yusuke1997/dblp-id-title huggingface dataset:
Global multi-output decision trees for interaction prediction
Selecting a Classification Method by Cross-Validation
Post-selection confidence bounds for prediction performance
Statistical properties of kernel principal component analysis
QCC: a novel clustering algorithm based on Quasi-Cluster Centers
Nested barycentric coordinate system as an explicit feature map for polyhedra approximation and learning tasks
Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm
Aggregate features and ADABOOSTfor music classification
The Utility of Knowledge in Inductive Learning
Interactive relational reinforcement learning of concept semantics
Extreme value correction: a method for correcting optimistic estimations in rule learning
Propositionalization-based relational subgroup discovery with RSD
Fast greedy C -bound minimization with guarantees
Satellite derived bathymetry using deep learning
Fair prediction sets through multi-objective hyperparameter optimization
Guest editorial: special issue on learning theory
Ranking data with ordinal labels: optimality and pairwise aggregation
Support Vector Data Description
Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics
Explainable reinforcement learning (XRL): a systematic literature review and taxonomy
Unified SVM algorithm based on LS-DC loss
Asymptotic accuracy of Bayes estimation for latent variables with redundancy
Data driven conditional optimal transport
A comparison of proximity-based methods for detecting temporal anomalies in business processes
Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases
Big Data: from collection to visualization
Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm
Tracking treatment effect heterogeneity in evolving environments
Effective short-term opponent exploitation in simplified poker
An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts
Exploratory Research in Machine Learning
Correction to: Robust classification via MOM minimization
Integration of multi-modal datasets to estimate human aging
Grounded action transformation for sim-to-real reinforcement learning
Maximum Entropy Modeling: A Suitable Framework to Learn Context-Dependent Lexicon Models for Statistical Machine Translation
Warping resilient robust anomaly detection for multivariate time series
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation
The KANDY benchmark: Incremental neuro-symbolic learning and reasoning with Kandinsky patterns
Classic Learning
Fast online feature selection in streaming data
Introduction to the special issue on mining and learning with graphs
An expressive dissimilarity measure for relational clustering using neighbourhood trees
Joint detection of malicious domains and infected clients
A novel probabilistic clustering model for heterogeneous networks
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Encouraging Experimental Results on Learning CNF
Half-space mass: a maximally robust and efficient data depth method
Machine Learning and Qualitative Reasoning
Construction and learnability of canonical Horn formulas
Guest editors' introduction: special issue on mining and learning with graphs
Incremental predictive clustering trees for online semi-supervised multi-target regression
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Scale-preserving automatic concept extraction (SPACE)
Adversarial examples for extreme multilabel text classification
Quantitative Gaussian approximation of randomly initialized deep neural networks
Novel applications of item response theory for analysing data set complexity and benchmark selection
Classifier Systems that Learn Internal World Models
On the analysis of adaptability in multi-source domain adaptation
Structured large margin machines: sensitive to data distributions
An asymptotically optimal policy for finite support models in the multiarmed bandit problem
Pruning Algorithms for Rule Learning
Efficient and interpretable raw audio classification with diagonal state space models
Calibrated explanations for regression
When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days
Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons
Training Invariant Support Vector Machines
Weightless neural networks for open set recognition
Arbitrary conditional inference in variational autoencoders via fast prior network training
A Bayesian Method for the Induction of Probabilistic Networks from Data
Online dimensionality reduction through stacked generalization of spectral methods with deep networks
Large margin classification with indefinite similarities
PROUD: PaRetO-gUided diffusion model for multi-objective generation
Editorial: New Editorial Board Members
Learning with infinitely many features
On the analysis and design of software for reinforcement learning, with a survey of existing systems
Machine truth serum: a surprisingly popular approach to improving ensemble methods
Foraging theory for dimensionality reduction of clustered data
Hierarchically structured task-agnostic continual learning
Khiops: A Statistical Discretization Method of Continuous Attributes
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics
Partitioned hybrid learning of Bayesian network structures
Introduction to the Special Issue on Grammatical Inference
Inferring individual direct causal effects under heterogeneous peer influence
Resolving power: a general approach to compare the distinguishing ability of threshold-free evaluation metrics
