yusuke1997/dblp-id-title huggingface dataset:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
Risk Measures and Upper Probabilities: Coherence and Stratification
On NDCG Consistency of Listwise Ranking Methods
Predictive Inference with Weak Supervision
Efficient Modality Selection in Multimodal Learning
Machine Learning for Computer Security
Lower Bounds and Aggregation in Density Estimation
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data
A kernel method for unsupervised structured network inference
Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions
Space-Efficient Sampling
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Prioritization Methods for Accelerating MDP Solvers
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Variable Sparsity Kernel Learning
A metric learning perspective of SVM: on the relation of LMNN and SVM
Nonnegative Garrote Component Selection in Functional ANOVA models
Stochastic Nested Variance Reduction for Nonconvex Optimization
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling
Support Vector Machine Active Learning with Applications to Text Classification
Fair Data Representation for Machine Learning at the Pareto Frontier
A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering
A Forward Approach for Sufficient Dimension Reduction in Binary Classification
Minimal Kernel Classifiers
Random Subgraph Detection Using Queries
Apache Mahout: Machine Learning on Distributed Dataflow Systems
Continuous-in-time Limit for Bayesian Bandits
Kernel Partial Least Squares for Stationary Data
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA
Deterministic Bayesian inference for the p* model
Tree-Based Inference for Dirichlet Process Mixtures
How to Explain Individual Classification Decisions
Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation
Yahoo! Learning to Rank Challenge Overview
Robust near-separable nonnegative matrix factorization using linear optimization
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
Consistency of Nearest Neighbor Classification under Selective Sampling
Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis
DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models
Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
Open Problem: Learning Dynamic Network Models from a Static Snapshot
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms
Distributionally Ambiguous Optimization for Batch Bayesian Optimization
On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods
Active Structure Learning of Bayesian Networks in an Observational Setting
Limitations of Learning Via Embeddings in Euclidean Half Spaces
Reinforcement Learning with Factored States and Actions
Multi-objective reinforcement learning using sets of pareto dominating policies
Ellipsoidal Support Vector Machines
Novelty detection: Unlabeled data definitely help
Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Sparse Additive Matrix Factorization for Robust PCA and Its Generalization
Random measure priors in Bayesian recovery from sketches
Two-mode Networks: Inference with as Many Parameters as Actors and Differential Privacy
Learning Theory for Distribution Regression
The student-t mixture as a natural image patch prior with application to image compression
Segregating event streams and noise with a Markov renewal process model
Challenges in multimodal gesture recognition
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
Lenient Learning in Independent-Learner Stochastic Cooperative Games
Bayesian Graphical Models for Multivariate Functional Data
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Metric and Kernel Learning Using a Linear Transformation
Distributed Estimation on Semi-Supervised Generalized Linear Model
Robust Gaussian Process Regression with a Student- t Likelihood
Local identification of overcomplete dictionaries
Descent Methods for Tuning Parameter Refinement
Deep Learning Made Easier by Linear Transformations in Perceptrons
Link prediction in graphs with autoregressive features
Projection-free Distributed Online Learning with Sublinear Communication Complexity
Distributed High-dimensional Regression Under a Quantile Loss Function
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method
Feature Extraction for Outlier Detection in High-Dimensional Spaces
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling
Detection of Server-side Web Attacks
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
Regularization via Mass Transportation
Bayesian Data Selection
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds
Large-Margin Classification in Banach Spaces
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping
MISEP -- Linear and Nonlinear ICA Based on Mutual Information
Finite-sample Analysis of Bellman Residual Minimization
Large-scale SVD and manifold learning
Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation
The ensmallen library for flexible numerical optimization
Global analytic solution of fully-observed variational Bayesian matrix factorization
The adversarial stochastic shortest path problem with unknown transition probabilities
From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data
Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs
Learning Parts-Based Representations of Data
A Nonparametric Statistical Approach to Clustering via Mode Identification
Krylov Subspace Descent for Deep Learning
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Large Margin Semi-supervised Learning
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?
Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty
Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality
Posterior Consistency for Bayesian Relevance Vector Machines
Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Multi-Task Learning using Generalized t Process
UPAL: Unbiased Pool Based Active Learning
Efficient graphlet kernels for large graph comparison
Machine learning with operational costs
HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
SVMTorch: Support Vector Machines for Large-Scale Regression Problems
Dynamic policy programming
Calibrated Multiple-Output Quantile Regression with Representation Learning
Limitations on approximation by deep and shallow neural networks
abess: A Fast Best-Subset Selection Library in Python and R
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence
Acceleration technique for boosting classification and its application to face detection
Bayesian subset selection and variable importance for interpretable prediction and classification
Sufficient covariates and linear propensity analysis
Analysis of the KDD Cup 2009: Fast Scoring on a Large Orange Customer Database
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
Sparse activity and sparse connectivity in supervised learning
Faith-Shap: The Faithful Shapley Interaction Index
SMART: An Open Source Data Labeling Platform for Supervised Learning
Universal Measurement Bounds for Structured Sparse Signal Recovery
An Improved 1-norm SVM for Simultaneous Classification and Variable Selection
Policy-Gradients for PSRs and POMDPs
Mixing Time of Metropolis-Hastings for Bayesian Community Detection
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces
Poisson Random Fields for Dynamic Feature Models
Ranking the Best Instances
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Compression-Based Averaging of Selective Naive Bayes Classifiers
Learning Low-order Models for Enforcing High-order Statistics
Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning
High-Dimensional Inference for Cluster-Based Graphical Models
Linear dimensionality reduction: survey, insights, and generalizations
Kronecker Graphs: An Approach to Modeling Networks
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling
Consistency of Cheeger and Ratio Graph Cuts
Kernel-Based Learning of Hierarchical Multilabel Classification Models
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
A universally consistent learning rule with a universally monotone error
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Toolbox for Multimodal Learn (scikit-multimodallearn)
Bagging Provides Assumption-free Stability
Active Boosted Learning (ActBoost)
EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation
Stochastic Optimization under Distributional Drift
Analysis of Variance of Cross-Validation Estimators of the Generalization Error
Stability Properties of Empirical Risk Minimization over Donsker Classes
Stumping along a Summary for Exploration & Exploitation Challenge 2011
Fast String Kernels using Inexact Matching for Protein Sequences
Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
Learning Valuation Functions
Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Incorporating Unlabeled Data into Distributionally Robust Learning
Query Strategies for Evading Convex-Inducing Classifiers
Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology
Axiomatic effect propagation in structural causal models
GFlowNet Foundations
Incremental Learning in Diagonal Linear Networks
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes
Fairness guarantees in multi-class classification with demographic parity
An Error Bound Based on a Worst Likely Assignment
Active Sensing
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means
Group Orthogonal Matching Pursuit for Logistic Regression
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
Double Generative Adversarial Networks for Conditional Independence Testing
A Network That Learns Strassen Multiplication
Nonlinear low-dimensional regression using auxiliary coordinates
A Random Matrix Perspective on Random Tensors
Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems
LPmade: Link Prediction Made Easy
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits
On the Foundations of Noise-free Selective Classification
Performance Guarantees for Information Theoretic Active Inference
A Nonconvex Framework for Structured Dynamic Covariance Recovery
Self-measuring Similarity for Multi-task Gaussian Process
Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation
Future directions in learning to rank
The Best of Both Worlds: Stochastic and Adversarial Bandits
Robust Frequent Directions with Application in Online Learning
Approximation Hardness for A Class of Sparse Optimization Problems
Globally Optimizing Graph Partitioning Problems Using Message Passing
Generalized Nonbacktracking Bounds on the Influence
Tianshou: A Highly Modularized Deep Reinforcement Learning Library
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
An Online Sequential Test for Qualitative Treatment Effects
Cross-Domain Object Matching with Model Selection
A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
Causal Discovery Toolbox: Uncovering causal relationships in Python
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory
Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces
Near-Optimal Weighted Matrix Completion
Learnability of Linear Port-Hamiltonian Systems
Sufficient reductions in regression with mixed predictors
Online Inference of Topics with Latent Dirichlet Allocation
Universal Kernel-Based Learning with Applications to Regular Languages
Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm
Fourier Theoretic Probabilistic Inference over Permutations
Causal Reasoning with Ancestral Graphs
Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
Multi Kernel Learning with Online-Batch Optimization
Feature Selection, Association Rules Network and Theory Building
Stable Classification
Unsupervised feature selection applied to SPOT5 satellite images indexing
MCMC Methods for Bayesian Mixtures of Copulas
Learning Nondeterministic Classifiers
Autoencoders, Unsupervised Learning, and Deep Architectures
Interpretable Deep Generative Recommendation Models
Refinery: An Open Source Topic Modeling Web Platform
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
Maximum sampled conditional likelihood for informative subsampling
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
Active Learning Based on Parzen Window
