yusuke1997/dblp-id-title huggingface dataset:
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks
Dual Iterative Hard Thresholding
Online submodular minimization
A Convex Parametrization of a New Class of Universal Kernel Functions
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Cluster-Adaptive Network A/B Testing: From Randomization to Estimation
Multimodal learning with deep Boltzmann machines
A Coupled Indian Buffet Process Model for Collaborative Filtering
Streaming Principal Component Analysis From Incomplete Data
Tree-AMP: Compositional Inference with Tree Approximate Message Passing
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions
Pattern for Python
Deep Reinforcement Learning for Swarm Systems
Rationality, optimism and guarantees in general reinforcement learning
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization
A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
Handling Hard Affine SDP Shape Constraints in RKHSs
POT: Python Optimal Transport
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?
Point-Based Value Iteration for Continuous POMDPs
Monotonic Alpha-divergence Minimisation for Variational Inference
Classifier cascades and trees for minimizing feature evaluation cost
Boosting Multi-agent Reinforcement Learning via Contextual Prompting
On Sufficient Graphical Models
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
Generalized Conditional Gradient for Sparse Estimation
GIBBON: General-purpose Information-Based Bayesian Optimisation
Fast MCMC Sampling Algorithms on Polytopes
On the Complexity of Learning Lexicographic Strategies
Signature Moments to Characterize Laws of Stochastic Processes
When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks?
Adaptive Randomized Dimension Reduction on Massive Data
Generalization Properties of hyper-RKHS and its Applications
Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
Learning Interpretations Using Sequence Classification
A binary-classification-based metric between time-series distributions and its use in statistical and learning problems
Human gesture recognition on product manifolds
A Survey on Multi-player Bandits
Learning from Weak Teachers
Conjugate relation between loss functions and uncertainty sets in classification problems
The Optimal Sample Complexity of PAC Learning
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes
MOA Concept Drift Active Learning Strategies for Streaming Data
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
Random spanning trees and the prediction ofweighted graphs
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
Wasserstein barycenters can be computed in polynomial time in fixed dimension
Gauss-Legendre Features for Gaussian Process Regression
A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks
Exploration of the Search Space of Gaussian Graphical Models for Paired Data
Online Learning in the Embedded Manifold of Low-rank Matrices
Second Order Cone Programming Formulations for Feature Selection
Round Robin Classification
Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination
Variational Fourier Features for Gaussian Processes
A General Framework for Consistency of Principal Component Analysis
Asymptotic Consistency of α-Rényi-Approximate Posteriors
Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso
Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
Quadratic Programming Feature Selection
Kernel Estimation and Model Combination in A Bandit Problem with Covariates
New Techniques for Disambiguation in Natural Language and Their Application to Biological Text
Use of the Zero-Norm with Linear Models and Kernel Methods
Statistical Optimization of Non-Negative Matrix Factorization
On the Learnability of Out-of-distribution Detection
Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model
Order-independent constraint-based causal structure learning
Matrix completion with the trace norm: learning, bounding, and transducing
Benchmarking Unsupervised Object Representations for Video Sequences
Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces
The correlation-assisted missing data estimator
Density estimation on low-dimensional manifolds: an inflation-deflation approach
Movement Segmentation and Recognition for Imitation Learning
Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data
Plug-in Approach to Active Learning
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
A Bregman Learning Framework for Sparse Neural Networks
Least Squares Model Averaging for Distributed Data
Learning Recursive Control Programs from Problem Solving
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression
Tensor Train Decomposition on TensorFlow (T3F)
Zeroth-Order Alternating Gradient Descent Ascent Algorithms for A Class of Nonconvex-Nonconcave Minimax Problems
Nearest Neighbor Dirichlet Mixtures
On the Convergence Properties of Contrastive Divergence
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem
On Combining Graph-based Variance Reduction schemes
New Insights for the Multivariate Square-Root Lasso
Information Theoretical Clustering via Semidefinite Programming
Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing
Universal Approximation Property of Invertible Neural Networks
Stochastic DCA with Variance Reduction and Applications in Machine Learning
Generic Methods for Optimization-Based Modeling
Large Scale Multiple Kernel Learning
Regularization-free principal curve estimation
Efficient methods for robust classification under uncertainty in kernel matrices
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Models of Cooperative Teaching and Learning
Multi-armed Bandit Problems with History
Fast and Robust Rank Aggregation against Model Misspecification
StructED: Risk Minimization in Structured Prediction
Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space
Oracle inequalities for computationally budgeted model selection
Target Propagation in Recurrent Neural Networks
Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
On Biased Stochastic Gradient Estimation
Feature Extraction by Non-Parametric Mutual Information Maximization
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
Learning using privileged information: similarity control and knowledge transfer
On the Consistency of Ordinal Regression Methods
Information criteria for non-normalized models
One-shot-learning gesture recognition using HOG-HOF features
Stability and Generalization
Sampling Permutations for Shapley Value Estimation
