feature Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Effective Ways to Build and Evaluate Individual Survival Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Empirical Priors for Prediction in Sparse High-dimensional Linear Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ensemble Learning for Relational Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimation of a Low-rank Topic-Based Model for Information Cascades |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Expected Policy Gradients for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Fair Data Adaptation with Quantile Preservation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| General Latent Feature Models for Heterogeneous Datasets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generalized Nonbacktracking Bounds on the Influence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized Optimal Matching Methods for Causal Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized probabilistic principal component analysis of correlated data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geomstats: A Python Package for Riemannian Geometry in Machine Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GraKeL: A Graph Kernel Library in Python |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| High Dimensional Forecasting via Interpretable Vector Autoregression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| High-Dimensional Inference for Cluster-Based Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| High-dimensional Gaussian graphical models on network-linked data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| High-dimensional quantile tensor regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Identifiability of Additive Noise Models Using Conditional Variances |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Importance Sampling Techniques for Policy Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Joint Causal Inference from Multiple Contexts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kymatio: Scattering Transforms in Python |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Causal Networks via Additive Faithfulness |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Data-adaptive Non-parametric Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Mixed Latent Tree Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Sums of Independent Random Variables with Sparse Collective Support |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning and Interpreting Multi-Multi-Instance Learning Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Learning with Fenchel-Young losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Local Causal Network Learning for Finding Pairs of Total and Direct Effects |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Lower Bounds for Parallel and Randomized Convex Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MFE: Towards reproducible meta-feature extraction |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Memoryless Sequences for General Losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimax Nonparametric Parallelism Test |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mining Topological Structure in Graphs through Forest Representations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Monte Carlo Gradient Estimation in Machine Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Player Bandits: The Adversarial Case |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiclass Anomaly Detector: the CS++ Support Vector Machine |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Multiparameter Persistence Landscapes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| NEVAE: A Deep Generative Model for Molecular Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Near-optimal Individualized Treatment Recommendations |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nesterov's Acceleration for Approximate Newton |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| New Insights and Perspectives on the Natural Gradient Method |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Neyman-Pearson classification: parametrics and sample size requirement |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Noise Accumulation in High Dimensional Classification and Total Signal Index |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Nonparametric graphical model for counts |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On Efficient Adjustment in Causal Graphs |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| On Mahalanobis Distance in Functional Settings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On lp-Support Vector Machines and Multidimensional Kernels |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Sufficient Dimension Reduction Through Sliced Inverse Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Online matrix factorization for Markovian data and applications to Network Dictionary Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Bipartite Network Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Estimation of Sparse Topic Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Orlicz Random Fourier Features |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Practical Locally Private Heavy Hitters |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Prediction regions through Inverse Regression |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Probabilistic Learning on Graphs via Contextual Architectures |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Probabilistic Symmetries and Invariant Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ProtoAttend: Attention-Based Prototypical Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provable Convex Co-clustering of Tensors |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Quadratic Decomposable Submodular Function Minimization: Theory and Practice |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quantile Graphical Models: a Bayesian Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rank-based Lasso - efficient methods for high-dimensional robust model selection |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Representation Learning for Dynamic Graphs: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Risk Bounds for Reservoir Computing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Reinforcement Learning with Bayesian Optimisation and Quadrature |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust high dimensional learning for Lipschitz and convex losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable Approximate MCMC Algorithms for the Horseshoe Prior |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scikit-network: Graph Analysis in Python |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-paced Multi-view Co-training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-parametric Learning of Structured Temporal Point Processes |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Sequential change-point detection in high-dimensional Gaussian graphical models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Significance Tests for Neural Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sparse Projection Oblique Randomer Forests |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse and low-rank multivariate Hawkes processes |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Algorithms for Community Detection in Directed Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Deconfounding via Perturbed Sparse Linear Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral bandits |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stable Regression: On the Power of Optimization over Randomization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Nested Variance Reduction for Nonconvex Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Streamlined Variational Inference with Higher Level Random Effects |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Target Propagation in Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tensor Regression Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Tensor Train Decomposition on TensorFlow (T3F) |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| The Error-Feedback framework: SGD with Delayed Gradients |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Kalai-Smorodinsky solution for many-objective Bayesian optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The weight function in the subtree kernel is decisive |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Theory of Curriculum Learning, with Convex Loss Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ThunderGBM: Fast GBDTs and Random Forests on GPUs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Topology of Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Trust-Region Variational Inference with Gaussian Mixture Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tslearn, A Machine Learning Toolkit for Time Series Data |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ultra-High Dimensional Single-Index Quantile Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Union of Low-Rank Tensor Spaces: Clustering and Completion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Universal Latent Space Model Fitting for Large Networks with Edge Covariates |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Inference for Computational Imaging Inverse Problems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Wide Neural Networks with Bottlenecks are Deep Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| apricot: Submodular selection for data summarization in Python |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| metric-learn: Metric Learning Algorithms in Python |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| pyDML: A Python Library for Distance Metric Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| pyts: A Python Package for Time Series Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |