yusuke1997/dblp-id-title huggingface dataset:
Selective Sampling Using the Query by Committee Algorithm
Algorithms for learning parsimonious context trees
Assessing the risk of discriminatory bias in classification datasets
Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy
ILP turns 20 - Biography and future challenges
Troubleshooting image segmentation models with human-in-the-loop
Semi-supervised learning from tabular data with autoencoders: when does it work?
Can metafeatures help improve explanations of prediction models when using behavioral and textual data?
An Experimental Comparison of Model-Based Clustering Methods
ISA 3 : a 3-dimensional expansion of instance space analysis
Correction to: Conformal load prediction with transductive graph autoencoders
High-dimensional correlation matrix estimation for general continuous data with Bagging technique
Block coordinate descent algorithms for large-scale sparse multiclass classification
Guest editors' introduction: Special issue on Inductive Logic Programming (ILP-2008)
A general criterion and an algorithmic framework for learning in multi-agent systems
Achieving collective welfare in multi-agent reinforcement learning via suggestion sharing
ROC curves in cost space
A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting
Utilizing reinforcement learning for de novo drug design
Softmin discrete minimax classifier for imbalanced classes and prior probability shifts
AUTOMAT[R]IX: learning simple matrix pipelines
Manas: multi-agent neural architecture search
An empirical study on image bag generators for multi-instance learning
Population Markov Chain Monte Carlo
Guest Editors' introduction: special issue of selected papers from ECML PKDD 2008
Clustering with missing features: a penalized dissimilarity measure based approach
Heterogeneous graph embedding with single-level aggregation and infomax encoding
The Lagging Anchor Algorithm: Reinforcement Learning in Two-Player Zero-Sum Games with Imperfect Information
DEFT: distilling entangled factors by preventing information diffusion
Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers
Efficient implementation of class-based decomposition schemes for Naïve Bayes
Learning from interpretation transition using differentiable logic programming semantics
Reinforcement learning tutor better supported lower performers in a math task
Convex optimization with an interpolation-based projection and its application to deep learning
Modelradar: aspect-based forecast evaluation
Projection Support Vector Machine Generators
A hybrid ensemble method with negative correlation learning for regression
Learning probabilistic logic models from probabilistic examples
Incremental learning of event definitions with Inductive Logic Programming
Minimum discrepancy principle strategy for choosing k in k-NN regression
Combining instance-based learning and logistic regression for multilabel classification
Introduction to the Abstracts of the Invited Talks Presented at ML92 Conference in Aberdeen, 1-3 July 1992
Adaptive covariate acquisition for minimizing total cost of classification
Enhancing Low-Degree Graph Neural Networks via Joint Training and Improved Message Passing
Gaussian conditional random fields extended for directed graphs
Instance spaces for machine learning classification
Convergence analysis of kernel Canonical Correlation Analysis: theory and practice
Learning directed relational models with recursive dependencies
Boundary-restricted metric learning
Asymptotic analysis of the learning curve for Gaussian process regression
Contrastive counterfactual visual explanations with overdetermination
Improving maximum margin matrix factorization
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
Combining Classifiers with Meta Decision Trees
Exploration and generalization in deep learning with SwitchPath activations
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
Deep latent force models: ODE-based process convolutions for Bayesian deep learning
Annotation cost-sensitive active learning by tree sampling
Learning and Updating of Uncertainty in Dirichlet Models
Accelerated gradient boosting
Data and task parallelism in ILP using MapReduce
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Probabilistic (logic) programming concepts
Adaptive regularization of weight vectors
Mind the gap: from plausible to valid self-explanations in large language models
Paf-tracker: a novel pre-frame auxiliary and fusion visual tracker
A Reply to Zito-Wolf's Book Review of Learning Search Control Knowledge: An Explanation-Based Approach
Few-shot learning for spatial regression via neural embedding-based Gaussian processes
Scenic: a language for scenario specification and data generation
Learning safe multi-label prediction for weakly labeled data
Drop-in efficient self-attention approximation method
On-Line Learning of Rectangles and Unions of Rectangles
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Multi-label image classification with multi-layered multi-perspective dynamic semantic representation
A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments
Correction: Learning sample-aware threshold for semi-supervised learning
Massively parallel feature selection: an approach based on variance preservation
