yusuke1997/dblp-id-title huggingface dataset:
Distribution-Matching Embedding for Visual Domain Adaptation
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
Data Summarization via Bilevel Optimization
Identifiability of Additive Noise Models Using Conditional Variances
Efficient and accurate methods for updating generalized linear models with multiple feature additions
On Asymptotic and Finite-Time Optimality of Bayesian Predictors
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Estimating Causal Effects under Network Interference with Bayesian Generalized Propensity Scores
Distance Dependent Chinese Restaurant Processes
The Yahoo! Music Dataset and KDD-Cup '11
Autonomous Experimentation
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution
Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks
Multicore Gibbs Sampling in Dense, Unstructured Graphs
Memory-Efficient Sequential Pattern Mining with Hybrid Tries
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic
Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Design and Analysis of the Causation and Prediction Challenge
DESlib: A Dynamic ensemble selection library in Python
Bayesian Classifier Combination
Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
Beyond Logarithmic Bounds in Online Learning
Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity
On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
A Recursive Method for Structural Learning of Directed Acyclic Graphs
Mutual Information Based Matching for Causal Inference with Observational Data
Transductive Learning of Structural SVMs via Prior Knowledge Constraints
A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation
Multiple Kernel Learning on the Limit Order Book
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits
Managing Uncertainty within KTD
Learning to Construct Fast Signal Processing Implementations
Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms
Exact Recovery of Sparsely-Used Dictionaries
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
Support Vector Machinery for Infinite Ensemble Learning
Bayesian Generalized Kernel Mixed Models
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text
Factorial Mixture of Gaussians and the Marginal Independence Model
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
Knowledge Matters: Importance of Prior Information for Optimization
Combining Information Extraction Systems Using Voting and Stacked Generalization
Scalable Inference on Kingman's Coalescent using Pair Similarity
Incorporating Prior Knowledge on Features into Learning
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
RaSE: Random Subspace Ensemble Classification
Prediction against a limited adversary
Scaling the Convex Barrier with Sparse Dual Algorithms
Bayesian structure discovery in Bayesian networks with less space
scikit-multilearn: A Python library for Multi-Label Classification
A comparison of AUC estimators in small-sample studies
Multi-label Subspace Ensemble
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
Truncated power method for sparse eigenvalue problems
Learning to Optimize: A Primer and A Benchmark
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
Estimating beta-mixing coefficients
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
Learning Optimal Feedback Operators and their Sparse Polynomial Approximations
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits
Concentration Bounds for Unigram Language Models
Adaptive estimation of nonparametric functionals
Multi-Manifold Semi-Supervised Learning
On Representing and Generating Kernels by Fuzzy Equivalence Relations
On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap
Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Deep Learners Benefit More from Out-of-Distribution Examples
General Latent Feature Models for Heterogeneous Datasets
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS)
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
Matrix completion and low-rank SVD via fast alternating least squares
Nested expectation propagation for Gaussian process classification
Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling
Penalized Model-Based Clustering with Application to Variable Selection
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
Concentration inequalities for empirical processes of linear time series
Sparse Causal Discovery in Multivariate Time Series
Is SGD a Bayesian sampler? Well, almost
Recovering shared structure from multiple networks with unknown edge distributions
The LRP Toolbox for Artificial Neural Networks
Convex and Network Flow Optimization for Structured Sparsity
Selective inference for k-means clustering
Multi-stage multi-task feature learning
OpenEnsembles: A Python Resource for Ensemble Clustering
Double Updating Online Learning
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
Cells in Multidimensional Recurrent Neural Networks
Flexible Martingale Priors for Deep Hierarchies
FedLab: A Flexible Federated Learning Framework
The Proximal ID Algorithm
Logical Explanations for Deep Relational Machines Using Relevance Information
A topic modeling toolbox using belief propagation
Generalized Do-Calculus with Testable Causal Assumptions
Multiple Output Regression with Latent Noise
Generalized Optimal Matching Methods for Causal Inference
Identifying a Minimal Class of Models for High-dimensional Data
Variance estimation in graphs with the fused lasso
Covariances, Robustness, and Variational Bayes
Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design
On Model Selection Consistency of Lasso
Towards Learning to Imitate from a Single Video Demonstration
Agnostic Estimation for Phase Retrieval
Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction
A Permutation-Free Kernel Independence Test
LDLE: Low Distortion Local Eigenmaps
Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Variational methods for Reinforcement Learning
Theoretical Analysis of Bayesian Matrix Factorization
A Generative Model for Separating Illumination and Reflectance from Images
Bayesian inference with posterior regularization and applications to infinite latent SVMs
Learning to Match via Inverse Optimal Transport
Exact simulation of diffusion first exit times: algorithm acceleration
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
SubSift: a novel application of the vector space model to support the academic research process
Minimax Manifold Estimation
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Machine Learning with Data Dependent Hypothesis Classes
Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent?
Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty
A New Algorithm for Estimating the Effective Dimension-Reduction Subspace
LIBOL: a library for online learning algorithms
Discover Local Causal Network around a Target to a Given Depth
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling
Multimodal gesture recognition via multiple hypotheses rescoring
Model-based Boosting 2.0
Alternating linearization for structured regularization problems
Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification
Transfer Learning for Reinforcement Learning Domains: A Survey
Adaptive MCMC with Bayesian Optimization
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
AD 3 : alternating directions dual decomposition for MAP inference in graphical models
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
Model-Free Monte Carlo-like Policy Evaluation
Seglearn: A Python Package for Learning Sequences and Time Series
Document Neural Autoregressive Distribution Estimation
Near-optimal Individualized Treatment Recommendations
Hit Miss Networks with Applications to Instance Selection
OpenML-Python: an extensible Python API for OpenML
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Posterior Sparsity in Unsupervised Dependency Parsing
Noisy-OR Component Analysis and its Application to Link Analysis
Learning Fourier Sparse Set Functions
Batch Normalization Preconditioning for Neural Network Training
Multi-Player Bandits: The Adversarial Case
Learning from Examples as an Inverse Problem
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
Statistical Consistency of Kernel Canonical Correlation Analysis
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
DL-Learner: Learning Concepts in Description Logics
Modular Proximal Optimization for Multidimensional Total-Variation Regularization
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression
The Search Problem in Mixture Models
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms
Domain Generalization by Marginal Transfer Learning
Quantifying Network Similarity using Graph Cumulants
Learning Precise Timing with LSTM Recurrent Networks
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
Sample Complexity for Distributionally Robust Learning under chi-square divergence
Projection-free Decentralized Online Learning for Submodular Maximization over Time-Varying Networks
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
A Unified View on Multi-class Support Vector Classification
Efficient Reductions for Imitation Learning
A First Look into the Carbon Footprint of Federated Learning
Improving Variational Methods via Pairwise Linear Response Identities
Tracking Linear-threshold Concepts with Winnow
Multivariate Spearman's rho for Aggregating Ranks Using Copulas
Commentary on "Near-Optimal Algorithms for Online Matrix Prediction"
Message-Passing Algorithms for MAP Estimation Using DC Programming
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
Predicting customer behaviour: The University of Melbourne's KDD Cup report
Sparsity Regret Bounds for Individual Sequences in Online Linear Regression
Entangled Kernels - Beyond Separability
Locally Defined Principal Curves and Surfaces
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Thompson Sampling Algorithms for Cascading Bandits
Learning to Locate Relative Outliers
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
Provably Efficient Learning with Typed Parametric Models
Universal Multi-Task Kernels
QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization
Prediction Equilibrium for Dynamic Network Flows
On Bayesian Upper Confidence Bounds for Bandit Problems
An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension
Quantifying Uncertainty in Online Regression Forests
Inference for the Case Probability in High-dimensional Logistic Regression
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models
Differentially Private Hypothesis Testing for Linear Regression
Online Learning of Multiple Tasks with a Shared Loss
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
Policy evaluation with temporal differences: a survey and comparison
Network granger causality with inherent grouping structure
A Bayesian Framework for Learning Rule Sets for Interpretable Classification
Distributed Inference for Linear Support Vector Machine
A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for Detecting Multiple Dependencies
Adaptation Based on Generalized Discrepancy
Unsupervised Supervised Learning II: Margin-Based Classification without Labels
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
Nash Q-Learning for General-Sum Stochastic Games
On the learnability of shuffle ideals
A C++ template-based reinforcement learning library: fitting the code to the mathematics
Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process
Minimax optimal approaches to the label shift problem in non-parametric settings
Gaussian Processes for Ordinal Regression
Locality Preserving Feature Learning
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Marginal Regression For Multitask Learning
Shared Subspace Models for Multi-Group Covariance Estimation
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models
No Weighted-Regret Learning in Adversarial Bandits with Delays
Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm
Double Spike Dirichlet Priors for Structured Weighting
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Universal consistency of localized versions of regularized kernel methods
A New and Flexible Approach to the Analysis of Paired Comparison Data
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife
Block-sparse Solutions using Kernel Block RIP and its Application to Group Lasso
Optimal Allocation Strategies for the Dark Pool Problem
Learning Probabilistic Models of Link Structure
Complexity of equivalence and learning for multiplicity tree automata
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
Monotone multi-armed bandit allocations
