yusuke1997/dblp-id-title huggingface dataset:
Using Background Knowledge to Build Multistrategy Learners
Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach
Analyzing business process anomalies using autoencoders
FairSwiRL: fair semi-supervised classification with representation learning
LLiMe: enhancing text classifier explanations with large language models
POMDP inference and robust solution via deep reinforcement learning: an application to railway optimal maintenance
Learning domain invariant representations of heterogeneous image data
Rule learning by modularity
Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models
Group online adaptive learning
Parallel ILP for distributed-memory architectures
Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track
The impact of data distribution on Q-learning with function approximation
Unsupervised representation learning with Minimax distance measures
Creating non-minimal triangulations for use in inference in mixed stochastic/deterministic graphical models
Towards interpreting deep neural networks via layer behavior understanding
DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy
Addressing task conflicts in LLMs multi-task fine-tuning with task-specific subnetwork refinement
Coping with Uncertainty in Map Learning
Asymptotic analysis of temporal-difference learning algorithms with constant step-sizes
Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning
Optimal learning with Bernstein online aggregation
Uncertainty Quantification in Pairwise Difference Learning for Classification
Distributed Representations, Simple Recurrent Networks, and Grammatical Structure
Toward a Model of Intelligence as an Economy of Agents
Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the-Art
Discordance minimization-based imputation algorithms for missing values in rating data
On Exact Learning of Unordered Tree Patterns
Automated Support for Building and Extending Expert Models
A Machine Learning Approach to POS Tagging
On the use of ROC analysis for the optimization of abstaining classifiers
The Knowledge Level Reinterpreted: Modeling How Systems Interact
Interactive sequential generative models for team sports
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)
Inductive process modeling
Improving Markov Chain Monte Carlo Model Search for Data Mining
Learning undirected graphical models using persistent sequential Monte Carlo
Is Combining Classifiers with Stacking Better than Selecting the Best One?
An Exact Probability Metric for Decision Tree Splitting and Stopping
Prequential and Cross-Validated Regression Estimation
Alignclip: navigating the misalignments for robust vision-language generalization
Model selection in reconciling hierarchical time series
Operator-valued kernel-based vector autoregressive models for network inference
Enriched spatial comparison of clusterings through discovery of deviating subspaces
Predicting Survival Outcomes in the Presence of Unlabeled Data
Experience Selection and Problem Choice in an Exploratory Learning System
Hierarchical Dirichlet scaling process
Feature ranking for multi-target regression
Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification
Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries
Towards efficient AutoML: a pipeline synthesis approach leveraging pre-trained transformers for multimodal data
The Robustness of the p-Norm Algorithms
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio
On the eigenvectors of p -Laplacian
Constructing effective personalized policies using counterfactual inference from biased data sets with many features
Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates
Multiscale principle of relevant information for hyperspectral image classification
Finding a short and accurate decision rule in disjunctive normal form by exhaustive search
Statistical comparison of classifiers through Bayesian hierarchical modelling
Semi-Supervised Learning on Riemannian Manifolds
Trimming stability selection increases variable selection robustness
Subspace Adaptation Prior for Few-Shot Learning
Multi-way set enumeration in weight tensors
Spike2CGR: an efficient method for spike sequence classification using chaos game representation
Concept Learning and Feature Selection Based on Square-Error Clustering
Learning with tensors: a framework based on convex optimization and spectral regularization
Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference
Editorial: Kernel Methods: Current Research and Future Directions
Training a reciprocal-sigmoid classifier by feature scaling-space
Active deep Q-learning with demonstration
Symbolic DNN-Tuner
Learning graphical models for relational data via lattice search
Learning large-alphabet and analog circuits with value injection queries
Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening
Efficient regularized least-squares algorithms for conditional ranking on relational data
Distribution aligned semantics adaption for lifelong person re-identification
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems
The teaching size: computable teachers and learners for universal languages
Introduction to the special issue on discovery science
Efficient Algorithms for the Inference of Minimum Size DFAs
Logarithmic regret algorithms for online convex optimization
Crowdsourcing with unsure option
Online aggregation of unbounded losses using shifting experts with confidence
Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results
Stagewise learning for noisy k-ary preferences
DAFS: a domain aware few shot generative model for event detection
SPA: A poisoning attack framework for graph neural networks through searching and pairing
Predicting the Future of Discrete Sequences from Fractal Representations of the Past
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Online multi-label dependency topic models for text classification
Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning
Accelerating EM for Large Databases
Self-organizing maps with adaptive distances for multiple dissimilarity matrices
A case based approach to expressivity-aware tempo transformation
Convergence of Adam for non-convex objectives: relaxed hyperparameters and non-ergodic case
Similarity-Based Models of Word Cooccurrence Probabilities
Children, Adults, and Machines as Discovery Systems
Moreau-Yoshida variational transport: a general framework for solving regularized distributional optimization problems
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Knowledge Acquisition via Incremental Conceptual Clustering
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling
Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move
An accelerated variance reducing stochastic method with Douglas-Rachford splitting
Towards a foundation large events model for soccer
VEST: automatic feature engineering for forecasting
Incremental Abductive EBL
A Critical Look at Experimental Evaluations of EBL
Coding Decision Trees
Multi-Class Learning by Smoothed Boosting
A "Microscopic" Study of Minimum Entropy Search in Learning Decomposable Markov Networks
Root-finding approaches for computing conformal prediction set
Online Multiclass Learning with k -Way Limited Feedback and an Application to Utterance Classification
Relation Between Permutation-Test P Values and Classifier Error Estimates
Joint optimization of an autoencoder for clustering and embedding
Graph kernels and Gaussian processes for relational reinforcement learning
Recursive tree grammar autoencoders
Temporal pattern attention for multivariate time series forecasting
An encoding approach for stable change point detection
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data
Scaling Up Inductive Learning with Massive Parallelism
Conditional validity of inductive conformal predictors
Fairness seen as global sensitivity analysis
SVRG meets AdaGrad: painless variance reduction
BoostingTree: parallel selection of weak learners in boosting, with application to ranking
Cascade Generalization
Semantic-enhanced graph neural networks with global context representation
An Introduction to Variational Methods for Graphical Models
Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated data
Asymptotically optimal algorithms for budgeted multiple play bandits
Coefficient tree regression: fast, accurate and interpretable predictive modeling
Leveraging differentiable NAS and abstract genetic algorithms for optimizing on-mobile VSR performance
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Textmatcher: cross-attentional neural network to compare image and text
Sparse classification: a scalable discrete optimization perspective
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms
Variance-constrained actor-critic algorithms for discounted and average reward MDPs
Explicit Representation of Concept Negation
Probabilistic frequent subtrees for efficient graph classification and retrieval
Foreword: special issue for the journal track of the 12th Asian conference on machine learning (ACML 2020)
Multi-modal co-learning for Earth observation: enhancing single-modality models via modality collaboration
A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships
Efficient and provable online reduced rank regression via online gradient descent
Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees
Unconfused ultraconservative multiclass algorithms
Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion
Clausal Discovery
A generalized Weisfeiler-Lehman graph kernel
Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts
An empirical study of on-line models for relational data streams
High-probability minimax probability machines
Navigating explanatory multiverse through counterfactual path geometry
One transformer for all time series: representing and training with time-dependent heterogeneous tabular data
Fast learning of relational dependency networks
Improving graph neural networks through feature importance learning
Identifying and tracking topic-level influencers in the microblog streams
Tracking Context Changes through Meta-Learning
On metafeatures' ability of implicit concept identification
FFNSL: Feed-Forward Neural-Symbolic Learner
Lessons on off-policy methods from a notification component of a chatbot
Deep doubly robust outcome weighted learning
Learning from evolving video streams in a multi-camera scenario
Learning to rank aspects and opinions for comparative explanations
Benchmarking adversarial robustness to bias elicitation in large language models: scalable automated assessment with LLM-as-a-judge
XRules: An effective algorithm for structural classification of XML data
Sparse canonical correlation analysis
Model selection for primal SVM
Physics-informed variational autoencoders for improved robustness to environmental factors of variation