Ddog: optimizing multi-hop inference via dual-driven retrieval and reasoning path
Bottom-Up Induction of Feature Terms
Polynomial time Learnability of Simple Deterministic Languages
Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting - application to machine health prognostics
Empirical evaluation methods for multiobjective reinforcement learning algorithms
Imbalanced COVID-19 vaccine sentiment classification with synthetic resampling coupled deep adversarial active learning
Two-level quantile regression forests for bias correction in range prediction
Multi-task seizure detection: addressing intra-patient variation in seizure morphologies
Active learning for data streams: a survey
Benchmarking Least Squares Support Vector Machine Classifiers
Efficient benchmarking of algorithm configurators via model-based surrogates
A robust adversarial ensemble with causal (feature interaction) interpretations for image classification
Kappa Updated Ensemble for drifting data stream mining
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors
Extracting certainty from uncertainty: regret bounded by variation in costs
Computational social science and social computing
First order random forests: Learning relational classifiers with complex aggregates
Collaborative topic regression for predicting topic-based social influence
Interpretable domain adaptation via optimization over the Stiefel manifold
Feature space perspectives for learning the kernel
Classification using proximity catch digraphs
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results
Offline reinforcement learning with task hierarchies
Learning deterministic probabilistic automata from a model checking perspective
A Meta-Learning Method to Select the Kernel Width in Support Vector Regression
On The Learnability Of Disjunctive Normal Form Formulas
MultiClust special issue on discovering, summarizing and using multiple clusterings
Pseudo-marginal Bayesian inference for Gaussian process latent variable models
A Tale of Two Classifier Systems
Reconciling privacy and utility: an unscented Kalman filter-based framework for differentially private machine learning
Towards interpretable and robust UAV-based foundation model for endangered species monitoring in complex ecosystems
Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries
Learning to bid in bridge
Rényi divergence in hidden Markov models
Data-aware process discovery for malware detection: an empirical study
Learning to Perceive and Act by Trial and Error
Neural network structure simplification by assessing evolution in node weight magnitude
Offline reinforcement learning for learning to dispatch for job shop scheduling
Principle Components and Importance Ranking of Distributed Anomalies
Relational data factorization
JGPR: a computationally efficient multi-target Gaussian process regression algorithm
Inductive learning of answer set programs for autonomous surgical task planning
Skew Gaussian processes for classification
A decision-theoretic approach for model interpretability in Bayesian framework
Decision Tree Induction Based on Efficient Tree Restructuring
Constraint-based clustering selection
Speeding up algorithm selection using average ranking and active testing by introducing runtime
Hierarchical optimal transport for unsupervised domain adaptation
Revealing vulnerable regions through diverse adversarial examples
Machine learning and games
Evaluating time series forecasting models: an empirical study on performance estimation methods
Finding relational redescriptions
Double random forest
Using Neural Networks to Modularize Software
Relational Instance-Based Learning with Lists and Terms
Robust high dimensional expectation maximization algorithm via trimmed hard thresholding
Evaluating feature attribution methods in the image domain
Ensembles of extremely randomized predictive clustering trees for predicting structured outputs
Mass estimation
QG/GA: a stochastic search for Progol
Queries and Concept Learning
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Pruning during training by network efficacy modeling
On the incompatibility of accuracy and equal opportunity
NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation
Constructing generative logical models for optimisation problems using domain knowledge
Ensemble and continual federated learning for classification tasks
A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles
MASS-CSP: mining with answer set solving for contrast sequential pattern mining
The Effect of Instance-Space Partition on Significance
When Will Machines Learn?
Classification with costly features in hierarchical deep sets
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)
Learning with confidence: training better classifiers from soft labels
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Robust linear classification from limited training data
Policy space identification in configurable environments
A new method of moments for latent variable models
Bridging XAI and spectral analysis to investigate the inductive biases of deep graph networks
Optimal survival trees
Evolving Soccer Keepaway Players Through Task Decomposition
Are LSTMs good few-shot learners?