Walk-Sums and Belief Propagation in Gaussian Graphical Models
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
Semi-supervised interpolation in an anticausal learning scenario
Learning Latent Variable Models by Pairwise Cluster Comparison
On Mixup Regularization
The Group Dantzig Selector
Concave penalized estimation of sparse Gaussian Bayesian networks
A Contextual Bandit Bake-off
Learning When Concepts Abound
Solving Markov Random Fields with Spectral Relaxation
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem
Sparse and unique nonnegative matrix factorization through data preprocessing
FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples
Dense Message Passing for Sparse Principal Component Analysis
On the Convergence of Optimistic Policy Iteration
Transfer Learning in Information Criteria-based Feature Selection
Entropy Search for Information-Efficient Global Optimization
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Multiclass learnability and the ERM principle
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity
Importance Sampling for Minibatches
Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models
Robust Bayesian Matrix Factorisation
Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models
Stationary-sparse causality network learning
Discovering Salient Neurons in deep NLP models
New Algorithms for Efficient High-Dimensional Nonparametric Classification
Laplacian Support Vector Machines Trained in the Primal
Normal Bandits of Unknown Means and Variances
On the optimality of the Hedge algorithm in the stochastic regime
Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points
Quadratic Decomposable Submodular Function Minimization: Theory and Practice
Exclusive Lasso for Multi-task Feature Selection
Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials
Online tensor methods for learning latent variable models
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Posterior Regularization for Structured Latent Variable Models
Distribution-dependent sample complexity of large margin learning
Generalization Error Bounds for Threshold Decision Lists
Sparse Exchangeable Graphs and Their Limits via Graphon Processes
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Towards ultrahigh dimensional feature selection for big data
Convergence of an Alternating Maximization Procedure
Some Discriminant-Based PAC Algorithms
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
Ranking forests
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation
On the Relationship Between Feature Selection and Classification Accuracy
Statistical decision making for optimal budget allocation in crowd labeling
Bandit problems with fidelity rewards
Exact Bayesian Structure Discovery in Bayesian Networks
Histogram Transform Ensembles for Large-scale Regression
Structure Learning for Directed Trees
Regularization Paths with Guarantees for Convex Semidefinite Optimization
Comparing hard and overlapping clusterings
Approximation of hidden Markov models by mixtures of experts with application to particle filtering
Causality with Gates
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation
Scalable and Efficient Hypothesis Testing with Random Forests
Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013
An Extensive Empirical Study of Feature Selection Metrics for Text Classification
Spectral Clustering on a Budget
A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events
Fast Mean Shift with Accurate and Stable Convergence
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
Euler-Lagrange Analysis of Generative Adversarial Networks
Linear Regression With Unmatched Data: A Deconvolution Perspective
Learnability of Gaussians with Flexible Variances
Statistical Optimization in High Dimensions
On lp-Support Vector Machines and Multidimensional Kernels
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
V-matrix method of solving statistical inference problems
Shallow Parsing with PoS Taggers and Linguistic Features
Tree-Based Batch Mode Reinforcement Learning
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes
Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning
Statistical Dynamics of On-line Independent Component Analysis
MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions
A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States
Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks
AIC and BIC based approaches for SVM parameter value estimation with RBF kernels
Reducing Label Complexity by Learning From Bags
Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization
Spam Filtering Using Statistical Data Compression Models
Relational Dependency Networks
Variational Inference in high-dimensional linear regression
Hidden-Unit Conditional Random Fields
Gradient Methods Never Overfit On Separable Data
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Streamlined Variational Inference with Higher Level Random Effects
Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms
A Framework for Improving the Reliability of Black-box Variational Inference
The Sample Complexity of Self-Verifying Bayesian Active Learning
Learning Algorithms for the Classification Restricted Boltzmann Machine
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
Multi-instance learning with any hypothesis class
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
Introduction to the Special Issue on Learning Theory
Mixed Cumulative Distribution Networks
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
An Alternative Prior Process for Nonparametric Bayesian Clustering
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
Metrizing Weak Convergence with Maximum Mean Discrepancies
Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources
Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions
Non-linear Causal Inference using Gaussianity Measures
Deep Learning in Target Space
A Constructive Approach to $L_0$ Penalized Regression