yusuke1997/dblp-id-title huggingface dataset:
A Review of Machine Learning at AAAI-87
Inverse reinforcement learning from summary data
Combining Symbolic and Neural Learning
Bidding agents for online auctions with hidden bids
On the Handling of Continuous-Valued Attributes in Decision Tree Generation
On the Discrepancy between Kleinberg's Clustering Axioms and k-Means Clustering Algorithm Behavior
Knowledge-aware image understanding with multi-level visual representation enhancement for visual question answering
Feature-subspace aggregating: ensembles for stable and unstable learners
On Average Versus Discounted Reward Temporal-Difference Learning
Entity recognition based on heterogeneous graph reasoning of visual region and text candidate
Adaptive Retrieval Agents: Internalizing Local Context and Scaling up to the Web
DynamiSE: dynamic signed network embedding for link prediction
Using Iterated Bagging to Debias Regressions
Heterogeneous sets in dimensionality reduction and ensemble learning
Differentially-private data synthetisation for efficient re-identification risk control
Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding
Towards enabling learnware to handle heterogeneous feature spaces
Shifting Vocabulary Bias in Speedup Learning
Engineering problems in machine learning systems
Multilabel classification through random graph ensembles
Boolean Feature Discovery in Empirical Learning
The World Would Be a Better Place if Non-Programmers Could Program
Extending boosting for large scale spoken language understanding
Traditional and context-specific spam detection in low resource settings
A unified probabilistic framework for robust manifold learning and embedding
Model selection in reinforcement learning
3DVerifier: efficient robustness verification for 3D point cloud models
Robust ordinal regression in preference learning and ranking
On Mining Summaries by Objective Measures of Interestingness
Guest editorial: special issue on reinforcement learning for real life
Least-squares independence regression for non-linear causal inference under non-Gaussian noise
Interactive learning of node selecting tree transducer
Robust non-parametric regression via incoherent subspace projections
An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition
MUSO: achieving exact machine unlearning in over-parameterized regimes
An efficient model training framework for green AI
Trajectory test-train overlap in next-location prediction datasets
Visualizing non-metric similarities in multiple maps
Creating a Memory of Casual Relationships (Book Review)
Learning Sequential Decision Rules Using Simulation Models and Competition
On-line Prediction and Conversion Strategies
The Complexity of Learning According to Two Models of a Drifting Environment
Optimistic optimisation of composite objective with exponentiated update
Uncover and unlearn nuisances: agnostic fully test-time adaptation
Fair tree classifier using strong demographic parity
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo
Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks
Sparse topical analysis of dyadic data using matrix tri-factorization
Ranking by inspiration: a network science approach
Corruption-tolerant bandit learning
Evolutionary Rule Mining in Time Series Databases
Performance and interaction assessment of neural network architectures and bivariate smart predict-then-optimize
Deep Errors-in-Variables using a diffusion model
New algorithms for trace-ratio problem with application to high-dimension and large-sample data dimensionality reduction
The Synergy Between PAV and AdaBoost
Hyperparameter importance and optimization of quantum neural networks across small datasets
Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain
Improved Generalization Through Explicit Optimization of Margins
Multi-domain learning by confidence-weighted parameter combination
DPQ: dynamic pseudo-mean mixed-precision quantization for pruned neural network
Preference-based learning to rank
Learning payoff functions in infinite games
Pasting Small Votes for Classification in Large Databases and On-Line
Robust query performance prediction for dense retrievers via adaptive disturbance generation
Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement
Inverse learning in Hilbert scales
Guest Editorial: Machine learning in and for music
Bayesian learning of graphical vector autoregressions with unequal lag-lengths
Learning from Cluster Examples
Upper and lower bounds for complete linkage in general metric spaces
Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning
Bridging logic and kernel machines
A Bayesian nonparametric model for multi-label learning
Introduction to the special issue on learning and computational game theory
An investigation into feature construction to assist word sense disambiguation
Exact Learning of Formulas in Parallel
A Version Space Approach to Learning Context-free Grammars
Expectation propagation in linear regression models with spike-and-slab priors
Reshaped tensor nuclear norms for higher order tensor completion
Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification
The Use of Background Knowledge in Decision Tree Induction
Predictive spreadsheet autocompletion with constraints
Hitting the target: stopping active learning at the cost-based optimum
LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica
Toward finding strong pareto optimal policies in multi-agent reinforcement learning
Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier
Co-Evolution in the Successful Learning of Backgammon Strategy
Linear support vector regression with linear constraints
End-to-end entity-aware neural machine translation
Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments
Simulating the Child's Acquisition of the Lexicon and Syntax-Experiences with Babel
PAL: A Pattern-Based First-Order Inductive System
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics
Universal parameter optimisation in games based on SPSA
Multi-target prediction for dummies using two-branch neural networks
Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantisation
Can language models automate data wrangling?
