yusuke1997/dblp-id-title huggingface dataset:
Splitting stump forests: tree ensemble compression for edge devices (extended version)
LARS: A learning algorithm for rewriting systems
Learning state importance for preference-based reinforcement learning
A linear fit gets the correct monotonicity directions
Learning to increase the power of conditional randomization tests
Correction to: efficient generator of mathematical expressions for symbolic regression
A distributed feature selection scheme with partial information sharing
Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks
The bane of skew - Uncertain ranks and unrepresentative precision
Tree-structured model diagnostics for linear regression
Self explainable graph convolutional recurrent network for spatio-temporal forecasting
Emergence and Categorization of Coordinated Visual Behavior Through Embodied Interaction
Choice of Basis for Laplace Approximation
Measuring the accuracy of currency crisis prediction with combined classifiers in designing early warning system
Learning from multiple annotators with varying expertise
Task-decoupled interactive embedding network for object detection
Propagation kernels: efficient graph kernels from propagated information
Deep collective matrix factorization for augmented multi-view learning
Randomised restarted search in ILP
On the convergence analysis of over-parameterized variational autoencoders: a neural tangent kernel perspective
Discovering predictive ensembles for transfer learning and meta-learning
PAV and the ROC convex hull
Online learning of network bottlenecks via minimax paths
SAMBA: safe model-based & active reinforcement learning
Comparative study of matrix refinement approaches for ensemble clustering
Asymptotic properties of Turing's formula in relative error
Entangle-then-disentangle: a novel approach for enhancing large vision-language model
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification
Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation
Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm
A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint
Quantum speed-up for unsupervised learning
Capturing the context-aware code change via dynamic control flow graph for commit message generation
MLife: a lite framework for machine learning lifecycle initialization
UCoDe: unified community detection with graph convolutional networks
Gradient boosted trees for evolving data streams
A simple homotopy proximal mapping algorithm for compressive sensing
A Heuristic Approach to the Discovery of Macro-Operators
A Simple Method for Generating Additive Clustering Models with Limited Complexity
Classification with costly features as a sequential decision-making problem
Using Correspondence Analysis to Combine Classifiers
Tight mixed-integer optimization formulations for prescriptive trees
Analytical Mean Squared Error Curves for Temporal Difference Learning
Real-World Robotics: Learning to Plan for Robust Execution
Hybrid acceleration techniques for the physics-informed neural networks: a comparative analysis
Learning Probabilistic Automata and Markov Chains via Queries
Empirical Learning as a Function of Concept Character
Neural discovery of balance-aware polarized communities
Conjectural Equilibrium in Multiagent Learning
Semi-supervised AUC optimization based on positive-unlabeled learning
Deep negative correlation classification
Transfer of Learning by Composing Solutions of Elemental Sequential Tasks
A framework for training larger networks for deep Reinforcement learning
Partial label learning with emerging new labels
Credal ensembling in multi-class classification
Invariant kernel functions for pattern analysis and machine learning
Guest Editors Introduction: Machine Learning in Speech and Language Technologies
Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning
Per-example gradient regularization improves learning signals from noisy data
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms
Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data
In-game soccer outcome prediction with offline reinforcement learning
Robust Classification for Imprecise Environments
A topological insight into restricted Boltzmann machines
Model-driven validation of visual explanations for multimodal emotion recognition
Kalt: generating adversarial explainable chinese legal texts
Classifier Systems and the Animat Problem
Regularized feature selection in reinforcement learning
Machine Learning and Grammar Induction
Search-based structured prediction
Languages as hyperplanes: grammatical inference with string kernels
Dual-view data augmentation at subgraph level and graph contrastive learning for sequential recommendation
Learning hierarchical probabilistic logic programs
Parameter identifiability of a deep feedforward ReLU neural network
Inductive Policy: The Pragmatics of Bias Selection
A constrained $$\ell $$ ℓ 1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models
Learning Decision Lists
Memory-adaptive high utility sequential pattern mining over data streams
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Worst-case regret analysis of computationally budgeted online kernel selection
Efficient max-margin multi-label classification with applications to zero-shot learning
AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow
Multi-label classification via multi-target regression on data streams
Multi-target regression via input space expansion: treating targets as inputs
Mining probabilistic automata: a statistical view of sequential pattern mining
Technical Update: Least-Squares Temporal Difference Learning
Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps
Exact bootstrap k -nearest neighbor learners
On the benefits of representation regularization in invariance based domain generalization
Also for k-means: more data does not imply better performance
Characterizing reinforcement learning methods through parameterized learning problems
Re-assessing accuracy degradation: a framework for understanding DNN behavior on similar-but-non-identical test datasets
Theoretical and Experimental Evaluation of the Subspace Information Criterion
Finding anomalous periodic time series
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
A comparative evaluation of stochastic-based inference methods for Gaussian process models
Survey on extreme learning machines for outlier detection
PAC Analogues of Perceptron and Winnow Via Boosting the Margin
Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets
Learning Boolean Functions in an Infinite Attribute Space
Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition
DEARank: a data-envelopment-analysis-based ranking method
STUDD: a student-teacher method for unsupervised concept drift detection
Data-efficient reinforcement learning by generalized value estimation
Integrating Feature Extraction and Memory Search
On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
Factorizing LambdaMART for cold start recommendations
Malicious Omissions and Errors in Answers to Membership Queries
Discretization for naive-Bayes learning: managing discretization bias and variance
A generic approach for reproducible model distillation
Lifted model checking for relational MDPs
Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations
A class sensitivity feature guided T-type generative model for noisy label classification
Transfer and share: semi-supervised learning from long-tailed data
Negative Results for Equivalence Queries
The pure exploration problem with general reward functions depending on full distributions
Synthesis of UNIX Programs Using Derivational Analogy
Constraint-based probabilistic modeling for statistical abduction
Imputation of missing links and attributes in longitudinal social surveys
Bandit-based Monte-Carlo structure learning of probabilistic logic programs
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning
Provable accelerated gradient method for nonconvex low rank optimization
Stabilize deep ResNet with a sharp scaling factor τ
Theory Change via View Application in Instructionless Learning
Multi-consensus decentralized primal-dual fixed point algorithm for distributed learning
Kernels and Distances for Structured Data
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Learning block-preserving graph patterns and its application to data mining
TRU-NET: a deep learning approach to high resolution prediction of rainfall
Bayesian policy reuse
Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes
Editorial of the special issue data analysis and intelligent optimization with applications
Faster Riemannian Newton-type optimization by subsampling and cubic regularization
Learning programs by learning from failures
A comparison of hierarchical multi-output recognition approaches for anuran classification
High-order proximity and relation analysis for cross-network heterogeneous node classification
An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method
Evidential uncertainty sampling strategies for active learning
A theory of transfer learning with applications to active learning
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Selecting Examples for Partial Memory Learning
Adversarial attacks on neural networks through canonical Riemannian foliations
Tiles: an online algorithm for community discovery in dynamic social networks
Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data
Mast: interpretable stress testing via meta-learning for forecasting model robustness evaluation
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving
A theory of learning with similarity functions
Node classification over bipartite graphs through projection
Special Issue of Machine Learning on Information Retrieval - Introduction
A scalable preference model for autonomous decision-making
A general framework for the practical disintegration of PAC-Bayesian bounds
Distribution-based aggregation for relational learning with identifier attributes
Fast Online Q(lambda)
Fast deep mixtures of Gaussian process experts
Selective switching mechanism in virtual machines via support vector machines and transfer learning
Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions
Efficiently identifying deterministic real-time automata from labeled data
Learning to rank on graphs
Elastic Translation Invariant Matching of Trajectories
Deep contrastive coordinated multi-view consistency clustering
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Limits of multi-relational graphs
Imbalanced regression and extreme value prediction
Modelling relational statistics with Bayes Nets
Distributed Bayesian matrix factorization with limited communication
Onboard object recognition for planetary exploration
Smoothing graphons for modelling exchangeable relational data
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
A brain-inspired algorithm for training highly sparse neural networks
Generalized Low Rank Approximations of Matrices
A new geometry-aware non-euclidean distance metric
Multiple Comparisons in Induction Algorithms
A Reply to Pazzani's Book Review of "Inductive Logic Programming: Techniques and Applications"
Module-Based Reinforcement Learning: Experiments with a Real Robot
Gradient descent fails to learn high-frequency functions and modular arithmetic
Preserving differential privacy in convolutional deep belief networks
Scalable variable selection for two-view learning tasks with projection operators
Bayesian Clustering by Dynamics
Neural RELAGGS
MAGMA: inference and prediction using multi-task Gaussian processes with common mean
On Learning Sets and Functions
Bounds on the sample complexity for private learning and private data release
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models
Improving active Mealy machine learning for protocol conformance testing
Periodic step-size adaptation in second-order gradient descent for single-pass on-line structured learning
Coresets for kernel clustering
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
Cost-sensitive classification with cost uncertainty: do we need surrogate losses?