How to measure uncertainty in uncertainty sampling for active learning
Metacluster-based Projective Clustering Ensembles
Correction to: Modeling PU learning using probabilistic logic programming
Distributed and explainable GHSOM for anomaly detection in sensor networks
Research Note on Decision Lists
Low-Rank Fully-Connected Tensor Network Learning for Tensor-on-Tensor Regression
Stability of Unstable Learning Algorithms
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions
Commonsense knowledge enhanced event graph representation learning for script event prediction
Learning from crowds with sparse and imbalanced annotations
Wasserstein dropout
Sketching information divergences
Integrating human knowledge for explainable AI
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
A Formalism for Relevance and Its Application in Feature Subset Selection
Online variance minimization
A Review of the Fourth International Workshop on Machine Learning
A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping
SWoTTeD: an extension of tensor decomposition to temporal phenotyping
Robust Sensor Fusion: Analysis and Application to Audio Visual Speech Recognition
Meta-learning for evolutionary parameter optimization of classifiers
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
Guest editorial: special issue on Inductive Logic Programming
The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures
Interpretable clustering: an optimization approach
Classifier calibration: a survey on how to assess and improve predicted class probabilities
Generalization bounds for metric and similarity learning
A Further Comparison of Splitting Rules for Decision-Tree Induction
Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity
Guest editors introduction: special issue on Inductive Logic Programming (ILP 2012)
Model-based transductive learning of the kernel matrix
Relational Learning with Statistical Predicate Invention: Better Models for Hypertext
Linearly Combining Density Estimators via Stacking
Learning explanations for biological feedback with delays using an event calculus
Tracking people over time in 19th century Canada for longitudinal analysis
Distribution-free uncertainty quantification for kernel methods by gradient perturbations
Machine Learning and Concept Formation
Integrating Guidance into Relational Reinforcement Learning
Discovering subjectively interesting multigraph patterns
Lessons and Challenges from Mining Retail E-Commerce Data
A Bayesian approach for comparing cross-validated algorithms on multiple data sets
Methods for the combination of kernel matrices within a support vector framework
Word embeddings-based transfer learning for boosted relational dependency networks
Normalizing flow sampling with Langevin dynamics in the latent space
Ranking and Reranking with Perceptron
The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training
Re-attentive experience replay in off-policy reinforcement learning
Learning de-biased environment models for delivery incentive policy optimization on food delivery platforms
Multi Level Knowledge in Modeling Qualitative Physics Learning
Boosting and Hard-Core Set Construction
Probabilistic inference for determining options in reinforcement learning
A Reply to Hellerstein's Book Review of Machine Learning: A Theoretical Approach
Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
Efficient fair principal component analysis
Introduction: Special Issue on Theoretical Advances in Data Clustering
SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments
Analysis of classifiers' robustness to adversarial perturbations
Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads
Targeted adversarial attacks on wind power forecasts
Efficient quantification on large-scale networks
Multi-armed bandits with censored consumption of resources
A General Framework for Induction and a Study of Selective Induction
Top program construction and reduction for polynomial time Meta-Interpretive learning
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
Mining adversarial patterns via regularized loss minimization
Acquiring and Combining Overlapping Concepts
Analyzing RL components for Wagner's framework via Brouwer's conjecture
Cost-sensitive label embedding for multi-label classification
Exposing and explaining fake news on-the-fly
Likelihood-ratio-based confidence intervals for neural networks
Learning from interpretation transition
Guest editorial: special issue on machine learning for soccer
Explainable online ensemble of deep neural network pruning for time series forecasting
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models
Semi-supervised Latent Block Model with pairwise constraints
JANET: Joint Adaptive predictioN-region Estimation for Time-series
A distributed Frank-Wolfe framework for learning low-rank matrices with the trace norm
Persian offensive language detection
Learning from Different Teachers
Learning multi-axis representation in frequency domain for medical image segmentation
MODES: model-based optimization on distributed embedded systems
A survey on interpretable reinforcement learning
Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP
Fraud detection with natural language processing
Exceptional contextual subgraph mining
Towards harnessing feature embedding for robust learning with noisy labels
Conformal multistep-ahead multivariate time-series forecasting
Conceptual Clustering, Categorization, and Polymorphy
