yusuke1997/dblp-id-title huggingface dataset:
Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
A Convex-Concave Relaxation Procedure Based Subgraph Matching Algorithm
Adaptive Clustering Using Kernel Density Estimators
Bayesian mixed-effects inference on classification performance in hierarchical data sets
On the Estimation of alpha-Divergences
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
Stochastic Composite Likelihood
Random Forests for Change Point Detection
Optimal Distributed Online Prediction Using Mini-Batches
Does an Efficient Calibrated Forecasting Strategy Exist?
Attraction-Repulsion Spectrum in Neighbor Embeddings
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery
Network Completion and Survey Sampling
Learning Scale Free Networks by Reweighted L1 regularization
pyts: A Python Package for Time Series Classification
A Kernel Multiple Change-point Algorithm via Model Selection
A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Consistency and Rates for Clustering with DBSCAN
An Error Analysis of Generative Adversarial Networks for Learning Distributions
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
A Model of Fake Data in Data-driven Analysis
Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study
The Hyperspherical Geometry of Community Detection: Modularity as a Distance
Shallow Parsing using Specialized HMMs
Further results on latent discourse models and word embeddings
Learning Transformation Models for Ranking and Survival Analysis
Sampling random graph homomorphisms and applications to network data analysis
A Primer for Neural Arithmetic Logic Modules
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
What regularized auto-encoders learn from the data-generating distribution
Security analysis of online centroid anomaly detection
Asymptotic Theory for Linear-Chain Conditional Random Fields
A Subgroup Discovery Approach for Relating Chemical Structure and Phenotype Data in Chemical Genomics
Multi-Task Learning for Classification with Dirichlet Process Priors
The Learning-Curve Sampling Method Applied to Model-Based Clustering
Stable-Baselines3: Reliable Reinforcement Learning Implementations
Spanning Tree Approximations for Conditional Random Fields
Perturbative corrections for approximate inference in Gaussian latent variable models
Contrastive Estimation Reveals Topic Posterior Information to Linear Models
Efficient Structure-preserving Support Tensor Train Machine
Efficient and Effective Visual Codebook Generation Using Additive Kernels
COKE: Communication-Censored Decentralized Kernel Learning
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
Bagged Structure Learning of Bayesian Network
Convexity of Proper Composite Binary Losses
A Multi-Stage Framework for Dantzig Selector and LASSO
Adaptive False Discovery Rate Control with Privacy Guarantee
Markov Topic Models
Confidence-Weighted Linear Classification for Text Categorization
Exploration and Exploitation with Insufficient Resources
Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Plug-and-play dual-tree algorithm runtime analysis
From Predictive Methods to Missing Data Imputation: An Optimization Approach
Memory-efficient inference in dynamic graphical models using multiple cores
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization
Topological Node2vec: Enhanced Graph Embedding via Persistent Homology
Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection
Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods
Deep Learning the Ising Model Near Criticality
Graphical Gaussian modelling of multivariate time series with latent variables
Sample Complexity of Composite Likelihood
Community Recovery in the Geometric Block Model
Efficient Hypergraph Clustering
Set-valued approachability and online learning with partial monitoring
Gradient Descent Learns Linear Dynamical Systems
Learning Coordinate Covariances via Gradients
Scalable Interpretable Multi-Response Regression via SEED
Visualizing pairwise similarity via semidefinite programming
Learning to Classify Ordinal Data: The Data Replication Method
An Entropy-Based Model for Hierarchical Learning
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method
Project and Forget: Solving Large-Scale Metric Constrained Problems
Regimes of No Gain in Multi-class Active Learning
Lossless Online Bayesian Bagging
µTOSS - Multiple hypothesis testing in an open software system
Infinite-sigma Limits For Tikhonov Regularization
Distributed Semi-supervised Learning with Kernel Ridge Regression
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Community Detection and Stochastic Block Models: Recent Developments
New results for random walk learning
ICA Using Spacings Estimates of Entropy
Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning
Bootstrapping Dependency Grammar Inducers from Incomplete Sentence Fragments via Austere Models
Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
Tensor decompositions for learning latent variable models
Learning symbolic representations of hybrid dynamical systems
Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models
Improving prediction from dirichlet process mixtures via enrichment
Distributed Algorithms for U-statistics-based Empirical Risk Minimization
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models
Online Incremental Feature Learning with Denoising Autoencoders
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
Online Learning of Structured Predictors with Multiple Kernels
Regret Bounds and Minimax Policies under Partial Monitoring
Regularized Joint Mixture Models
More Efficient Estimation for Logistic Regression with Optimal Subsamples
Exploiting Feature Covariance in High-Dimensional Online Learning
Iterate Averaging in the Quest for Best Test Error
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets
(Approximate) Subgradient Methods for Structured Prediction
Compression, Generalization and Learning
A direct estimation of high dimensional stationary vector autoregressions
Gaussian Processes in Practice, Bletchley Park, Bletchley, UK, June 12-13, 2006
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Approximate inference for the loss-calibrated Bayesian
Results of the Active Learning Challenge
Learning the Variance of the Reward-To-Go
HOP-MAP: Efficient Message Passing with High Order Potentials
Random Sets Approach and its Applications
Nonlinear acyclic causal models
MSVMpack: A Multi-Class Support Vector Machine Package
On Low-rank Trace Regression under General Sampling Distribution
Margin based Transductive Graph Cuts using Linear Programming
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks
Efficient Learning of Deep Boltzmann Machines
Finding optimal Bayesian networks using precedence constraints
Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds
Sparse Additive Machine
Distance preserving embeddings for general n-dimensional manifolds
Truncating the Loop Series Expansion for Belief Propagation
QuantMiner for mining quantitative association rules
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques
A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data
Convex Perturbations for Scalable Semidefinite Programming
Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression
Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
On the Influence of Momentum Acceleration on Online Learning
Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality
Adaptive Smoothing for Path Integral Control
Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit
Tight Lower Bounds on the VC-dimension of Geometric Set Systems
Gaussian Processes with Linear Operator Inequality Constraints
Revisiting MAP Estimation, Message Passing and Perfect Graphs
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally
Low Permutation-rank Matrices: Structural Properties and Noisy Completion
Fairness in Survival Analysis with Distributionally Robust Optimization
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
Non-Negative Semi-Supervised Learning
Positive Semidefinite Metric Learning Using Boosting-like Algorithms
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
How Deep Are Deep Gaussian Processes?