Probabilistic logic programming for hybrid relational domains
Neural Network Design and the Complexity of Learning (Book Review)
Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
Implementing Valiant's Learnability Theory Using Random Sets
A Microchoice Bound for Continuous-Space Classification Algorithms
Learning curves for decision making in supervised machine learning: a survey
Active Learning for Vision-Based Robot Grasping
Explaining Siamese networks in few-shot learning
Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Feature selection via sensitivity analysis of SVM probabilistic outputs
A neural meta model for predicting winter wheat crop yield
Model Selection for Small Sample Regression
LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction
Quantifying Prior Determination Knowledge Using the PAC Learning Model
Sparse kernel SVMs via cutting-plane training
Handling concept drift via model reuse
Varying-coefficient models for geospatial transfer learning
Optimal control as a graphical model inference problem
Self Calibration of the Fixation Movement of a Stereo Camera Head
Editorial: Data Mining Lessons Learned
A comparison of latent space modeling techniques in a plain-vanilla autoencoder setting
Task-Structures, Knowledge Acquisition and Learning
Hellinger distance decision trees for PU learning in imbalanced data sets
Regional bias in monolingual English language models
A Riemannian gossip approach to subspace learning on Grassmann manifold
Robust Learning with Missing Data
Predicting rice phenotypes with meta and multi-target learning
Improved second-order bounds for prediction with expert advice
Embed2Detect: temporally clustered embedded words for event detection in social media
Explanation-Based Generalization: A Unifying View
Learning the structure of dynamic Bayesian networks from time series and steady state measurements
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Online Learning versus Offline Learning
Learning with Genetic Algorithms: An Overview
SPADE: An Efficient Algorithm for Mining Frequent Sequences
Conformal prediction for regression models with asymmetrically distributed errors: application to aircraft navigation during landing maneuver
Research Papers in Machine Learning
Large scale tensor regression using kernels and variational inference
Deep intrinsically motivated exploration in continuous control
A High Order Cumulants Based Multivariate Nonlinear Blind Source Separation Method
An efficient top-down search algorithm for learning Boolean networks of gene expression
Local neighborhood encodings for imbalanced data classification
Topic level expertise search over heterogeneous networks
Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains
Tracking Drifting Concepts By Minimizing Disagreements
Learning higher-order logic programs
Data scarcity, robustness and extreme multi-label classification
Optimizing model-agnostic random subspace ensembles
Misclassification bounds for PAC-Bayesian sparse deep learning
SDANet: spatial deep attention-based for point cloud classification and segmentation
Foreword: special issue for the journal track of the 11th Asian Conference on Machine Learning (ACML 2019)
Modeling individual email patterns over time with latent variable models
Slow emergence of cooperation for win-stay lose-shift on trees
Mathematical applications of inductive logic programming
When is the Naive Bayes approximation not so naive?