Fortuna: A Library for Uncertainty Quantification in Deep Learning
Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage
Minimum Volume Embedding
Universal Approximation Theorems for Differentiable Geometric Deep Learning
Addressing Environment Non-Stationarity by Repeating Q-learning Updates
Bayesian Network Learning with Parameter Constraints
Herded Gibbs Sampling
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence
On Bayes Risk Lower Bounds
Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
Robustness and Regularization of Support Vector Machines
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
Causal Bandits for Linear Structural Equation Models
Learning mixtures of Gaussians with maximum-a-posteriori oracle
Fully General Online Imitation Learning
mlr3pipelines - Flexible Machine Learning Pipelines in R
Clustering from General Pairwise Observations with Applications to Time-varying Graphs
Low-Rank Matrix Estimation in the Presence of Change-Points
Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data
Structured prediction via output space search
A Unified Sample Selection Framework for Output Noise Filtering: An Error-Bound Perspective
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations
Learnability of Solutions to Conjunctive Queries
Sequential change-point detection in high-dimensional Gaussian graphical models
Log-concave sampling: Metropolis-Hastings algorithms are fast
Bootstrap-Based Regularization for Low-Rank Matrix Estimation
Multi-task Sparse Structure Learning with Gaussian Copula Models
Local Propagation in Conditional Gaussian Bayesian Networks
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
Learning to Detect and Classify Malicious Executables in the Wild
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models
Scikit-network: Graph Analysis in Python
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models
Autonomous Exploration For Navigating In MDPs
Finite-Time Bounds for Fitted Value Iteration
Attacks against Federated Learning Defense Systems and their Mitigation
A Statistical Approach for Optimal Topic Model Identification
A Linear Non-Gaussian Acyclic Model for Causal Discovery
On Biased Compression for Distributed Learning
MAGIC summoning: towards automatic suggesting and testing of gestures with low probability of false positives during use
Sequential Learning of Classifiers for Structured Prediction Problems
A Framework for Probability Density Estimation
A Bayesian View of Challenges in Feature Selection: Feature Aggregation, Multiple Targets, Redundancy and Interaction
The Brier Score under Administrative Censoring: Problems and a Solution
Statistical Robustness of Empirical Risks in Machine Learning
Web-Search Ranking with Initialized Gradient Boosted Regression Trees
Differential privacy for functions and functional data
Contextual Explanation Networks
Computational Limits of A Distributed Algorithm for Smoothing Spline
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Stochastic dual coordinate ascent methods for regularized loss
Bayesian entropy estimation for countable discrete distributions
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Online Optimization with Gradual Variations
A Robust Minimax Approach to Classification
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks
Robust Interactive Learning
Representation Learning via Manifold Flattening and Reconstruction
Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
Unlabeled Principal Component Analysis and Matrix Completion
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
Bayesian Hierarchical Cross-Clustering
Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
The Need for Open Source Software in Machine Learning
Pairwise support vector machines and their application to large scale problems
Benign overfitting in ridge regression
Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
Transductive Classification via Local Learning Regularization
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data
On Ranking and Generalization Bounds
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity
Sparse Graphical Linear Dynamical Systems
Multi-label Classification with Error-correcting Codes
Nonparametric Tree Graphical Models
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach
On the Generalization of Stochastic Gradient Descent with Momentum
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
A Distribution Free Conditional Independence Test with Applications to Causal Discovery
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
Causal Discovery with Unobserved Confounding and Non-Gaussian Data
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Quasi-Monte Carlo Quasi-Newton in Variational Bayes
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size
Functional Linear Regression with Mixed Predictors
Characterizing the Sample Complexity of Pure Private Learners
Learning Markov Structure by Maximum Entropy Relaxation
Learning Rules and Their Exceptions
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables
Importance Sparsification for Sinkhorn Algorithm
Parameter Screening and Optimisation for ILP using Designed Experiments
Large Margin Methods for Structured and Interdependent Output Variables
Scikit-Multiflow: A Multi-output Streaming Framework
Competitive Closeness Testing
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
Topological Hidden Markov Models
Optimal Strategies for Reject Option Classifiers
Learning to Rank Using an Ensemble of Lambda-Gradient Models
Statistical analysis of metric graph reconstruction
Greedy feature selection for subspace clustering
Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program)
Considering Cost Asymmetry in Learning Classifiers
Gaussian Approximation for Bias Reduction in Q-Learning
Learning Substitutable Binary Plane Graph Grammars
Nieme: Large-Scale Energy-Based Models
An Introduction to Variable and Feature Selection
Joint PLDA for Simultaneous Modeling of Two Factors
Testing for Membership to the IFRA and the NBU Classes of Distributions
PAC-bayes bounds with data dependent priors
Learning Class-relevant Features and Class-irrelevant Features via a Hybrid third-order RBM
Testing Conditional Independence via Quantile Regression Based Partial Copulas
Connecting Spectral Clustering to Maximum Margins and Level Sets
Tensor-train methods for sequential state and parameter learning in state-space models
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
