yusuke1997/dblp-id-title huggingface dataset:
Cost-Sensitive Learning with Noisy Labels
Volterra Neural Networks (VNNs)
A Unified Framework for Optimization-Based Graph Coarsening
Facilitating score and causal inference trees for large observational studies
Linear fitted-Q iteration with multiple reward functions
Stable and Efficient Gaussian Process Calculations
Regularized Optimal Transport and the Rot Mover's Distance
Particle-Gibbs Sampling for Bayesian Feature Allocation Models
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
Online Sufficient Dimension Reduction Through Sliced Inverse Regression
LassoNet: A Neural Network with Feature Sparsity
Estimation of a Low-rank Topic-Based Model for Information Cascades
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
High-Rank Matrix Completion
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Estimating Labels from Label Proportions
Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Estimation Consistency of the Group Lasso and its Applications
Agnostic insurability of model classes
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity
Transferability of Spectral Graph Convolutional Neural Networks
Designing Committees of Models through Deliberate Weighting of Data Points
WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Efficient variable selection in support vector machines via the alternating direction method of multipliers
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
Derivative estimation based on difference sequence via locally weighted least squares regression
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning
Efficient Online and Batch Learning Using Forward Backward Splitting
Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms
Learning Ensembles from Bites: A Scalable and Accurate Approach
New Insights and Perspectives on the Natural Gradient Method
Classification with Gaussians and Convex Loss
Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs
Sparse Semi-supervised Learning Using Conjugate Functions
Multi-Label Output Codes using Canonical Correlation Analysis
Batch learning from logged bandit feedback through counterfactual risk minimization
Efficient Convex Algorithms for Universal Kernel Learning
Anechoic Blind Source Separation Using Wigner Marginals
Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Cluster-Specific Predictions with Multi-Task Gaussian Processes
On Tilted Losses in Machine Learning: Theory and Applications
Nonparametric Quantile Estimation
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
High-Dimensional Inference for Generalized Linear Models with Hidden Confounding
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Latent Variable Models for Dimensionality Reduction
Improving Policy Gradient Estimates with Influence Information
Empirical Risk Minimization under Random Censorship
A Parameter-Free Classification Method for Large Scale Learning
Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling"
Hope and Fear for Discriminative Training of Statistical Translation Models
A Hierarchy of Support Vector Machines for Pattern Detection
End-to-End Training of Deep Visuomotor Policies
Understanding the difficulty of training deep feedforward neural networks
Stochastic Methods for l 1 -regularized Loss Minimization
A Stochastic Quasi-Newton Method for Online Convex Optimization
Dynamics and Generalization Ability of LVQ Algorithms
Classification of Imbalanced Marketing Data with Balanced Random Sets
Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems
Joint Structural Estimation of Multiple Graphical Models
DARWIN: a framework for machine learning and computer vision research and development
Semiparametric Inference Using Fractional Posteriors
Linear Programming Relaxations and Belief Propagation - An Empirical Study
Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method
Off-policy learning with eligibility traces: a survey
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
Adaptive Exact Inference in Graphical Models
Large-scale linear support vector regression
Discussion of "The Neural Autoregressive Distribution Estimator"
Finite-sample analysis of least-squares policy iteration
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Alexey Chervonenkis's bibliography
An algorithmic view of L2 regularization and some path-following algorithms
Oger: modular learning architectures for large-scale sequential processing
POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
Lovász ϑ function, SVMs and finding dense subgraphs
Model Linkage Selection for Cooperative Learning
Online Variational Inference for the Hierarchical Dirichlet Process
An Instantiation-Based Theorem Prover for First-Order Programming
Efficient active learning with generalized linear models
Embarrassingly Parallel Inference for Gaussian Processes
Learning Algorithms for Second-Price Auctions with Reserve
Large data limit of the MBO scheme for data clustering: convergence of the dynamics
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
The Randomized Causation Coefficient
A Perturbation-Based Kernel Approximation Framework
Learning Tree Adjoining Grammars from Structures and Strings
Structured Prediction, Dual Extragradient and Bregman Projections
High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Learning Mean-Field Games with Discounted and Average Costs
Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph
Distance Preserving Embeddings for General n-Dimensional Manifolds
Generalized Resubstitution for Classification Error Estimation
Taxonomy-Informed Latent Factor Models for Implicit Feedback
Reconstruction from Anisotropic Random Measurements
Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion
Label Distribution Changing Learning with Sample Space Expanding
A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
Domain adaptation under structural causal models
Efficient Sampling from Time-Varying Log-Concave Distributions
Individual-centered Partial Information in Social Networks
Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine
Photonic delay systems as machine learning implementations
Iterated Learning in Dynamic Social Networks
A Survey of Preference-Based Reinforcement Learning Methods
Experience Selection in Deep Reinforcement Learning for Control
Bayesian nonparametric covariance regression
QUIC: quadratic approximation for sparse inverse covariance estimation
DPPy: DPP Sampling with Python
Learning Optimized Risk Scores
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Kernel Independent Component Analysis
Instance-Dependent Generalization Bounds via Optimal Transport
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
Statistical Comparisons of Classifiers over Multiple Data Sets
Minimax Filter: Learning to Preserve Privacy from Inference Attacks
GraKeL: A Graph Kernel Library in Python
Structure Identification by Optimized Interventions
Multi-layered gesture recognition with Kinect
Dimensionless machine learning: Imposing exact units equivariance
Learning Scalable Deep Kernels with Recurrent Structure
Approximation Vector Machines for Large-scale Online Learning
Energy-Based Models for Sparse Overcomplete Representations
Generalized Maximum Entropy Estimation
Change point localization in dependent dynamic nonparametric random dot product graphs
A Family of MCMC Methods on Implicitly Defined Manifolds
Fast Learning for Renewal Optimization in Online Task Scheduling
Asymptotic accuracy of distribution-based estimation of latent variables
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
Kernel Regression in the Presence of Correlated Errors
The Neural Autoregressive Distribution Estimator
Beyond Spectral Gap: The Role of the Topology in Decentralized Learning
Additive nonlinear quantile regression in ultra-high dimension
Learning with Fenchel-Young losses
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Clustering: Science or Art?
Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
One-class classification of point patterns of extremes
Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits
Incremental Sigmoid Belief Networks for Grammar Learning
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Continuous Time Bayesian Network Reasoning and Learning Engine
A compression technique for analyzing disagreement-based active learning
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Gaussian processes with monotonicity information
Recommender Systems Using Linear Classifier
Gaussian Process Boosting
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
A Gibbs Sampler for Learning DAGs
Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach
Learning the Kernel Function via Regularization
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition
Relational Topic Models for Document Networks
Core Vector Machines: Fast SVM Training on Very Large Data Sets
Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks
Sum of Ranked Range Loss for Supervised Learning
From Theories to Queries
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Bayesian Group Factor Analysis
Should We Really Use Post-Hoc Tests Based on Mean-Ranks?
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Speed and Sparsity of Regularized Boosting
Why Does Unsupervised Pre-training Help Deep Learning?
The Love-Hate Square Counting Method for Recommender Systems
Nonconvex Matrix Completion with Linearly Parameterized Factors
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
A Representer Theorem for Deep Kernel Learning
Adaptive strategy for stratified Monte Carlo sampling
glm-ie: Generalised Linear Models Inference & Estimation Toolbox
Perturbed message passing for constraint satisfaction problems
Polynomial-Delay Enumeration of Monotonic Graph Classes
Sparse Concordance-assisted Learning for Optimal Treatment Decision
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
The Kalai-Smorodinsky solution for many-objective Bayesian optimization
Maximum Relative Margin and Data-Dependent Regularization
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
Beyond Independent Components: Trees and Clusters
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
Transfer Learning for Auto-gating of Flow Cytometry Data
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
Differentially Private Data Releasing for Smooth Queries
Minimax Rates for High-Dimensional Random Tessellation Forests
Geometric structure of graph Laplacian embeddings
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Expanding Boundaries of Gap Safe Screening
Improving Markov network structure learning using decision trees
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations
Classifier Cascade for Minimizing Feature Evaluation Cost
Statistical topological data analysis using persistence landscapes
Hinge-Minimax Learner for the Ensemble of Hyperplanes
Nearly Uniform Validation Improves Compression-Based Error Bounds
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
Variational Message Passing
Estimation and Optimization of Composite Outcomes
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees
On Nonparametric Guidance for Learning Autoencoder Representations
Multi-Stage Classifier Design
Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
Adversarial Classification: Necessary Conditions and Geometric Flows
An Information Geometry Approach for Distance Metric Learning
Stochastic Gradient Descent as Approximate Bayesian Inference
Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space
Synergy of Monotonic Rules
Consistency of Random Forests and Other Averaging Classifiers
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
A Stochastic Algorithm for Feature Selection in Pattern Recognition
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
Multi-Agent Multi-Armed Bandits with Limited Communication
Convex Geometry and Duality of Over-parameterized Neural Networks
Multiclass-Multilabel Classification with More Classes than Examples
Predicting a switching sequence of graph labelings
CAT: Compression-Aware Training for bandwidth reduction
No-Regret Bayesian Optimization with Unknown Hyperparameters
Empirical Bayes Matrix Factorization