Classification of Multivariate Time Series and Structured Data Using Constructive Induction
Stream-based semi-supervised learning for recommender systems
Learning efficient logic programs
Graph kernels based on tree patterns for molecules
A new large-scale learning algorithm for generalized additive models
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
Piecewise training for structured prediction
Calibration and regret bounds for order-preserving surrogate losses in learning to rank
Inclusion of Textual Documentation in the Analysis of Multidimensional Data Sets: Application to Gene Expression Data
F*: an interpretable transformation of the F-measure
Enlarging the Margins in Perceptron Decision Trees
Dual-domain graph convolutional networks for skeleton-based action recognition
NP-hardness of Euclidean sum-of-squares clustering
Boosting as a kernel-based method
Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD)
Trustworthy Representation Learning via Information Funnels and Bottlenecks
Protect privacy of deep classification networks by exploiting their generative power
Computational complexity of kernel-based density-ratio estimation: a condition number analysis
Extracting Context-Sensitive Models in Inductive Logic Programming
Learning from Examples and Membership Queries with Structured Determinations
Complexity-Based Induction
Generating probabilistic safety guarantees for neural network controllers
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Exploration Bonuses and Dual Control
Compressed labeling on distilled labelsets for multi-label learning
Semi-supervised online structure learning for composite event recognition
Guest editors' introduction: special issue of selected papers from ECML-PKDD 2012
Inductive equivalence in clausal logic and nonmonotonic logic programming
Generalized gradient learning on time series
Boosted Bayesian network classifiers
Guest editors' note
Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods
A unified view of forward and backward losses for learning from weak labels
How to Better Use Expert Advice
Classification with guaranteed probability of error
An unsupervised adversarial domain adaptation based on variational auto-encoder
A semiparametric method for clustering mixed data
Pointwise exact bootstrap distributions of ROC curves
Relational data embeddings for feature enrichment with background information
Active learning for logistic regression: an evaluation
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
Exploiting label dependencies for improved sample complexity
Compositional scene modeling with global object-centric representations
Algorithms for optimal dyadic decision trees
Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems
Effective feature construction by maximum common subgraph sampling
Dyad ranking using Plackett-Luce models based on joint feature representations
Optimizing regression models for data streams with missing values
PAutomaC: a probabilistic automata and hidden Markov models learning competition
Recurrent segmentation meets block models in temporal networks
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Non-redundant multiple clustering by nonnegative matrix factorization
Learning relational dependency networks in hybrid domains
Synthesizing efficient data with diffusion models for person re-identification pre-training
Learning Coordination Strategies for Cooperative Multiagent Systems
Refining Numerical Constants in First Order Logic Theories
Feature ranking for semi-supervised learning
MODL: A Bayes optimal discretization method for continuous attributes
Quantitative pharmacophore models with inductive logic programming
Convex multi-task feature learning
Structured prediction with reinforcement learning
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication
New closed-form bounds on the partition function
Random fourier features for asymmetric kernels
Greedy structure learning from data that contain systematic missing values
ST-PPO: a spatio-temporal attention enhanced proximal policy optimization algorithm for autonomous driving in complex traffic scenarios
On the robustness of randomized classifiers to adversarial examples
Auction-based incentive mechanism with personalized privacy protection in federated learning
A primal-dual perspective of online learning algorithms
MbExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
PAC Learning of One-Dimensional Patterns
Efficient federated unlearning under plausible deniability
Discrete-time graph neural networks for transaction prediction in Web3 social platforms
Optimized YOLOv8 for lightweight and high-precision metal surface defect detection in industrial applications
Predictive Modelling of Heterogeneous Sequence Collections by Topographic Ordering of Histories
SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels
Technical Note: Bias and the Quantification of Stability
Supervised maximum variance unfolding
Genetic Reinforcement Learning for Neurocontrol Problems
Detecting communities and their evolutions in dynamic social networks - a Bayesian approach
Meta-classifier free negative sampling for extreme multilabel classification
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Attacking neural machine translations via hybrid attention learning
On analyzing user preference dynamics with temporal social networks
Probabilistic topic models for sequence data
Bivariate Scientific Function Finding in a Sampled, Real-Data Testbed