Shift guided active learning
Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation
DPG: a model to build feature subspace against adversarial patch attack
Robust matrix estimations meet Frank-Wolfe algorithm
Probabilistic movement models and zones of control
Randomized approximate class-specific kernel spectral regression analysis for large-scale face verification
De-biased two-sample U-statistics with application to conditional distribution testing
Machine learning for science and society
Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks
Exploiting symmetries for scaling loopy belief propagation and relational training
Efficient Specialization of Relational Concepts
Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification
The Problem of Expensive Chunks and its Solution by Restricting Expressiveness
Population recovery and partial identification
Learning and Revising User Profiles: The Identification of Interesting Web Sites
Least-squares temporal difference with expected eligibility traces
Correction to: Meta-interpretive learning as metarule specialisation
Inductive logic programming at 30
Neural Network-Based Vision for Precise Control of a Walking Robot
Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming
Adversarial supervised contrastive learning
Correction to: Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events
Infinite factorization of multiple non-parametric views
InfoGram and admissible machine learning
Learning system parameters from turing patterns
Learning to Predict Non-Deterministically Generated Strings
Causality from bottom to top: a survey
Corpus-based Learning of Analogies and Semantic Relations
Explaining word embeddings with perfect fidelity: a case study in predicting research impact
Online Bayesian max-margin subspace learning for multi-view classification and regression
A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning
Training data influence analysis and estimation: a survey
Sequential event prediction
A Unified View on Clustering Binary Data
Causal discovery with a mixture of DAGs
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference
Cost curves: An improved method for visualizing classifier performance
Optimised one-class classification performance
A geometric framework for multiclass ensemble classifiers
Preface to the special issue on inductive logic programming
Online active classification via margin-based and feature-based label queries
Computing the distance between unbalanced distributions: the flat metric
Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images
Safety-constrained reinforcement learning with a distributional safety critic
Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning
Intramodal consistency in triplet-based cross-modal learning for image retrieval
A survey of class-imbalanced semi-supervised learning
Temporal kernel CCA and its application in multimodal neuronal data analysis
Binary classification with ambiguous training data
A constrained matrix-variate Gaussian process for transposable data
An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization
Learning noisy linear classifiers via adaptive and selective sampling
General and Efficient Multisplitting of Numerical Attributes
Online Multiple Kernel Classification
Explaining recommendation system using counterfactual textual explanations
Transfer in variable-reward hierarchical reinforcement learning
Connecting weighted automata, tensor networks and recurrent neural networks through spectral learning
Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning
Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016)
A parameter-less algorithm for tensor co-clustering
Improve generated adversarial imitation learning with reward variance regularization
Book Review: "Machine Learning: A Theoretical Approach"
An incremental piecewise linear classifier based on polyhedral conic separation
Adaptively learning probabilistic deterministic automata from data streams
An Empirical Comparison of Pruning Methods for Decision Tree Induction
Reduced implication-bias logic loss for neuro-symbolic learning
Feasible Direction Decomposition Algorithms for Training Support Vector Machines
PolieDRO: a novel classification and regression framework with non-parametric data-driven regularization
Neural network relief: a pruning algorithm based on neural activity
Greedy learning of latent tree models for multidimensional clustering
On Data and Algorithms: Understanding Inductive Performance
The Induction of Dynamical Recognizers
Strategies in Combined Learning via Logic Programs
Handling epistemic and aleatory uncertainties in probabilistic circuits
Application of abductive ILP to learning metabolic network inhibition from temporal data
The Appropriateness of Predicate Invention as Bias Shift Operation in ILP
Rethinking statistical learning theory: learning using statistical invariants
Modeling topic control to detect influence in conversations using nonparametric topic models