Importance Sampling for General Hybrid Bayesian Networks
Strategic Knowledge Transfer
Optimal aggregation of affine estimators
Split Conformal Prediction and Non-Exchangeable Data
Event based text mining for integrated network construction
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)
A Characterization of Linkage-Based Hierarchical Clustering
Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation
MDPs: Learning in Varying Environments
Ensemble Pruning Via Semi-definite Programming
Preference Elicitation via Theory Refinement
Axioms for graph clustering quality functions
Parallel MCMC with generalized elliptical slice sampling
Generalized Sparse Additive Models
Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods
A Bayesian Approximation Method for Online Ranking
Some Dichotomy Theorems for Neural Learning Problems
Learning Multi-modal Similarity
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
Efficient SVM Training Using Low-Rank Kernel Representations
Divergences and Risks for Multiclass Experiments
Fast generalized subset scan for anomalous pattern detection
Classifying with confidence from incomplete information
Learning From Crowds
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent
Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Deep Sparse Rectifier Neural Networks
On the Rate of Convergence of the Bagged Nearest Neighbor Estimate
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions
Kernel Mean Shrinkage Estimators
LinCDE: Conditional Density Estimation via Lindsey's Method
Preference-based Online Learning with Dueling Bandits: A Survey
The CAM software for nonnegative blind source separation in R-Java
A Least-squares Approach to Direct Importance Estimation
Similarity-based Classification: Concepts and Algorithms
An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
How to Center Deep Boltzmann Machines
Variable Metric Stochastic Approximation Theory
Metrics of Calibration for Probabilistic Predictions
Composite Binary Losses
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
Some Properties of Regularized Kernel Methods
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Particle Belief Propagation
Jointly Informative Feature Selection Made Tractable by Gaussian Modeling
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
PAC-Bayesian Analysis of Co-clustering and Beyond
Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction
Collective Matrix Completion
DEAP: evolutionary algorithms made easy
Covariate Shift Adaptation by Importance Weighted Cross Validation
Oblivious Data for Fairness with Kernels
Stabilized Sparse Online Learning for Sparse Data
Pinview: Implicit Feedback in Content-Based Image Retrieval
(weak) Calibration is Computationally Hard
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
RLScore: Regularized Least-Squares Learners
Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results
Risk Bounds for Levy Processes in the PAC-Learning Framework
Prediction Under Latent Factor Regression: Adaptive PCR, Interpolating Predictors and Beyond
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data
Convergence rates for persistence diagram estimation in topological data analysis
A Geometric Approach to Multi-Criterion Reinforcement Learning
Graph Attention Retrospective
Bipartite Ranking: a Risk-Theoretic Perspective
Rate Minimaxity of the Lasso and Dantzig Selector for the l q Loss in l r Balls
LIBLINEAR: A Library for Large Linear Classification
Probabilistic Symmetries and Invariant Neural Networks
Subgroup Analysis via Recursive Partitioning
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Active Learning from Multiple Knowledge Sources
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation
Significance-based community detection in weighted networks
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem
Extrapolating Expected Accuracies for Large Multi-Class Problems
Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets
Generative Adversarial Ranking Nets
Learning RoboCup-Keepaway with Kernels
Kernel Learning by Unconstrained Optimization
Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models
Feature Selection for Text Classification Based on Gini Coefficient of Inequality
Generalization Bounds for Ranking Algorithms via Algorithmic Stability
Regret Bounds for the Adaptive Control of Linear Quadratic Systems
On Solving Probabilistic Linear Diophantine Equations
A junction tree framework for undirected graphical model selection
A general framework for fast stagewise algorithms
A PDE approach for regret bounds under partial monitoring
Multitask Learning for Brain-Computer Interfaces
Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling
Active Learning with Clustering
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation
On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates
Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion
Bayesian nonparametric comorbidity analysis of psychiatric disorders
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
Fairness Constraints: A Flexible Approach for Fair Classification
A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
Contextual bandits with similarity information
The flare package for high dimensional linear regression and precision matrix estimation in R
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
Incremental Sparsification for Real-time Online Model Learning
A framework for evaluating approximation methods for Gaussian process regression
Distance Patterns in Structural Similarity
Sign language recognition using sub-units
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
tntorch: Tensor Network Learning with PyTorch
Mini-batching error and adaptive Langevin dynamics
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
Langevin Monte Carlo: random coordinate descent and variance reduction
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Computationally Efficient Convolved Multiple Output Gaussian Processes
A Unified Approach to Controlling Implicit Regularization via Mirror Descent
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization
Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters
CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels
Parallelizing Spectrally Regularized Kernel Algorithms
A reliable effective terascale linear learning system
Interpolating Classifiers Make Few Mistakes
Large sample spectral analysis of graph-based multi-manifold clustering