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Supervised learning : Given a set of N training examples of the form ,y_),...,(x_,\;y_)\ such that x i is the feature vector of the i -th example and y i is its label (i.e., class), a learning algorithm seeks a function g : X → Y , where X is the input space and Y is the output space. The function g is an elem... |
Supervised learning : The training methods described above are discriminative training methods, because they seek to find a function g that discriminates well between the different output values (see discriminative model). For the special case where f ( x , y ) = P ( x , y ) is a joint probability distribution and th... |
Supervised learning : There are several ways in which the standard supervised learning problem can be generalized: Semi-supervised learning or weak supervision: the desired output values are provided only for a subset of the training data. The remaining data is unlabeled or imprecisely labeled. Active learning: Instead... |
Supervised learning : Analytical learning Artificial neural network Backpropagation Boosting (meta-algorithm) Bayesian statistics Case-based reasoning Decision tree learning Inductive logic programming Gaussian process regression Genetic programming Group method of data handling Kernel estimators Learning automata Lear... |
Supervised learning : Bioinformatics Cheminformatics Quantitative structure–activity relationship Database marketing Handwriting recognition Information retrieval Learning to rank Information extraction Object recognition in computer vision Optical character recognition Spam detection Pattern recognition Speech recogni... |
Supervised learning : Computational learning theory Inductive bias Overfitting (Uncalibrated) class membership probabilities Version spaces |
Supervised learning : List of datasets for machine-learning research Unsupervised learning |
Supervised learning : Machine Learning Open Source Software (MLOSS) |
Associative classifier : An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., in which the authors defined a model made of rules "whose right-hand side are restricted to the classifi... |
Associative classifier : The model generated by an AC and used to label new records consists of association rules, where the consequent corresponds to the class label. As such, they can also be seen as a list of "if-then" clauses: if the record matches some criteria (expressed in the left side of the rule, also called ... |
Associative classifier : The rules of an AC inherit some of the metrics of association rules, like the support or the confidence. Metrics can be used to order or filter the rules in the model and to evaluate their quality. |
Associative classifier : The first proposal of a classification model made of association rules was FBM. The approach was popularized by CBA, although other authors had also previously proposed the mining of association rules for classification. Other authors have since then proposed multiple changes to the initial mod... |
Imitation learning : Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations. It is also called learning from demonstration and apprenticeship learning. It has been applied to underactuated robotics, self-driving cars, quadcop... |
Imitation learning : Expert demonstrations are recordings of an expert performing the desired task, often collected as state-action pairs ( o t ∗ , a t ∗ ) ^,a_^) . |
Imitation learning : Inverse Reinforcement Learning (IRL) learns a reward function that explains the expert's behavior and then uses reinforcement learning to find a policy that maximizes this reward. Generative Adversarial Imitation Learning (GAIL) uses generative adversarial networks (GANs) to match the distribution ... |
Imitation learning : Reinforcement learning Supervised learning Inverse reinforcement learning |
Imitation learning : Hussein, Ahmed; Gaber, Mohamed Medhat; Elyan, Eyad; Jayne, Chrisina (2018-03-31). "Imitation Learning: A Survey of Learning Methods". ACM Computing Surveys. 50 (2): 1–35. doi:10.1145/3054912. hdl:10059/2298. ISSN 0360-0300. == References == |
Structured prediction : Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than discrete or real values. Similar to commonly used supervised learning techniques, structured prediction models are typically... |
Structured prediction : An example application is the problem of translating a natural language sentence into a syntactic representation such as a parse tree. This can be seen as a structured prediction problem in which the structured output domain is the set of all possible parse trees. Structured prediction is used i... |