Principled diverse counterfactuals in multilinear models
A Study of Explanation-Based Methods for Inductive Learning
Reducing classifier overconfidence against adversaries through graph algorithms
Explaining classifiers by constructing familiar concepts
Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs
Continuation methods for approximate large scale object sequencing
Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search
Analyzing the effect of residual connections to oversmoothing in graph neural networks
Constrained regret minimization for multi-criterion multi-armed bandits
How to reverse-engineer quality rankings
Introduction to the special issue on learning from multi-label data
A new adaptive gradient method with gradient decomposition
gBoost: a mathematical programming approach to graph classification and regression
Panda: partially approximate newton methods for distributed minimax optimization with unbalanced dimensions
Online strongly convex optimization with unknown delays
Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization
Beyond What's Normal: Bimodal and Heaviside Alternatives to Gaussian Process Regression
An Algorithm that Learns What's in a Name
Multiclass optimal classification trees with SVM-splits
Asynchronous Stochastic Approximation and Q-Learning
Squared Wasserstein-2 loss functions for efficient learning of stochastic differential equations
Explaining outliers and anomalous groups via subspace density contrastive loss
Extracting automata from recurrent neural networks using queries and counterexamples (extended version)
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering
Learning goal hierarchies from structured observations and expert annotations
A self-training approach to cost sensitive uncertainty sampling
Deep multimodal representation learning for generalizable person re-identification
Fast linear model trees by PILOT
On using nearly-independent feature families for high precision and confidence
Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting
Adversarial concept drift detection under poisoning attacks for robust data stream mining
Soft-max boosting
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Weak approximation of transformed stochastic gradient MCMC
No regret sample selection with noisy labels
Lifted graphical models: a survey
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms
Autoencoding slow representations for semi-supervised data-efficient regression
Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?
Early classification of time series
Data complexity meta-features for regression problems
VE-cGAN: Improved Generalization Analysis of Conditional Generative Adversarial Networks Using Vicinal Estimation
Dexter: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration
Robust partial-label learning by leveraging class activation values
Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Sparse and smooth additive isotonic model in high-dimensional settings
Transfer bounds for linear feature learning
Birds look like cars: adversarial analysis of intrinsically interpretable deep learning
Non-technical losses detection in energy consumption focusing on energy recovery and explainability
Matrix representations, linear transformations, and kernels for disambiguation in natural language
Soft quantification in statistical relational learning
All-time safety and sample-efficient meta update for online safe meta reinforcement learning under Markov task transition
Order preserving hierarchical agglomerative clustering
An aggregate and iterative disaggregate algorithm with proven optimality in machine learning
SketchMLP: effectively utilize rasterized images and drawing sequences for sketch recognition
Learning Probabilistic Read-once Formulas on Product Distributions
Evo-Path: a two-stage temporal knowledge graph reasoning model and its application in human behavior prediction
On-Line Learning from Search Failures
Consensus hashing
An Incremental Deductive Strategy for Controlling Constructive Induction in Learning from Examples
PRL: A probabilistic relational language
A flexible probabilistic framework for large-margin mixture of experts
A majorization-minimization approach to the sparse generalized eigenvalue problem
A k -norm pruning algorithm for decision tree classifiers based on error rate estimation
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams
A Theory of Historical Discovery: The Construction of Componential Models
Learning in the " Real World"
The role of mutual information in variational classifiers
Explanation-Based Learning: An Alternative View
Learning in the Presence of Concept Drift and Hidden Contexts
Inductive general game playing
Triadic Formal Concept Analysis and triclustering: searching for optimal patterns
Consensus-based modeling using distributed feature construction with ILP
Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes
Skill-based curiosity for intrinsically motivated reinforcement learning
Training a Vision Guided Mobile Robot
Algorithm selection on a meta level
Considerations when learning additive explanations for black-box models
Is it still fair? A comparative evaluation of fairness algorithms through the lens of covariate drift
Surrogate maximization/minimization algorithms and extensions
Empirical Bayes linked matrix decomposition
Manifold-based synthetic oversampling with manifold conformance estimation
Structural Results About On-line Learning Models With and Without Queries