Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised...

data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

parse tree or a labeled graph, then standard methods must be extended. Learning to rank: When the input is a set of objects and the desired output is a ranking...

The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (ICML '05). ACM, New...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...

the learning rate is often varied during training either in accordance to a learning rate schedule or by using an adaptive learning rate. The learning rate...

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank)...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms...

machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year...