Structured prediction : Probabilistic graphical models form a large class of structured prediction models. In particular, Bayesian networks and random fields are popular. Other algorithms and models for structured prediction include inductive logic programming, case-based reasoning, structured SVMs, Markov logic networ... |
Structured prediction : Noah Smith, Linguistic Structure Prediction, 2011. Michael Collins, Discriminative Training Methods for Hidden Markov Models, 2002. |
Structured prediction : Implementation of Collins structured perceptron |
Constrained conditional model : A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the mode... |
Constrained conditional model : Making decisions in many domains (such as natural language processing and computer vision problems) often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. These settings ... |
Constrained conditional model : Given a set of feature functions (x,y)\ and a set of constraints (x,y)\ , defined over an input structure x ∈ X and an output structure y ∈ Y , a constraint conditional model is characterized by two weight vectors, w and ρ , and is defined as the solution to the following optimizati... |
Constrained conditional model : The advantages of the CCM declarative formulation and the availability of off-the-shelf solvers have led to a large variety of natural language processing tasks being formulated within the framework, including semantic role labeling, syntactic parsing, coreference resolution, summarizati... |
Constrained conditional model : CCM Tutorial Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP |
Constrained conditional model : University of Illinois Cognitive Computation Group Workshop on Integer Linear Programming for Natural Language Processing, NAACL-2009 == References == |
Structured kNN : Structured k-nearest neighbours (SkNN) is a machine learning algorithm that generalizes k-nearest neighbors (k-NN). k-NN supports binary classification, multiclass classification, and regression, whereas SkNN allows training of a classifier for general structured output. For instance, a data sample mig... |
Structured kNN : As a training set, SkNN accepts sequences of elements with class labels. The type of element does not matter; the only requirement is a defined metric function that gives a distance between each pair of elements of a set. SkNN is based on idea of creating a graph, with each node representing a class la... |
Structured kNN : Labelling input sequences by SkNN consists of finding the sequence of transitions in the graph, starting from node START. Each transition corresponds to a single element of the input sequence. As a result, the label of each element is determined as the target node label of the transition. The cost of t... |
Structured kNN : Implementation examples |
Anima Anandkumar : Animashree (Anima) Anandkumar is the Bren Professor of Computing at California Institute of Technology. Previously, she was a senior director of Machine Learning research at NVIDIA and a principal scientist at Amazon Web Services. Her research considers tensor-algebraic methods, deep learning and non... |
Anima Anandkumar : Anandkumar was born in Mysore. Her parents are both engineers, and her grandfather was a mathematician. Her great-great-grandfather was the Sanskrit scholar R. Shamasastry. She began to study Bharatanatyam and she learnt this style of dancing for many years. She studied electrical engineering at the ... |
Anima Anandkumar : In 2010, Anandkumar joined University of California, Irvine, as an assistant professor. At the time, the technology industry was at the beginning of the big data revolution. Here she started working on tensor decompositions of latent variable models. She joined Microsoft Research in New England as a ... |
Anima Anandkumar : In December 2020, Anandkumar was embroiled in a Twitter controversy, when she published a list of individuals who allegedly followed, liked or supported any Tweets made by Pedro Domingos allegedly in relation to his controversial views on the renaming of NeurIPS, Timnit Gebru's controversial exit at ... |
Anima Anandkumar : Anandkumar has won several awards and honours, including: 2025 Time100 Impact Award 2025 IEEE Kiyo Tomiyasu Award 2024 Blavatnik Award for Young Scientists 2024 TED Speaker 2024 Distinguished Alumnus Award by IIT Madras 2023 Guggenheim Fellow 2023 Schmidt Sciences AI 2050 Senior Fellow 2023 AAAI Fell... |
Tal Arbel : Tal Arbel is a Professor of Electrical Engineering at McGill University who specialises in computer vision. She is interested in the application of artificial intelligence in healthcare. |