Majority vote ensembles of conformal predictors
Improving fairness generalization through a sample-robust optimization method
Re-thinking model robustness from stability: a new insight to defend adversarial examples
Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks
Determining Arguments of Invariant Functional Descriptions
Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
Erratum to: Preference Relation-based Markov Random Fields for Recommender Systems
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model
Any-time Relational Reasoning: Resource-bounded Induction and Deduction Through Stochastic Matching
Optimised probabilistic active learning (OPAL) - For fast, non-myopic, cost-sensitive active classification
Dolores: a model that predicts football match outcomes from all over the world
Scaling the weight parameters in Markov logic networks and relational logistic regression models
Spatiotemporal-view member preference contrastive representation learning for group recommendation
Generalization bounds for non-stationary mixing processes
Dealing with the unevenness: deeper insights in graph-based attack and defense
Modeling outcomes of soccer matches
Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test
Testing exchangeability in the batch mode with e-values and Markov alternatives
Beam search algorithms for multilabel learning
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Guest Editors' Introduction: Special issue on Learning Theory (COLT-2007)
Colearning in Differential Games
Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue
Suboptimal behavior of Bayes and MDL in classification under misspecification
Geometry preserving multi-task metric learning
Dynamic weighted ensemble for diarrhoea incidence predictions
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks
Learning rotations with little regret
Similarity measures over refinement graphs
Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series
Adaptive Probabilistic Networks with Hidden Variables
Classification-based objective functions
Melodic analysis with segment classes
Wasserstein discriminant analysis
Bayesian object matching
Tensor Q-rank: new data dependent definition of tensor rank
Guest editors' introduction: special issue on inductive logic programming (ILP-2007)
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music
Matrix-wise ℓ 0 -constrained sparse nonnegative least squares
Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data
Upper and Lower Bounds on the Learning Curve for Gaussian Processes
Improving reinforcement learning by using sequence trees
Toward a Unified Science of Machine Learning
Focused multi-task learning in a Gaussian process framework
Boost image captioning with knowledge reasoning
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Kernel machines for current status data
An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat
A Noninformative Prior for Neural Networks
Creating Advice-Taking Reinforcement Learners
Experiment databases - A new way to share, organize and learn from experiments
Incremental Learning from Noisy Data
Completing causal networks by meta-level abduction
Learning global object-centric representations via disentangled slot attention
SLISEMAP: supervised dimensionality reduction through local explanations
Machine Learning: A Maturing Field
Learning to rank anomalies: scalar performance criteria and maximization of rank statistics
On Discriminative Bayesian Network Classifiers and Logistic Regression
Guest editorial: Learning theory
Toward optimal probabilistic active learning using a Bayesian approach
Multistrategy Learning and Theory Revision
Persistent Laplacian-enhanced algorithm for scarcely labeled data classification
Discriminative extended canonical correlation analysis for pattern set matching
When Interpretability Meets Noise: An LLM-Assisted Hybrid Deep Logical Rule Learning Framework
A notion of task relatedness yielding provable multiple-task learning guarantees
Introduction: special issue of selected papers from ACML 2014
Improving sequential latent variable models with autoregressive flows
The optimal unbiased value estimator and its relation to LSTD, TD and MC
Nyström landmark sampling and regularized Christoffel functions
Improving classification performance through selective instance completion
Conflict Resolution as Discovery in Particle Physics
Tikhonov, Ivanov and Morozov regularization for support vector machine learning
SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting
Adaptive infinite dropout for noisy and sparse data streams
Variable Resolution Discretization in Optimal Control
A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval
Deep learning's shallow gains: a comparative evaluation of algorithms for automatic music generation
Distributed block-diagonal approximation methods for regularized empirical risk minimization
Introduction to the special issue on dynamic networks and knowledge discovery
Guest editors' introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning
Partitioned least squares
Bayesian tensor factorisations for time series of counts
Probabilistic and exact frequent subtree mining in graphs beyond forests
An interpretable sample selection framework against numerical label noise
Lifting symmetry breaking constraints with inductive logic programming
Structured prediction by joint kernel support estimation
Automatic Feature Extraction for Classifying Audio Data
On combining machine learning with decision making
Multistrategy Theory Revision: Induction and Abduction in INTHELEX
Modeling, analyzing, and synthesizing expressive piano performance with graphical models
On better training the infinite restricted Boltzmann machines
Using duration models to reduce fragmentation in audio segmentation
A survey on self-supervised learning for non-sequential tabular data
Meta-interpretive learning as metarule specialisation
Boosted multi-task learning
Neural predictor-based automated graph classifier framework
Learning Team Strategies: Soccer Case Studies
Empirical risk minimization in the interpolating regime with application to neural network learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Spatio-temporal random fields: compressible representation and distributed estimation
Let the noise flow away: combating noisy labels using normalizing flows
