yusuke1997/dblp-id-title huggingface dataset:
Multilevel Monte Carlo Variational Inference
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays
Concentration analysis of multivariate elliptic diffusions
String and Membrane Gaussian Processes
Collaborative Filtering on a Budget
Reproducing Kernel Banach Spaces for Machine Learning
Condition for perfect dimensionality recovery by variational Bayesian PCA
One-Class SVMs for Document Classification
Subgroup Discovery with CN2-SD
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation
Refined approachability algorithms and application to regret minimization with global costs
Semi-supervised Inference for Block-wise Missing Data without Imputation
Causal Discovery as a Game
Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
Ranking and synchronization from pairwise measurements via SVD
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
Critically Assessing the State of the Art in Neural Network Verification
Semi-Analytic Resampling in Lasso
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
High-dimensional covariance decomposition into sparse Markov and independence models
Dropout Regularization Versus l2-Penalization in the Linear Model
Exact Simplification of Support Vector Solutions
On Semiparametric Exponential Family Graphical Models
CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums
Error-Correcting Ouput Codes Library
A General Framework for Structured Sparsity via Proximal Optimization
Anytime Learning of Decision Trees
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency
Improved sequence classification using adaptive segmental sequence alignment
PromptBench: A Unified Library for Evaluation of Large Language Models
Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers
Total Stability of SVMs and Localized SVMs
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Learning the Structure of Linear Latent Variable Models
FineMorphs: Affine-Diffeomorphic Sequences for Regression
Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
Efficient learning and planning with compressed predictive states
Distributed Statistical Inference under Heterogeneity
Smooth epsiloon-Insensitive Regression by Loss Symmetrization
Approximate Inference by Intersecting Semidefinite Bound and Local Polytope
Robust Approximate Bilinear Programming for Value Function Approximation
Nonextensive Information Theoretic Kernels on Measures
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
The d-Separation Criterion in Categorical Probability
Semi-Supervised Mean Fields
Bounds on Individual Risk for Log-loss Predictors
High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model
PC algorithm for nonparanormal graphical models
An asynchronous parallel stochastic coordinate descent algorithm
Causal Search in Structural Vector Autoregressive Models
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
Feature Selection via Dependence Maximization
Nearly optimal classification for semimetrics
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
LibMTL: A Python Library for Deep Multi-Task Learning
Generative Models of Information Diffusion with Asynchronous Timedelay
LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
Density Estimation in Infinite Dimensional Exponential Families
Causal learning without DAGs
Deep linear networks can benignly overfit when shallow ones do
Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
Automating News Content Analysis: An Application to Gender Bias and Readability
Bilinear Discriminant Component Analysis
Scalable Bayes via Barycenter in Wasserstein Space
Refinable Kernels
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling
Follow the leader if you can, hedge if you must
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network
Robust online gesture recognition with crowdsourced annotations
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adaptive Data Depth via Multi-Armed Bandits
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Relational Learning with One Network: An Asymptotic Analysis
Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars
"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
Active Learning via Perfect Selective Classification
Covariance-based Clustering in Multivariate and Functional Data Analysis
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression
Interpretable algorithmic fairness in structured and unstructured data
Stick-breaking Construction for the Indian Buffet Process
Tensor Regression Networks
A Unified Framework for Model-based Clustering
PyGOD: A Python Library for Graph Outlier Detection
Steering Social Activity: A Stochastic Optimal Control Point Of View
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization
Learning and Interpreting Multi-Multi-Instance Learning Networks
Cautious Collective Classification
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction
Multiple-Splitting Projection Test for High-Dimensional Mean Vectors
Belief propagation for continuous state spaces: stochastic message-passing with quantitative guarantees
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Multivariate convex regression with adaptive partitioning
Discriminative Learning of Max-Sum Classifiers
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature
Factorization Machines with Regularization for Sparse Feature Interactions
Interpolating Predictors in High-Dimensional Factor Regression
MEKA: A Multi-label/Multi-target Extension to WEKA
Tractable Search for Learning Exponential Models of Rankings
Approximate Profile Maximum Likelihood
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Frustratingly Simplified Deployment in WLAN Localization by Learning from Route Annotation
Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach
Effective Wrapper-Filter hybridization through GRASP Schemata
Recovering PCA and Sparse PCA via Hybrid-(l1, l2) Sparse Sampling of Data Elements
Polynomial Identification in the Limit of Substitutable Context-free Languages
Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors
Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
On Boosting with Polynomially Bounded Distributions
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Learning Multilevel Distributed Representations for High-Dimensional Sequences
Matching Pursuit Kernel Fisher Discriminant Analysis
Quality assessment of nonlinear dimensionality reduction based on K-ary neighborhoods
Exploring Strategies for Training Deep Neural Networks
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
DBSCAN: Optimal Rates For Density-Based Cluster Estimation
Analogical Reasoning with Relational Bayesian Sets
Sparse Spectrum Gaussian Process Regression
Saturating Splines and Feature Selection
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
Adaptive Metropolis with Online Relabeling
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
Marginal Likelihood Integrals for Mixtures of Independence Models
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Model Monitor ( M 2 ): Evaluating, Comparing, and Monitoring Models
Rademacher Complexities and Bounding the Excess Risk in Active Learning
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training
Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
Practical Kernel-Based Reinforcement Learning
Managing Diversity in Regression Ensembles
Hierarchical Knowledge Gradient for Sequential Sampling
An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
Minimax Risk Classifiers with 0-1 Loss
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Failures of Model-dependent Generalization Bounds for Least-norm Interpolation
Risk bounds of learning processes for Lévy processes
Improved spectral community detection in large heterogeneous networks
Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport
Continuous Neural Networks
One-shot learning gesture recognition from RGB-D data using bag of features
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Refining the Confidence Level for Optimistic Bandit Strategies
The sample complexity of learning linear predictors with the squared loss
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
A truncated EM approach for spike-and-slab sparse coding
Keep it simple and sparse: real-time action recognition
Orlicz Random Fourier Features
Collaborative Filtering Ensemble
Measuring Dependence Powerfully and Equitably
Two Distributed-State Models For Generating High-Dimensional Time Series
Learning from many trajectories
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
Empirical Priors for Prediction in Sparse High-dimensional Linear Regression
Megaman: Scalable Manifold Learning in Python
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels
Benefitting from the Variables that Variable Selection Discards
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
Quantifying the Informativeness of Similarity Measurements
Second-order non-stationary online learning for regression
Links between multiplicity automata, observable operator models and predictive state representations: a unified learning framework
An extension of slow feature analysis for nonlinear blind source separation
Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis
Gap Between Theory and Practice: Noise Sensitive Word Alignment in Machine Translation
Bayesian Probabilistic Models for Image Retrieval
Adaptive Online Prediction by Following the Perturbed Leader
A statistical perspective on algorithmic leveraging
Differential Privacy for Bayesian Inference through Posterior Sampling
Domain Adaptation: A Small Sample Statistical Approach
Employing The Complete Face in AVSR to Recover from Facial Occlusions
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
Euclidean Embedding of Co-occurrence Data
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data
A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs
Bayesian Spiked Laplacian Graphs
The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Contextual Bandits with Linear Payoff Functions
Differentially private methods for managing model uncertainty in linear regression
Learning Instance-Specific Predictive Models
Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
Learning Translation Invariant Kernels for Classification
On Equivalence Relationships Between Classification and Ranking Algorithms
Search for Additive Nonlinear Time Series Causal Models
Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields
A Primal-Dual Convergence Analysis of Boosting
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data
Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning
Generative Kernels for Exponential Families
A stochastic bandit algorithm for scratch games
KDD Cup 2009 @ Budapest: feature partitioning and boosting
Sensitivity-Free Gradient Descent Algorithms
Multi-Source Feature Selection via Geometry-Dependent Covariance Analysis
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion
Statistically and Computationally Efficient Change Point Localization in Regression Settings
On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
A Spike and Slab Restricted Boltzmann Machine
Discussion of "Spectral Dimensionality Reduction via Maximum Entropy"
Dropout Training is Distributionally Robust Optimal
CODA: high dimensional copula discriminant analysis
Emerge and spread models and word burstiness
Euler Characteristic Tools for Topological Data Analysis
Estimating Causal Structure Using Conditional DAG Models