Conditional t-SNE: more informative t-SNE embeddings
Inverse subsumption for complete explanatory induction
Reduction Techniques for Instance-Based Learning Algorithms
Learning Information Extraction Rules for Semi-Structured and Free Text
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Sparse and low-rank matrix regularization for learning time-varying Markov networks
Fast parameterless prototype-based co-clustering
Information retrieval approach to meta-visualization
Efficiently mining δ -tolerance closed frequent subgraphs
Progressive semantic learning for unsupervised skeleton-based action recognition
DIMBA: discretely masked black-box attack in single object tracking
Perfect counterfactuals in imperfect worlds: modelling noisy implementation of actions in sequential algorithmic recourse
Layered critical values: a powerful direct-adjustment approach to discovering significant patterns
Sum-product graphical models
The Importance of Attribute Selection Measures in Decision Tree Induction
On the Existence of Linear Weak Learners and Applications to Boosting
A Nearest Hyperrectangle Learning Method
Learning representations from dendrograms
Bayesian generalized probability calculus for density matrices
Enhancing workforce attendance evaluation in vocational schools: a decision support framework powered by explainable machine learning
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment
Diverse and consistent multi-view networks for semi-supervised regression
Unsupervised discretization by two-dimensional MDL-based histogram
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
Guest Editorial: Special issue on robust machine learning
Masked autoencoder for multiagent trajectories
Adaptive optimization for prediction with missing data
Pairwise meta-rules for better meta-learning-based algorithm ranking
ScoreCL: augmentation-adaptive contrastive learning via score-matching function
QuicK-means: accelerating inference for K-means by learning fast transforms
Verification on out-of-distribution detectors under natural perturbations
Robust supervised topic models under label noise
Policy search for motor primitives in robotics
The Hierarchical Hidden Markov Model: Analysis and Applications
Stateless neural meta-learning using second-order gradients
Self-Organizing Latent Lattice Models for Temporal Gene Expression Profiling
Detecting concept change in dynamic data streams - A sequential approach based on reservoir sampling
Partially trained graph convolutional networks resist oversmoothing
Margin distribution and structural diversity guided ensemble pruning
Multi-label optimal margin distribution machine
Region-aware Minimal Counterfactual Rules for Model-agnostic Explainable Classification
Convex relaxations of penalties for sparse correlated variables with bounded total variation
Iterative learning from texts and counterexamples using additional information
Inductive logic programming for gene regulation prediction
Correction to: Adaptive random forests for evolving data stream classification
Forecasting electricity consumption by aggregating specialized experts - A review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions
Continuous-Action Q-Learning
Raising the numbers: multi-generation adversarial attack and frequency-based defense for heightened NLP security
Parametric non-parallel support vector machines for pattern classification
Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation
Learning deep kernels in the space of dot product polynomials
A Simple Decomposition Method for Support Vector Machines
TransFed: cross-domain feature alignment for semi-supervised federated transfer learning
LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanations
A geometric view of conjugate priors
Scaling up stochastic gradient descent for non-convex optimisation
Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning
Global optimization of objective functions represented by ReLU networks
Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
Margin-based first-order rule learning
Supporting Start-to-Finish Development of Knowledge Bases
An effective negative sampling approach for contrastive learning of sentence embedding
Learning to Play Chess Using Temporal Differences
Regularisation of neural networks by enforcing Lipschitz continuity
Correction to: Efficient feature selection using shrinkage estimators
Homotopy continuation approaches for robust SV classification and regression
Non-homogeneous dynamic Bayesian networks for continuous data
Using Genetic Algorithms for Concept Learning
Bootstrap learning for accurate onset detection
Relational retrieval using a combination of path-constrained random walks
AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
Lazy Learning of Bayesian Rules
Temporal-difference search in computer Go
Information-Based Evaluation Criterion for Classifier's Performance
Analysis of network traffic features for anomaly detection
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification
An analysis of diversity measures
Efficiently learning the preferences of people
Editorial: Inductive Logic Programming is Coming of Age
Exploiting causality in gene network reconstruction based on graph embedding
Learning to Predict by the Methods of Temporal Differences
Theory Revision with Queries: DNF Formulas
Self-Improving Reactive Agents Based On Reinforcement Learning, Planning and Teaching
Arbitrage of forecasting experts
A generalized online mirror descent with applications to classification and regression
Classifier chains for multi-label classification
An accurate, scalable and verifiable protocol for federated differentially private averaging
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms
Symbolic and Neural Learning Algorithms: An Experimental Comparison
A deep reinforcement learning framework for continuous intraday market bidding
Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming
Bayesian optimistic Kullback-Leibler exploration
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned
Model-based trajectory stitching for improved behavioural cloning and its applications
Finite-time Analysis of the Multiarmed Bandit Problem
An efficient Bayes error rate estimation method
Maximizing Agreements with One-Sided Error with Applications to Heuristic Learning
Statistical Models for Text Segmentation
Extracting Refined Rules from Knowledge-Based Neural Networks
Metric-Based Methods for Adaptive Model Selection and Regularization
Guest Editors' Introduction: On Applied Research in Machine Learning
Graph regularization methods for Web spam detection
Predicting Protein Secondary Structure Using Stochastic Tree Grammars
Unconditional lower bounds for learning intersections of halfspaces
On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata
Reinforcement learning for robotic manipulation using simulated locomotion demonstrations
Local contrast as an effective means to robust clustering against varying densities
Guest Editorial: Learning from multiple sources
Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis
Concept Formation During Interactive Theory Revision
A contrastive neural disentanglement approach for query performance prediction
Clustering Time Series with Clipped Data
Guest Editors' introduction - Special issue on inductive logic programming (ILP 2010)
Asymptotic analysis of estimators on multi-label data
Visualizing and understanding Sum-Product Networks
Unsupervised feature selection with ensemble learning
Reflections on reciprocity in research
Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective
Automatic face recognition with well-calibrated confidence measures
Information bottleneck and selective noise supervision for zero-shot learning
Classification-based melody transcription
An experimental test of Occam's razor in classification
Lifted generative learning of Markov logic networks
On the infeasibility of modeling polymorphic shellcode - Re-thinking the role of learning in intrusion detection systems
A non-negative representation learning algorithm for selecting neighbors
A New Conceptual Clustering Framework
Describing group evolution in temporal data using multi-faceted events
Introduction to the special issue on learning semantics
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders
Explaining mixture models through semantic pattern mining and banded matrix visualization
1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation
Anomaly detection with inexact labels
Design Methods for Scientific Hypothesis Formation and Their Application to Molecular Biology
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine learning in adversarial environments
Scrutinizing XAI using linear ground-truth data with suppressor variables
Multimodal deep learning for cetacean distribution modeling of fin whales (Balaenoptera physalus) in the western Mediterranean Sea
Context-based unsupervised ensemble learning and feature ranking
Mining exceptional social behavior on attributed interaction networks
Correction to: Segmentation and feature extraction-based classification of pavement damages using hybrid computer vision and machine learning approaches
Machine Learning and Discovery
Learning Syntax by Automata Induction
LoRAS: an oversampling approach for imbalanced datasets
A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points
Robust reductions from ranking to classification
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms
Nearest neighbors distance ratio open-set classifier
Explaining Random Forest and XGBoost with Shallow Decision Trees by Co-clustering Feature Importance
CSM: A Computational Model of Cumulative Learning
Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations
Discovery as Autonomous Learning from the Environment
The security of machine learning
Density estimation with minimization of U-divergence
Gradient-based boosting for statistical relational learning: The relational dependency network case
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data
Inductive transfer with context-sensitive neural networks
Applied machine learning to the determination of biochar hydrogen sulfide adsorption capacity
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing
Compressed sensing: a discrete optimization approach
A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions
Model selection by bootstrap penalization for classification
Good arm identification via bandit feedback
An alternative view of variational Bayes and asymptotic approximations of free energy
Toward an Ideal Trainer
Learning perceptually grounded word meanings from unaligned parallel data
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders
Source-free domain adaptation requires penalized diversity
Natural language inference model for customer advocacy detection in online customer engagement