Unmasking deception: a topic-oriented multimodal approach to uncover false information on social media
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
Differentially private Riemannian optimization
The changing science of machine learning
Heterogeneous multi-task Gaussian Cox processes
How to be fair? A study of label and selection bias
Correction to: Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations
Knoop: practical enhancement of knockoff with over-parameterization for variable selection
Generalization of temporal logic tasks via future dependent options
Learning register automata: from languages to program structures
Constraint guided autoencoders for joint optimization of condition indicator estimation and anomaly detection in machine condition monitoring
Conditional density estimation and simulation through optimal transport
Probabilistic clustering of time-evolving distance data
Communication-efficient clustered federated learning via model distance
Evaluation and Selection of Biases in Machine Learning
Byzantine-robust distributed sparse learning for M-estimation
Brave induction: a logical framework for learning from incomplete information
Direct conditional probability density estimation with sparse feature selection
Utilising energy function and variational inference training for learning a graph neural network architecture
Distribution-free conformal joint prediction regions for neural marked temporal point processes
Effective and Efficient Knowledge Base Refinement
Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables
Bonsai: diverse and shallow trees for extreme multi-label classification
Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes
Theoretical guarantees for domain adaptation with hierarchical optimal transport
Editorial: special issue on machine learning for health and medicine
Special issue on inductive logic programming
Correlated topographic analysis: estimating an ordering of correlated components
Optimizing sharpe ratio: risk-adjusted decision-making in multi-armed bandits
Meta-Interpretive Learning from noisy images
Sandbox: safeguarded multi-label learning through safe optimal transport
Transforming variables to central normality
Adversarial vulnerability bounds for Gaussian process classification
Interactive Concept-Learning and Constructive Induction by Analogy
Multilabel classification via calibrated label ranking
Machine learning from casual conversation
Introduction to the Special Issue on Meta-Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Linear Programming Boosting via Column Generation
Introduction: special issue of selected papers from ACML 2015
Structural Modelling with Sparse Kernels
A Branch and Bound Incremental Conceptual Clusterer
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
ProtoSimi: label correction for fine-grained visual categorization
Learning constraints in spreadsheets and tabular data
Machine learning in corporate credit rating assessment using the expanded audit report
Uncovering temporal patterns in visualizations of high-dimensional data
Search or split: policy gradient with adaptive policy space
Graph spring neural ODEs for link sign prediction
Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms
Conformal load prediction with transductive graph autoencoders
Developing safe and responsible large language model: can we balance bias reduction and language understanding?
The randomized information coefficient: assessing dependencies in noisy data
Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction
Automated Knowledge Acquisition for Strategic Knowledge
Machine unlearning: linear filtration for logit-based classifiers
Gentle local robustness implies generalization
Explorations of an Incremental, Bayesian Algorithm for Categorization
Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem
The FFN as a Key-Value Memory: Functional Specialization in Transformer Computation
Efficient covariance matrix update for variable metric evolution strategies
Variational Bayesian sparse additive matrix factorization
Bayesian optimization with approximate set kernels
On the VC Dimension of Bounded Margin Classifiers
Understanding prediction discrepancies in classification
Efficient Reinforcement Learning through Symbiotic Evolution
Associative Reinforcement Learning: A Generate and Test Algorithm
SAED: self-attentive energy disaggregation
Reinforcement Learning with Replacing Eligibility Traces
Statistical analysis of kernel-based least-squares density-ratio estimation
Almost optimal estimates for approximation and learning by radial basis function networks
Markov logic networks
Gradient descent optimizes over-parameterized deep ReLU networks
Deep Gaussian Process autoencoders for novelty detection
Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs
Online Passive-Aggressive Active learning
Temporal distillation: compressing a policy in space and time