Learning rules for multi-label classification: a stacking and a separate-and-conquer approach
Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm
Multiway p-spectral graph cuts on Grassmann manifolds
Goal-conditioned offline reinforcement learning through state space partitioning
Learning From Noisy Examples
Editorial: Machine learning for safety-critical applications in engineering
Empirical analysis of performance assessment for imbalanced classification
Wavelet-packets for deepfake image analysis and detection
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations
Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017)
Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition
On the need for structure modelling in sequence prediction
Learning (predictive) risk scores in the presence of censoring due to interventions
Scalable individual treatment effect estimator for large graphs
ML-Plan: Automated machine learning via hierarchical planning
The true sample complexity of active learning
Mining frequent closed rooted trees
DiverSeed: Integrating Active Learning for Target Domain Data Generation in Instruction Tuning
Structured machine learning: the next ten years
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation
Effective approximation of parametrized closure systems over transactional data streams
Time-aware tensor decomposition for sparse tensors
U-shaped, iterative, and iterative-with-counter learning
Supervised clustering of label ranking data using label preference information
Imbalanced regression pipeline recommendation
Multivariate functional linear discriminant analysis for partially-observed time series
ROAD-R: the autonomous driving dataset with logical requirements
Online AutoML: an adaptive AutoML framework for online learning
Exploiting residual errors in nonlinear online prediction
Learning with Probabilistic Representations
Learning Green's function efficiently using low-rank approximations
Latent grouping models for user preference prediction
State-novelty guided action persistence in deep reinforcement learning
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks
Dynamic principal projection for cost-sensitive online multi-label classification
Epidemic-guided deep learning for spatiotemporal forecasting of tuberculosis outbreak
Simpler PAC-Bayesian bounds for hostile data
Conformal and probabilistic prediction with applications: editorial
One-Stage Tree: end-to-end tree builder and pruner
Derivational Analogy in Prodigy: Automating Case Acquisition, Storage, and Utilization
A formal framework and extensions for function approximation in learning classifier systems
Concept Decompositions for Large Sparse Text Data Using Clustering
A framework to uncover multiple alternative clusterings
Learning to assign degrees of belief in relational domains
ReliefE: feature ranking in high-dimensional spaces via manifold embeddings
Lower bounds on individual sequence regret
A survey on self-supervised methods for visual representation learning
Robust reputation independence in ranking systems for multiple sensitive attributes
Rope-net: deep convolutional neural network via robust principal component analysis
ALR n : accelerated higher-order logistic regression
Multivariate Decision Trees
Automated adaptation strategies for stream learning
Modeling Cognitive Development on Balance Scale Phenomena
Dual coordinate descent methods for logistic regression and maximum entropy models
Concept learning in description logics using refinement operators
Clustered and deep echo state networks for signal noise reduction
Lagrangian objective function leads to improved unforeseen attack generalization
Maximizing Theory Accuracy Through Selective Reinterpretation
RankMerging: a supervised learning-to-rank framework to predict links in large social networks
Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning
TAN Classifiers Based on Decomposable Distributions
A family of pairwise multi-marginal optimal transports that define a generalized metric
Efficient parameter learning of Bayesian network classifiers
Learning compact Markov logic networks with decision trees
Large-scale pinball twin support vector machines
First Nearest Neighbor Classification on Frey and Slate's Letter Recognition Problem
Good edit similarity learning by loss minimization
Integrating Quantitative and Qualitative Discovery: The ABACUS System
Deep learning and multivariate time series for cheat detection in video games
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
Riemannian block SPD coupling manifold and its application to optimal transport
A unified framework for online trip destination prediction
Wastewater Treatment Systems from Case-Based Reasoning
The Machine Learning journal: 25 years young
Guest editorial to the special issue on grammatical inference
A process for predicting manhole events in Manhattan
A 'PAC' Algorithm for Making Feature Maps
Linear classifiers are nearly optimal when hidden variables have diverse effects
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
A Decision-Tree Model of Balance Scale Development
Black-box Bayesian adversarial attack with transferable priors
Experiments with Incremental Concept Formation: UNIMEM
Spatio-temporal convolution kernels
Learning ( k , l )-contextual tree languages for information extraction from web pages
Constructing X-of-N Attributes for Decision Tree Learning
Improving Generalization with Active Learning
Learning a priori constrained weighted majority votes
Complexity parameters for first order classes
Pairwise learning to rank by neural networks revisited: reconstruction, theoretical analysis and practical performance
A reinforcement learning approach to autonomous decision-making in smart electricity markets
Lower Bound Methods and Separation Results for On-Line Learning Models
Projected estimators for robust semi-supervised classification
Introduction to the special issue on empirical evaluations in reinforcement learning
Introduction: Special Issue on Computational Learning Theory
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data
The first learning track of the international planning competition
Aligning model outputs for class imbalanced non-IID federated learning
Generalized vec trick for fast learning of pairwise kernel models
ALLPAD: approximate learning of logic programs with annotated disjunctions