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression
GraSPy: Graph Statistics in Python
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters
Structured Sparse Principal Component Analysis
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions
Pathfinder: Parallel quasi-Newton variational inference
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching
Locally Substitutable Languages for Enhanced Inductive Leaps
Learning-augmented count-min sketches via Bayesian nonparametrics
Algebraic Geometric Comparison of Probability Distributions
A New Perspective for Information Theoretic Feature Selection
Gaussian Processes with Errors in Variables: Theory and Computation
Grokking phase transitions in learning local rules with gradient descent
Statistical test for consistent estimation of causal effects in linear non-Gaussian models
An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach
Baseline Methods for Active Learning
Learning Green's functions associated with time-dependent partial differential equations
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification
The fastclime package for linear programming and large-scale precision matrix estimation in R
On the Dynamics Under the Unhinged Loss and Beyond
Fluid Dynamics Models for Low Rank Discriminant Analysis
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
Measuring the Effects of Data Parallelism on Neural Network Training
How Well Generative Adversarial Networks Learn Distributions
Pareto Smoothed Importance Sampling
Maximum Entropy Correlated Equilibria
Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets
Score Matched Neural Exponential Families for Likelihood-Free Inference
Regularization and the small-ball method II: complexity dependent error rates
Improving Model Inference of Black Box Components having Large Input Test Set
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
Multiparameter Persistence Landscapes
Estimation of Non-Normalized Statistical Models by Score Matching
A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes
Automated Scalable Bayesian Inference via Hilbert Coresets
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
Fairness-Aware PAC Learning from Corrupted Data
Fixed points of nonnegative neural networks
Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables
Locally adaptive factor processes for multivariate time series
A Composite Likelihood View for Multi-Label Classification
Streaming Multi-label Classification
d3rlpy: An Offline Deep Reinforcement Learning Library
Spectral Dimensionality Reduction via Maximum Entropy
An Efficient Explanation of Individual Classifications using Game Theory
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Nonparametric prior for adaptive sparsity
The Block Diagonal Infinite Hidden Markov Model
Multiple-Instance Learning from Distributions
Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
Beyond Trees: Classification with Sparse Pairwise Dependencies
Online Nonnegative CP-dictionary Learning for Markovian Data
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
Limits of Dense Simplicial Complexes
Conditional Independencies under the Algorithmic Independence of Conditionals
Infinite Predictor Subspace Models for Multitask Learning
ORCA: A Matlab/Octave Toolbox for Ordinal Regression
Structured Variable Selection with Sparsity-Inducing Norms
Infinite Hierarchical Hidden Markov Models
History-alignment models for bias-aware prediction of virological response to HIV combination therapy
On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data
Coherent Inference on Optimal Play in Game Trees
Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems
Generalization error bounds for multiclass sparse linear classifiers
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Multiclass Learnability and the ERM principle
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Bayesian Quadrature for Ratios
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
(f, Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
Stochastic Semi-supervised Learning
Community Extraction in Multilayer Networks with Heterogeneous Community Structure
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis
Commentary on "The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators"
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
Bayesian Text Classification and Summarization via A Class-Specified Topic Model
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval
Learning Nonlinear Dynamic Models from Non-sequenced Data
Random intersection trees
Recovering Networks from Distance Data
Bayes Point Machines
Posterior distributions are computable from predictive distributions
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
Fast cross-validation via sequential testing
Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach
Dynamic Assortment Optimization with Changing Contextual Information
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics
Revisiting minimum description length complexity in overparameterized models
Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning
Convergence for nonconvex ADMM, with applications to CT imaging
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
A risk comparison of ordinary least squares vs ridge regression
MULAN: A Java Library for Multi-Label Learning
A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries
T-Cal: An Optimal Test for the Calibration of Predictive Models
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Consistent selection of tuning parameters via variable selection stability
Spectrum Estimation from a Few Entries
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints
Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization