yusuke1997/dblp-id-title huggingface dataset:
Fast spectral analysis for approximate nearest neighbor search
Learning to Decode Cognitive States from Brain Images
Explicit Explore, Exploit, or Escape (E 4 ): near-optimal safety-constrained reinforcement learning in polynomial time
Targeted and contextual redescription set exploration
Learning Controllers for Industrial Robots
Learning logic programs by explaining their failures
Classification by Feature Partitioning
Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency
A critical analysis of variants of the AUC
Sampled Gromov Wasserstein
Multicategory Proximal Support Vector Machine Classifiers
Online probabilistic theory revision from examples with ProPPR
Regret to the best vs. regret to the average
Circular-symmetric correlation layer
Guest editorial: special issue on structured prediction
Sequence labeling with multiple annotators
Automatic discovery of interpretable planning strategies
Upper bound for variational free energy of Bayesian networks
Alignment based kernel learning with a continuous set of base kernels
Committee polyhedral separability: complexity and polynomial approximation
Adaptive differentiable trees for transparent learning on data streams
CPAT: cross-patch aggregated transformer for time series forecasting
Guest editorial: model selection and optimization in machine learning
The online performance estimation framework: heterogeneous ensemble learning for data streams
A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
Improved Estimates for the Accuracy of Small Disjuncts
First Order Regression
Extracting Hidden Context
Combined SVM-Based Feature Selection and Classification
Lifted discriminative learning of probabilistic logic programs
XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance
Feature-Based Methods for Large Scale Dynamic Programming
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces
Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study
A theory of learning from different domains
Glacier: guided locally constrained counterfactual explanations for time series classification
The backbone method for ultra-high dimensional sparse machine learning
Active learning algorithm through the lens of rejection arguments
Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data
Towards adaptive unknown authentication for universal domain adaptation by classifier paradox
Accelerated algorithms for convex and non-convex optimization on manifolds
Explainable dating of greek papyri images
Special issue for ECML PKDD 2010: Guest editors' introduction
Fast generalization rates for distance metric learning
Water pipe condition assessment: a hierarchical beta process approach for sparse incident data
Output Fisher embedding regression
A moment-matching metric for latent variable generative models
The Machine Learning journal: 250 issues and counting
Correction to: Nettop: A lightweight-network of orthogonal-plane features for image recognition
Dynamic customer segmentation via hierarchical fragmentation-coagulation processes
Artificial intelligence for laryngoscopy in vocal fold diseases: a review of dataset, technology, and ethics
Boosting Methods for Regression
Information-theoretic regularization for learning global features by sequential VAE
Shift of pairwise similarities for data clustering
Cross-model consensus of explanations and beyond for image classification models: an empirical study
Active evaluation of ranking functions based on graded relevance
Optimal dyadic decision trees
Learning semantic representations of objects and their parts
Hierarchical constraints - Providing structural bias for hierarchical clustering
Naive Bayesian Classification of Structured Data
MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm
ShuttleFlow: learning the distribution of subsequent badminton shots using normalizing flows
Efficient projection-free online convex optimization using stochastic gradients
Improving fraud detection via imbalanced graph structure learning
Bayesian Network Classifiers
CMD: controllable matrix decomposition with global optimization for deep neural network compression
Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation
Diametrical Risk Minimization: theory and computations
Discovery by Minimal Length Encoding: A Case Study in Molecular Evolution
Bias in Information-Based Measures in Decision Tree Induction
Variational learning from implicit bandit feedback
A taxonomy for similarity metrics between Markov decision processes
Bayesian mixture variational autoencoders for multi-modal learning
The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives
Learning with risks based on M-location
Introduction: special issue of selected papers of ACML 2012
Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers
Nrat: towards adversarial training with inherent label noise
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
High-dimensional Bayesian optimization using low-dimensional feature spaces
An investigation of visual foundation models robustness
Classification using Hierarchical Naïve Bayes models
Using random forests to diagnose aviation turbulence
Weighted neural tangent kernel: a generalized and improved network-induced kernel
Generalization of Clauses Relative to a Theory
Regret bounded by gradual variation for online convex optimization
Searching for Representations to Improve Protein Sequence Fold-Class Prediction
Sequential anomalies: a study in the Railway Industry
N-ary decomposition for multi-class classification
A review on instance ranking problems in statistical learning
Learning finite-state models for machine translation