Fast Kernel Classifiers with Online and Active Learning
The Feature Selection Path in Kernel Methods
WEKA - Experiences with a Java Open-Source Project
A Multiscale Framework For Blind Separation of Linearly Mixed Signals
Learning a Mahalanobis Metric from Equivalence Constraints
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Learning DNF Expressions from Fourier Spectrum
Derivative estimation with local polynomial fitting
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models
The Pyramid Match Kernel: Efficient Learning with Sets of Features
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs
Multi-Consensus Decentralized Accelerated Gradient Descent
Comparing classification methods for predicting distance students' performance
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
Classification in Networked Data: A Toolkit and a Univariate Case Study
Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition
KELP: a Kernel-based Learning Platform
Recursive teaching dimension, VC-dimension and sample compression
Gaussian Margin Machines
Java-ML: A Machine Learning Library
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace
Regularized spectral methods for clustering signed networks
Depth separation beyond radial functions
Kernel Analysis of Deep Networks
CARP: Software for Fishing Out Good Clustering Algorithms
Prediction With Expert Advice For The Brier Game
Learning Horn Expressions with LOGAN-H
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
Knowledge-Based Kernel Approximation
Loop Corrected Belief Propagation
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation
Sparse Popularity Adjusted Stochastic Block Model
On the Performance of Kernel Classes
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
The Constrained Dantzig Selector with Enhanced Consistency
Interpretable and Fair Boolean Rule Sets via Column Generation
HMMPayl: an application of HMM to the analysis of the HTTP Payload
OLPS: A Toolbox for On-Line Portfolio Selection
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
Multivariate Boosted Trees and Applications to Forecasting and Control
Variational Relevance Vector Machine for Tabular Data
Multi-target regression with rule ensembles
sklvq: Scikit Learning Vector Quantization
SGDLibrary: A MATLAB library for stochastic optimization algorithms
On Markov chain Monte Carlo methods for tall data
Matrix Completion with Covariate Information and Informative Missingness
One-Shot Learning with a Hierarchical Nonparametric Bayesian Model
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
Gaussian Processes for time-marked time-series data
On the Rate of Convergence of Regularized Boosting Classifiers
Blending Learning and Inference in Conditional Random Fields
A Distributed Method for Fitting Laplacian Regularized Stratified Models
An Analysis of Quantile Temporal-Difference Learning
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
Pattern alternating maximization algorithm for missing data in high-dimensional problems
Stochastic Online Optimization using Kalman Recursion
Fairlearn: Assessing and Improving Fairness of AI Systems
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Overfitting in Making Comparisons Between Variable Selection Methods
Stochastic Canonical Correlation Analysis
Consistent Model Selection Criteria on High Dimensions
Local Causal Network Learning for Finding Pairs of Total and Direct Effects
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
Evolving Static Representations for Task Transfer
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors
Optimal Structure Identification With Greedy Search
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
Discrete restricted Boltzmann machines
Neural Empirical Bayes
Online Learning of Search Heuristics
Group Sparse Optimization via lp, q Regularization
Dynamic affine-invariant shape-appearance handshape features and classification in sign language videos
Fuzzy Grammar-based Prediction of Amyloidogenic Regions
Stochastic subgradient for composite convex optimization with functional constraints
A Boosting Algorithm for Label Covering in Multilabel Problems
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
Trained Transformers Learn Linear Models In-Context
Manifold Learning: The Price of Normalization
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
Neighborhood Structure Assisted Non-negative Matrix Factorization and Its Application in Unsupervised Point-wise Anomaly Detection
Asynchronous Online Testing of Multiple Hypotheses
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks
Off-Policy Actor-Critic with Emphatic Weightings
Active contextual policy search
Efficient active learning of halfspaces: an aggressive approach
Training Gaussian Mixture Models at Scale via Coresets
Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory
Dirichlet Process Mixtures of Generalized Linear Models
A classification module for genetic programming algorithms in JCLEC
Approximate Post-Selective Inference for Regression with the Group LASSO
Semi-parametric Learning of Structured Temporal Point Processes
New Bounds for Learning Intervals with Implications for Semi-Supervised Learning
Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions
Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials
Estimating Probabilities in Recommendation Systems
Efficient augmentation and relaxation learning for individualized treatment rules using observational data