Causal and Non-Causal Feature Selection for Ridge Regression
Convergent Decomposition Solvers for Tree-reweighted Free Energies
Nystrom Regularization for Time Series Forecasting
Generalized TD Learning
Recovery and Generalization in Over-Realized Dictionary Learning
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Summarization of Yes/No Questions Using a Feature Function Model
Improving posterior marginal approximations in latent Gaussian models
Graph Laplacians and their Convergence on Random Neighborhood Graphs
A Lattice of Sets of Alignments Built on the Common Subwords in a Finite Language
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
A max-norm constrained minimization approach to 1-bit matrix completion
Pseudo-Marginal Hamiltonian Monte Carlo
Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC
Early stopping and non-parametric regression: an optimal data-dependent stopping rule
MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling
Nonparametric Neighborhood Selection in Graphical Models
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
On the inductive bias of dropout
Evolutionary Function Approximation for Reinforcement Learning
On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping
Learning Causal Models That Make Correct Manipulation Predictions
Characteristic and Universal Tensor Product Kernels
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Optimal Convergence Rates for Convex Distributed Optimization in Networks
Negative Results for Active Learning with Convex Losses
On the relation between universality, characteristic kernels and RKHS embedding of measures
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
Ridges, Neural Networks, and the Radon Transform
Classification with a Reject Option using a Hinge Loss
Bernoulli Mixture Models for Markov Blanket Filtering and Classification
On Distance and Kernel Measures of Conditional Dependence
Treba: Efficient Numerically Stable EM for PFA
Minimax Adaptive Estimation of Nonparametric Hidden Markov Models
Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Agnostic learning of disjunctions on symmetric distributions
Relative Entropy Inverse Reinforcement Learning
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization
Faster Algorithms for Max-Product Message-Passing
The Benefit of Multitask Representation Learning
On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification
Blackwell Approachability and No-Regret Learning are Equivalent
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models
Suffcient Component Analysis
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Learning Thin Junction Trees via Graph Cuts
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations
Flexible Bayesian Product Mixture Models for Vector Autoregressions
Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
Semi-supervised eigenvectors for large-scale locally-biased learning
Supervised learning via Euler's Elastica models
Modular Regression: Improving Linear Models by Incorporating Auxiliary Data
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
Random Forest Weighted Local Fréchet Regression with Random Objects
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
MULTIBOOST: A Multi-purpose Boosting Package
A New Approach to Laplacian Solvers and Flow Problems
Union Support Recovery in Multi-task Learning
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Analysis of spectral clustering algorithms for community detection: the general bipartite setting
Visualizing Similarity Data with a Mixture of Maps
Operator-valued Kernels for Learning from Functional Response Data
On Bisubmodular Maximization
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold
Active Learning for Nonlinear System Identification with Guarantees
Stability Bounds for Stationary phi-mixing and beta-mixing Processes
A Unified Framework for Spectral Clustering in Sparse Graphs
Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification
Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
Learning Policy Improvements with Path Integrals
Do we need hundreds of classifiers to solve real world classification problems?
Complexity of Representation and Inference in Compositional Models with Part Sharing
Ellipsoidal rounding for nonnegative matrix factorization under noisy separability
Pre-trained Gaussian Processes for Bayesian Optimization
Manopt, a matlab toolbox for optimization on manifolds
Reversible Jump MCMC for Non-Negative Matrix Factorization
Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods
Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance
Neural Q-learning for solving PDEs
Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification
Introducing CURRENNT: the munich open-source CUDA recurrent neural network toolkit
A Streaming Parallel Decision Tree Algorithm
On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines
A Simpler Approach to Matrix Completion
Exploiting locality in high-dimensional Factorial hidden Markov models
On the properties of variational approximations of Gibbs posteriors
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
Achievability of asymptotic minimax regret by horizon-dependent and horizon-independent strategies
Max-Margin Ratio Machine
Probabilistic Learning on Graphs via Contextual Architectures
Subset Infinite Relational Models
Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case
A near-optimal algorithm for differentially-private principal components
Iteration complexity of feasible descent methods for convex optimization
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
Combining Knowledge from Different Sources in Causal Probabilistic Models
Improved Shrinkage Prediction under a Spiked Covariance Structure
Learning Local Dependence In Ordered Data
ICA for Watermarking Digital Images
Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
Marginalizing stacked linear denoising autoencoders
Single and Multiple Change-Point Detection with Differential Privacy
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
Minimax Regret Classifier for Imprecise Class Distributions
Distributional Word Clusters vs. Words for Text Categorization
Minimax Algorithm for Learning Rotations
Parallel Algorithm for Learning Optimal Bayesian Network Structure
Query induction with schema-guided pruning strategies
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Complete Identification Methods for the Causal Hierarchy
As You Like It: Localization via Paired Comparisons
Learning Bayesian Network Structure using LP Relaxations