Elevator Group Control Using Multiple Reinforcement Learning Agents
On-the-fly image-level oversampling for imbalanced datasets of manufacturing defects
PAC-Bayesian Stochastic Model Selection
Using Model Trees for Classification
Transfer learning by mapping and revising boosted relational dependency networks
V-shaped interval insensitive loss for ordinal classification
A Neural Syntactic Language Model
Decision trees for hierarchical multi-label classification
RGCVAE: relational graph conditioned variational autoencoder for molecule design
Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee
Comprehension Grammars Generated from Machine Learning of Natural Languages
PerfoRank: cluster-based performance ranking for improved performance evaluation and estimation in professional cycling
Local2Global: a distributed approach for scaling representation learning on graphs
Spanning attack: reinforce black-box attacks with unlabeled data
An Empirical Comparison of Selection Measures for Decision-Tree Induction
Supervised representation learning for multi-label classification
Introduction to the special issue on COLT 2006
Discovering significant patterns
Fast adaptive algorithms for abrupt change detection
Stochastic Grammatical Inference of Text Database Structure
Nonparametric Time Series Prediction Through Adaptive Model Selection
A co-classification approach to learning from multilingual corpora
Propositionalization and embeddings: two sides of the same coin
Algebraic Reasoning about Reactions: Discovery of Conserved Properties in Particle Physics
BoosTexter: A Boosting-based System for Text Categorization
ILP-assisted de novo drug design
Understanding transfer learning and gradient-based meta-learning techniques
A network-based positive and unlabeled learning approach for fake news detection
Understanding imbalanced data: XAI & interpretable ML framework
An empirical study on impact of label noise on synthetic tabular data generation
Deep metric loss for multimodal learning
Bayesian Landmark Learning for Mobile Robot Localization
LaMMOn: language model combined graph neural network for multi-target multi-camera tracking in online scenarios
HorNets: learning from discrete and continuous signals with routing neural networks
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
Variable selection for both outcomes and predictors: sparse multivariate principal covariates regression
Adaptive Versus Nonadaptive Attribute-Efficient Learning
Approximate structure learning for large Bayesian networks
Multiclass classification with bandit feedback using adaptive regularization
World-class interpretable poker
Sparse non Gaussian component analysis by semidefinite programming
Decision Region Connectivity Analysis: A Method for Analyzing High-Dimensional Classifiers
Active sampling for multiple output identification
A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward: Empirical Results with Yield Management and Convergence Analysis
Moment Kernels for Regular Distributions
Semi-supervised model-based document clustering: A comparative study
On Data Preconditioning for Regularized Loss Minimization
A framework for statistical clustering with constant time approximation algorithms for K -median and K -means clustering
A Principal Components Approach to Combining Regression Estimates
Learning from patches by efficient spectral decomposition of a structured kernel
Correction: Adversarial concept drift detection under poisoning attacks for robust data stream mining
An efficient primal dual prox method for non-smooth optimization
Improved maximum inner product search with better theoretical guarantee using randomized partition trees
The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty
Improved graph-based SFA: information preservation complements the slowness principle
Generalizing Version Spaces
Meta-interpretive learning: application to grammatical inference
Adaptive adapter routing for long-tailed class-incremental learning
Evaluation methods and decision theory for classification of streaming data with temporal dependence
Robust Plackett-Luce model for k-ary crowdsourced preferences
From MNIST to ImageNet and back: benchmarking continual curriculum learning
An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection
An Efficient Method To Estimate Bagging's Generalization Error
Learning with mitigating random consistency from the accuracy measure
Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm
Federated causal inference from observational data
Learning long-term chess strategies from databases
Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system
Bayesian multi-tensor factorization
TD(lambda) Converges with Probability 1
A flexible class of dependence-aware multi-label loss functions
Efficient inference and learning in a large knowledge base - Reasoning with extracted information using a locally groundable first-order probabilistic logic
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
A cooperative coevolutionary algorithm for instance selection for instance-based learning
Leave-one-out cross-validation is risk consistent for lasso
Maximum margin partial label learning
An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms
Persistence B-spline grids: stable vector representation of persistence diagrams based on data fitting
Markov chain importance sampling for minibatches
Learning the set covering machine by bound minimization and margin-sparsity trade-off
PAC-learning with approximate predictors
Group feature selection using non-class data
Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
Robust regression using biased objectives
Permutation-invariant linear classifiers
Local procrustes for manifold embedding: a measure of embedding quality and embedding algorithms
The forget-set identification problem
On the effects of biased quantum random numbers on the initialization of artificial neural networks
Extending Domain Theories: Two Case Studies in Student Modeling
The generalization performance of ERM algorithm with strongly mixing observations
Cluster weighted models for functional data
Exploiting counter-examples for active learning with partial labels
Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series
Efficient cross-validation for kernelized least-squares regression with sparse basis expansions
Bayesian optimization with partially specified queries
An algebraic characterization of the optimum of regularized kernel methods
GENs: generative encoding networks
Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers
Inductive biases for zero-shot systematic generalization in language-informed reinforcement learning
Ranking-preserved generative label enhancement
New algorithms for budgeted learning
A Universal Method of Scientific Inquiry
Multiple clusterings of heterogeneous information networks
Chemical Discovery as Belief Revision
Online binary classification from similar and dissimilar data
Feature extractor stacking for cross-domain few-shot learning
Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning
On the quest for optimal rule learning heuristics
Semantic-aware contrastive learning via multi-prompt alignment
Can Machine Learning Offer Anything to Expert Systems?