Ensemble clustering using semidefinite programming with applications
Structured learning with constrained conditional models
Relating instance hardness to classification performance in a dataset: a visual approach
Learning Regular Languages from Simple Positive Examples
Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
Discovering a taste for the unusual: exceptional models for preference mining
Training parsers by inverse reinforcement learning
FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning
The Strength of Weak Learnability
An Analytic Center Machine
Proximal average approximated incremental gradient descent for composite penalty regularized empirical risk minimization
Adaptive trajectory analysis of replicator dynamics for data clustering
Incorporating prior knowledge in support vector regression
Support Vector Learning for Semantic Argument Classification
Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion
The risk of trivial solutions in bipartite top ranking
Gender disentangled representation learning in neural rankers
Stochastic Finite Learning of the Pattern Languages
From machine learning to machine reasoning - An essay
Classifying under computational resource constraints: anytime classification using probabilistic estimators
Collaborative topic regression for online recommender systems: an online and Bayesian approach
A multivariate Bayesian scan statistic for early event detection and characterization
Unsupervised ensemble minority clustering
Probabilistic consensus clustering using evidence accumulation
Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning
Latent Classification Models
ART: A Hybrid Classification Model
An adaptive heuristic for feature selection based on complementarity
HE-GAD: a behavior-enhanced contrastive learning framework for graph anomaly detection
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
Style spectroscope: improve interpretability and controllability through Fourier analysis
An Integrated Framework for Empirical Discovery
Emotion in reinforcement learning agents and robots: a survey
Generalized Twin Gaussian processes using Sharma-Mittal divergence
Federated learning with superquantile aggregation for heterogeneous data
Beyond graph neural networks with lifted relational neural networks
Choosing Multiple Parameters for Support Vector Machines
Representing Probabilistic Rules with Networks of Gaussian Basis Functions
FLight: optimization algorithm for traffic lights based on short-term traffic state forecast
A note on Platt's probabilistic outputs for support vector machines
Stacked Regressions
Local equivalences of distances between clusterings - a geometric perspective
Improving probabilistic inference in graphical models with determinism and cycles
Compressing probabilistic Prolog programs
Holistic deep learning
Technical Note Q-Learning
OT-net: a reusable neural optimal transport solver
Adaptive random forests for evolving data stream classification
Anytime classification for a pool of instances
Switching: understanding the class-reversed sampling in tail sample memorization
Automated Refinement of First-Order Horn-Clause Domain Theories
DISTFormer: Enhance 3D Human Pose Estimation via Dual Inverse-Order Spatial-Temporal Transformer
A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning
Towards efficient pareto-optimal utility-fairness between groups in repeated rankings
Incremental Induction of Decision Trees
Autoreplicative random forests with applications to missing value imputation
Programming by Demonstration Using Version Space Algebra
Single image inpainting and super-resolution with simultaneous uncertainty guarantees by universal reproducing kernels
An efficient algorithm for learning to rank from preference graphs
Ensembles for multi-target regression with random output selections
Speeding-up one-versus-all training for extreme classification via mean-separating initialization
Adapting performance metrics for ordinal classification to interval scale: length matters
A calibration test for evaluating set-based epistemic uncertainty representations
Maintaining AUC and H-measure over time
PCE-GNN: a node feature-enhanced graph neural network with pre-clustering strategy
Competing with wild prediction rules
Efficient feature selection using shrinkage estimators
Smoothing policies and safe policy gradients
Testing conditional independence in supervised learning algorithms
Analyzing and repairing concept drift adaptation in data stream classification
Naive Bayes for Regression (Technical Note)
CLAIRE: clustering evaluation based on item response theory and model agreement
Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function
Text Classification from Labeled and Unlabeled Documents using EM
Achieving adversarial robustness via sparsity
Semi-supervised learning with density-ratio estimation
Stochastic relational processes: Efficient inference and applications
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces
Improve global generalization for personalized federated learning within a Stackelberg game
A new formulation of Lipschitz constrained with functional gradient learning for GANs
Technical Note: Some Properties of Splitting Criteria
Weighting non-IID batches for out-of-distribution detection
Transfer learning with pre-trained conditional generative models
Machine Learning for Information Extraction in Informal Domains
Composite score for anomaly detection in imbalanced real-world industrial dataset
A study of BERT for context-aware neural machine translation
Personalization for web-based services using offline reinforcement learning
An instance-dependent simulation framework for learning with label noise
A survey on semi-supervised learning
Subjective interestingness of subgraph patterns
SPEED: secure, PrivatE, and efficient deep learning
Maximum causal entropy inverse constrained reinforcement learning
ICM ensemble with novel betting functions for concept drift
Minimal learning machine for multi-label learning
Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach
Adaptive game AI with dynamic scripting
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds
Robust federated learning under statistical heterogeneity via hessian-weighted aggregation
Lipschitzness is all you need to tame off-policy generative adversarial imitation learning
TSFuse: automated feature construction for multiple time series data
Novel high intrinsic dimensionality estimators
Markov Processes on Curves
Representation learning for clustering via building consensus
Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time
Annealing stochastic approximation Monte Carlo algorithm for neural network training
Schema-tune: noise-driven bias mitigation in transformer-based language models
Confirmation-Guided Discovery of First-Order Rules with Tertius
Flexible latent variable models for multi-task learning
Learning de-biased regression trees and forests from complex samples
Multi-stage classifier design
Class-prior estimation for learning from positive and unlabeled data
Manifestations of user personality in website choice and behaviour on online social networks
Acquisition of Children's Addition Strategies: A Model of Impasse-Free, Knowledge-Level Learning
Optimizing Epochal Evolutionary Search: Population-Size Dependent Theory
Fast rates by transferring from auxiliary hypotheses
On the properties of von Neumann kernels for link analysis
Introduction Structured Connectionist Systems
Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains
Temporal ensemble of multiple patterns' instances for continuous prediction of events
Improving kernel online learning with a snapshot memory
Bounding the Rademacher complexity of Fourier neural operators
Explanation-Based Learning for Diagnosis
Dense subgraphs induced by edge labels
Robust estimation in regression and classification methods for large dimensional data
Reinforced logical reasoning over KGs for interpretable recommendation system
Unifying Instance-Based and Rule-Based Induction
Correction to: Deep negative correlation classification
Construction of the Kolmogorov-Arnold networks using the Newton-Kaczmarz method
Dimension reduction and its application to model-based exploration in continuous spaces
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Robust generative adversarial network
Large margin vs. large volume in transductive learning
On structured output training: hard cases and an efficient alternative
Graph similarity learning for change-point detection in dynamic networks
Evaluating soccer match prediction models: a deep learning approach and feature optimization for gradient-boosted trees
Predicting Nearly As Well As the Best Pruning of a Decision Tree
FairMOE: counterfactually-fair mixture of experts with levels of interpretability
Genetic Algorithms in Noisy Environments
The voice of optimization
Compression and restoration: exploring elasticity in continual test-time adaptation
Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes
Optimal Ordered Problem Solver
Active Sampling for Class Probability Estimation and Ranking
Coupling matrix manifolds assisted optimization for optimal transport problems
Efficient zeroth-order proximal stochastic method for nonconvex nonsmooth black-box problems
Differential privacy based on importance weighting
LEAP: Linear equations for classifier accuracy prediction under prior probability shift
Correction to: Extracting automata from recurrent neural networks using queries and counterexamples (extended version)
Large Scale Kernel Regression via Linear Programming
Understanding generalization error of SGD in nonconvex optimization
Efficient SVDD sampling with approximation guarantees for the decision boundary
External Control in Markovian Genetic Regulatory Networks
Robustness verification of ReLU networks via quadratic programming
Stronger data poisoning attacks break data sanitization defenses
Entropic risk minimization for nonparametric estimation of mixing distributions
Balanced Cooperative Modeling
Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination
A Note on Learning from Multiple-Instance Examples
InfoCEVAE: treatment effect estimation with hidden confounding variables matching
New Theoretical Directions in Machine Learning
Learning from binary labels with instance-dependent noise
Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
Distributional learning of parallel multiple context-free grammars
Improving interpretability via regularization of neural activation sensitivity
Wasserstein-based fairness interpretability framework for machine learning models
Dynamic datasets and market environments for financial reinforcement learning
Probabilistic scoring lists for interpretable machine learning
Parameter Estimation in Stochastic Logic Programs
Learning (k,l)-context-sensitive probabilistic grammars with nonparametric Bayesian approach
A decomposition of the outlier detection problem into a set of supervised learning problems
Efficient SVM Regression Training with SMO
Multilabel classification with meta-level features in a learning-to-rank framework
Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks
Generalized ordering-search for learning directed probabilistic logical models