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning
Adaptive Lasso and group-Lasso for functional Poisson regression
Training SVMs Without Offset
Bayesian Learning of Dynamic Multilayer Networks
Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models
Time Series Analysis with the Causality Workbench
Differentiable Game Mechanics
Matching Words and Pictures
Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data
(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
An Exponential Model for Infinite Rankings
Sparse Bayesian Learning and the Relevance Vector Machine
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
Sources of Success for Boosted Wrapper Induction
Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case
pyDML: A Python Library for Distance Metric Learning
Stochastic Variance-Reduced Cubic Regularization Methods
Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
Local Anomaly Detection
Building Blocks for Variational Bayesian Learning of Latent Variable Models
Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method
Online Non-stochastic Control with Partial Feedback
Efficient large margin semisupervised learning
On the necessity of irrelevant variables
Accuracy-Rejection Curves (ARCs) for Comparing Classification Methods with a Reject Option
Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
Distributed Sparse Regression via Penalization
AutoKeras: An AutoML Library for Deep Learning
On Learning Discrete Graphical Models using Group-Sparse Regularization
Minimax hypothesis testing for curve registration
A Bayesian Analysis of the Radioactive Releases of Fukushima
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
Classification Methods with Reject Option Based on Convex Risk Minimization
Three rates of convergence or separation via U-statistics in a dependent framework
Fast Automatic Smoothing for Generalized Additive Models
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Can matrix coherence be efficiently and accurately estimated?
Learning the Structure of Deep Sparse Graphical Models
Fast rates in statistical and online learning
Measuring Differentiability: Unmasking Pseudonymous Authors
Cornac: A Comparative Framework for Multimodal Recommender Systems
Robust Multi-task Regression with Grossly Corrupted Observations
Keyword Optimization in Sponsored Search via Feature Selection
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
Diffusion Kernels on Statistical Manifolds
Integrated Principal Components Analysis
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Clearing Restarting Automata and Grammatical Inference
Data-Derived Weak Universal Consistency
Approximate Newton Methods for Policy Search in Markov Decision Processes
Tree-Structured Neural Decoding
Minimal Width for Universal Property of Deep RNN
Dependent Hierarchical Beta Process for Image Interpolation and Denoising
On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint
Learning the Switching Rate by Discretising Bernoulli Sources Online
Learning with the maximum correntropy criterion induced losses for regression
Geometric intuition and algorithms for Ev-SVM
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Adaptive Density Level Set Clustering
Communication-efficient Sparse Regression
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks
SVDFeature: a toolkit for feature-based collaborative filtering
A Generalization Error for Q-Learning
Distributed Learning with Regularized Least Squares
Causal Discovery from Heterogeneous/Nonstationary Data
NEVAE: A Deep Generative Model for Molecular Graphs
Convergence Guarantees for the Good-Turing Estimator
Testing Whether a Learning Procedure is Calibrated
Competitive Classification and Closeness Testing
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
Results of the PAutomaC Probabilistic Automaton Learning Competition
Cover tree Bayesian reinforcement learning
Counterfactual reasoning and learning systems: the example of computational advertising
Time-to-Event Prediction with Neural Networks and Cox Regression
Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction
Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
Fat-Shattering Dimension of k-fold Aggregations
Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory
Towards an axiomatic approach to hierarchical clustering of measures
Sparse Graph Learning from Spatiotemporal Time Series
Guarantees for Approximate Incremental SVMs
Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group
Multi-class Protein Classification Using Adaptive Codes
Evolving GPU machine code
Alleviating naive Bayes attribute independence assumption by attribute weighting
Evaluation of marginal likelihoods via the density of states
A Closer Look at Embedding Propagation for Manifold Smoothing
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Transport-based Counterfactual Models
Stochastic Unsupervised Learning on Unlabeled Data
Bayesian Policy Gradient and Actor-Critic Algorithms
Using More Data to Speed-up Training Time
Variable Selection for Gaussian Graphical Models
TIED: An Artificially Simulated Dataset with Multiple Markov Boundaries
Revisiting inference after prediction
A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification
MushroomRL: Simplifying Reinforcement Learning Research
L p -Nested Symmetric Distributions
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics
Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Performance bounds for λ policy iteration and application to the game of Tetris
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
Copula Network Classifiers (CNCs)
SAMOA: scalable advanced massive online analysis
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards
Towards Explainable Evaluation Metrics for Machine Translation
Predictive Discretization during Model Selection
Self-Healing Robust Neural Networks via Closed-Loop Control
The Geometry and Calculus of Losses
Multiscale Adaptive Representation of Signals: I. The Basic Framework
Volumetric Spanners: An Efficient Exploration Basis for Learning
Logistic Stick-Breaking Process