Nested aggregation of experts using inducing points for approximated Gaussian process regression
Checkpoint evolution for volatile correlation computing
Automotive fault nowcasting with machine learning and natural language processing
Rényi divergence minimization based co-regularized multiview clustering
A family of admissible heuristics for A* to perform inference in probabilistic classifier chains
Learning deterministic context free grammars: The Omphalos competition
Beyond confusion matrix: learning from multiple annotators with awareness of instance features
L2XGNN: learning to explain graph neural networks
Multi-view kernel construction
Bibliographic analysis on research publications using authors, categorical labels and the citation network
Sequential approaches for learning datum-wise sparse representations
2D compressed learning: support matrix machine with bilinear random projections
Correction to: A neural meta-model for predicting winter wheat crop yield
Comments on "Co-Evolution in the Successful Learning of Backgammon Strategy"
Attentional multilabel learning over graphs: a message passing approach
A comparison of statistical relational learning and graph neural networks for aggregate graph queries
Learning Logical Definitions from Relations
Multi-objective multi-armed bandit with lexicographically ordered and satisficing objectives
Exactly Learning Automata of Small Cover Time
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels
Bayesian instance selection for the nearest neighbor rule
Optimal classification trees
Local AdaGrad-type algorithm for stochastic convex-concave optimization
Guest editors' introduction: special issue on Inductive Logic Programming (ILP 2019)
X-Detect: explainable adversarial patch detection for object detectors in retail
Guest editors' introduction: special issue of selected papers from ECML PKDD 2011
kProbLog: an algebraic Prolog for machine learning
Libsignal: an open library for traffic signal control
SafeGen: safeguarding privacy and fairness through a genetic method
Assessing machine learning and data imputation approaches to handle the issue of data sparsity in sports forecasting
Composition of relational features with an application to explaining black-box predictors
Graph-based semi-supervised learning via improving the quality of the graph dynamically
ZipLine: an optimized algorithm for the elastic bulk synchronous parallel model
Feature Weighting in k -Means Clustering
A viable framework for semi-supervised learning on realistic dataset
k-Optimal: a novel approximate inference algorithm for ProbLog
Increasing coverage to improve detection of network and host anomalies
Machine Discovery of Protein Motifs
Solving imbalanced learning with outlier detection and features reduction
Adversarial learning for counterfactual fairness
Optimizing non-decomposable measures with deep networks
Efficient learning with robust gradient descent
Language Simplification through Error-Correcting and Grammatical Inference Techniques
Approximate Match of Rules Using Backpropagation Neural Networks
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
Provable training set debugging for linear regression
Convex programming based spectral clustering
An Introduction to MCMC for Machine Learning
Dynamic Parameter Encoding for Genetic Algorithms
HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning
Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning
Probabilistic instance dependent label refinement for noisy label learning
An Analysis of the WITT Algorithm
A comparison of collapsed Bayesian methods for probabilistic finite automata
On PAC-Bayesian bounds for random forests
Characteristic Sets for Polynomial Grammatical Inference
Single-Class Classification with Mapping Convergence
Large scale multi-label learning using Gaussian processes
Central Clustering of Attributed Graphs
PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples
Tree Induction for Probability-Based Ranking
Pdarts: projected differentiable architecture search for seismic inversion
Supersparse linear integer models for optimized medical scoring systems
The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
A comparison of pruning criteria for probability trees
GS 2 P: a generative pre-trained learning to rank model with over-parameterization for web-scale search
Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions
Stochastic feature mapping for PAC-Bayes classification
PANACEA: a neural model ensemble for cyber-threat detection
Learning biologically-interpretable latent representations for gene expression data
On label dependence and loss minimization in multi-label classification
Aggregating Algorithm for prediction of packs
Consensus-relevance kNN and covariate shift mitigation
Combination of inductive mondrian conformal predictors
Weakly supervised change detection using guided anisotropic diffusion
Metrics and methods for robustness evaluation of neural networks with generative models