Efficient mining for structurally diverse subgraph patterns in large molecular databases
Incremental exemplar learning schemes for classification on embedded devices
Criteria for Polynomial-Time (Conceptual) Clustering
Random drift particle swarm optimization algorithm: convergence analysis and parameter selection
GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data
A suffix tree approach to anti-spam email filtering
Structural causal models reveal confounder bias in linear program modelling
Indexing and Elaboration and Refinement: Incremental Learning of Explanatory Cases
Breaking domain barriers: mixture of experts for cross-domain fake news detection
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety
The flowing nature matters: feature learning from the control flow graph of source code for bug localization
Geometry-aware principal component analysis for symmetric positive definite matrices
IA-NGM: A bidirectional learning method for neural graph matching with feature fusion
Online optimization for max-norm regularization
Heterogeneous multi-task feature learning with mixed ℓ 2,1 regularization
Rigel: An Inductive Learning System
Filtering-Ranking Perceptron Learning for Partial Parsing
An instance level analysis of data complexity
Estimating variable structure and dependence in multitask learning via gradients
Stream-based active learning for sliding windows under the influence of verification latency
On the sample complexity of actor-critic method for reinforcement learning with function approximation
Phase Transitions in Relational Learning
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Recursive Automatic Bias Selection for Classifier Construction
Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models
The max-min hill-climbing Bayesian network structure learning algorithm
Pool-based active learning in approximate linear regression
Cherry on the cake: fairness is NOT an optimization problem
Introduction to the special issue on multi-relational data mining and statistical relational learning
Complexity Results on Learning by Neural Nets
Scalable Bayesian preference learning for crowds
Conformal predictions for probabilistically robust scalable machine learning classification
Guest editorial: special issue on machine learning in soccer
A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing
Acquiring Recursive and Iterative Concepts with Explanation-Based Learning
Large scale multi-output multi-class classification using Gaussian processes
Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Bandit algorithms to personalize educational chatbots
A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game
A computational approach to nonparametric regression: bootstrapping CMARS method
Hybrid approaches to optimization and machine learning methods: a systematic literature review
On cognitive preferences and the plausibility of rule-based models
Worst-Case Bounds for the Logarithmic Loss of Predictors
A Learning Generalization Bound with an Application to Sparse-Representation Classifiers
Segmentation and feature extraction-based classification of pavement damages using hybrid computer vision and machine learning approaches
Day trading profit maximization with multi-task learning and technical analysis
Using causal discovery for feature selection in multivariate numerical time series
On reoptimizing multi-class classifiers
Time series representations classroom (TSRC): a teacher-student-based framework for interpretability-enhanced unsupervised time series representation learning
Editorial: The Terminology of Machine Learning
Clustering Large Graphs via the Singular Value Decomposition
Generalized median of means principle for Bayesian inference
Learning Conjunctions of Horn Clauses
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
FAC-fed: Federated adaptation for fairness and concept drift aware stream classification
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition
Learning from natural instructions
A Multistrategy Approach to Relational Knowledge Discovery in Databases
Optimal transport for conditional domain matching and label shift
Explaining short text classification with diverse synthetic exemplars and counter-exemplars
Evaluation of geographical distortions in language models
Active model selection: A variance minimization approach
A cross-domain user association scheme based on graph attention networks with trajectory embedding
Smooth relevance vector machine: a smoothness prior extension of the RVM
Minimum Generalization Via Reflection: A Fast Linear Threshold Learner
Concentration properties of fractional posterior in 1-bit matrix completion
ROC convex hull and nonparametric maximum likelihood estimation
Toward practical human-interpretable explanations
Common abductive explanations in first order logic
On some transformations of high dimension, low sample size data for nearest neighbor classification
Density estimation with stagewise optimization of the empirical risk
MetaML: a multi-label meta-learning approach for pipeline recommendation
Guest Editorial: Special issue on Discovery Science
Continual variational dropout: a view of auxiliary local variables in continual learning
Towards accurate knowledge transfer via target-awareness representation disentanglement
Boosting Poisson regression models with telematics car driving data
A semantic matching energy function for learning with multi-relational data - Application to word-sense disambiguation
Learning data discretization via convex optimization
Tackle balancing constraints in semi-supervised ordinal regression
Learning in Autonomous Robots - Foreword