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
Learning Temporal Association Rules on Symbolic Time Sequences
Online Clustering of Processes
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models
Intrinsic Dimension Estimation Using Wasserstein Distance
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data
Principled Out-of-Distribution Detection via Multiple Testing
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection
A tensor approach to learning mixed membership community models
A Graphical Representation of Equivalence Classes of AMP Chain Graphs
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms
Recovering Cyclic Causal Structure
On the Optimality of Misspecified Spectral Algorithms
An Interior-Point Method for Large-Scale l 1 -Regularized Logistic Regression
Low Tree-Rank Bayesian Vector Autoregression Models
Towards Minimax Policies for Online Linear Optimization with Bandit Feedback
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
Generalization Bounds for the Area Under the ROC Curve
TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
Finite Time LTI System Identification
Conjugate Gradients for Kernel Machines
Bayesian Canonical correlation analysis
Variational inference in nonconjugate models
A plug-in approach to neyman-pearson classification
Online Anomaly Detection under Adversarial Impact
Stable Graphical Models
Increasing Feature Selection Accuracy for L1 Regularized Linear Models
Approximate modified policy iteration and its application to the game of Tetris
Detecting click fraud in online advertising: a data mining approach
Discussion of "Learning Scale Free Networks by Reweighted L1 regularization"
Asymptotic results on adaptive false discovery rate controlling procedures based on kernel estimators
Bayesian Tensor Regression
Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling
Continuous Prediction with Experts' Advice
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
RankSEG: A Consistent Ranking-based Framework for Segmentation
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering
Discrete reproducing kernel Hilbert spaces: sampling and distribution of Dirac-masses
Alibi Explain: Algorithms for Explaining Machine Learning Models
Noise Tolerant Variants of the Perceptron Algorithm
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs
Stability of Controllers for Gaussian Process Dynamics
Learning Unfaithful $K$-separable Gaussian Graphical Models
On Unbiased Estimation for Partially Observed Diffusions
A determinantal point process for column subset selection
Parallel Symmetric Class Expression Learning
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
An Efficient Sampling Algorithm for Non-smooth Composite Potentials
Gaussian Process Approximations of Stochastic Differential Equations
ROOTS: Object-Centric Representation and Rendering of 3D Scenes
Effect-Invariant Mechanisms for Policy Generalization
A Note on Metric Properties for Some Divergence Measures: The Gaussian Case
Group Lasso Estimation of High-dimensional Covariance Matrices
Proximal Methods for Hierarchical Sparse Coding
Supervised dimension reduction with topic models
Analyzing Tensor Power Method Dynamics in Overcomplete Regime
Hierarchical Kernels in Deep Kernel Learning
Forward-Backward Selection with Early Dropping
Large-Margin Structured Prediction via Linear Programming
Prediction regions through Inverse Regression
Kernels Based Tests with Non-asymptotic Bootstrap Approaches for Two-sample Problems
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
On Learning with Integral Operators
Surrogate regret bounds for bipartite ranking via strongly proper losses
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
A Regularization-Based Adaptive Test for High-Dimensional GLMs
Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
Invariant Models for Causal Transfer Learning
Evidence Contrary to the Statistical View of Boosting
The em Algorithm for Kernel Matrix Completion with Auxiliary Data
Kernel Density Estimation for Dynamical Systems
A Statistical Learning Approach to Modal Regression
On Binary Embedding using Circulant Matrices
Generalized Non-metric Multidimensional Scaling
Linear-Time Computation of Similarity Measures for Sequential Data
Image Denoising with Kernels Based on Natural Image Relations
Random walk kernels and learning curves for Gaussian process regression on random graphs
FastInf: An Efficient Approximate Inference Library
Heterogeneous-Agent Reinforcement Learning
True Online Temporal-Difference Learning
Kernel Partial Correlation Coefficient - a Measure of Conditional Dependence
Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
Language-motivated approaches to action recognition
Consistent Algorithms for Clustering Time Series
Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
Multiclass Classification with Multi-Prototype Support Vector Machines
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
Tighter and Convex Maximum Margin Clustering
Markov Properties for Linear Causal Models with Correlated Errors
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
A novel greedy algorithm for Nyström approximation
Decision boundary for discrete Bayesian network classifiers
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
Neyman-Pearson Classification under High-Dimensional Settings
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning
Grammar Induction: Beyond Local Search
On the geometry of Stein variational gradient descent