Tal Arbel : Arbel was born in Montreal. Arbel's father was an electrical engineer. As a child Arbel was given a TRS-80 computer, which she used to play video games like pong. Alongside her computer, Arbel's father encouraged her to play with model planes and Lego. She studied science at CEGEP, before joining McGill Uni... |
Tal Arbel : She works on algorithms to interpret medical images, which are used to assist in drug discovery and diagnostics. She is particularly interested in graphical models for pathology in large datasets of patient images. Her software can be used for image-guided neurosurgery. She was appointed to McGill Universit... |
Christopher G. Atkeson : Christopher Granger Atkeson (born 28 May 1959) is an American roboticist and a professor at the Robotics Institute and Human-Computer Interaction Institute at Carnegie Mellon University (CMU). Atkeson is known for his work in humanoid robots, soft robotics, and machine learning, most notably on... |
Christopher G. Atkeson : Atkeson graduated summa cum laude from Harvard University in 1981 with an A.B. in biochemistry. He received his S.M. degree in applied mathematics in the same year, also from Harvard. He then attended the Massachusetts Institute of Technology and received his PhD in brain and cognitive science ... |
Christopher G. Atkeson : Before joining the faculty at CMU in 2000, he was an assistant, then associate professor in the department of Brain and Cognitive Sciences at MIT from 1986 to 1993. He was also an associate professor at the College of Computing, Georgia Institute of Technology from 1994 to 2000. Atkeson's work ... |
Christopher G. Atkeson : National Science Foundation Engineering Initiation Award, 1987–1988. National Science Foundation Presidential Young Investigator Award, 1988–1993. W. M. Keck Foundation Assistant Professorship in Biomedical Engineering, 1988–1990. Alfred P. Sloan Research Fellow, 1989–1991. W. M. Keck Foundatio... |
Christopher G. Atkeson : Atkeson is married to Jessica Hodgins, Professor of Computer Science and Robotics at CMU, and former director of Disney Research, Pittsburgh. |
Christopher G. Atkeson : Home Page Mathematics Genealogy Project profile |
Australian Institute for Machine Learning : The Australian Institute for Machine Learning (AIML) is an artificial intelligence (AI) and machine learning research and translation institute based at the Lot Fourteen innovation precinct in Adelaide, South Australia. |
Australian Institute for Machine Learning : An institute of the University of Adelaide, AIML was established in 2018 by incorporating the university's Australian Centre of Visual Technologies (ACVT). It officially joined Lot Fourteen as a tenant in 2020. The Government of South Australia put A$7.1m into the opening of ... |
Australian Institute for Machine Learning : AIML is a research institute focused on artificial intelligence and machine learning based at Lot Fourteen on North Terrace in Adelaide city centre. It has more than 160 members, and is the largest university-based research site dedicated to machine learning in Australia, as ... |
Australian Institute for Machine Learning : AIML is regarded as the best in the world in several areas, for example in pedestrian detection, reconstructing 3D from 2D, semantic segmentation, tracking and identification, overhead image classification and face detection. In November 2021, AIML was awarded the Excellence ... |
Pierre Baldi : Pierre Baldi is a distinguished professor of computer science at University of California Irvine and the director of its Institute for Genomics and Bioinformatics. |
Pierre Baldi : Born in Rome (Italy), Pierre Baldi received his Bachelor of Science and Master of Science degrees at the University of Paris, in France. He then obtained his Ph.D. degree in mathematics at the California Institute of Technology in 1986 supervised by R. M. Wilson. |
Pierre Baldi : From 1986 to 1988, he was a postdoctoral fellow at the University of California, San Diego. From 1988 to 1995, he held faculty and member of the technical staff positions at the California Institute of Technology and at the Jet Propulsion Laboratory, where he was awarded the Lew Allen Award for Excellenc... |
Samy Bengio : Samy Bengio is a Canadian computer scientist currently serving as senior director of Artificial Intelligence and Machine Learning Research at Apple. |
Samy Bengio : Bengio obtained his Ph.D. in Computer Science in 1993 with a thesis titled Optimization of a Parametric Learning Rule for Neural Networks from the Université de Montréal. Before that, Bengio got an M.Sc. in Computer Science in 1989 with a thesis on Integration of Traditional and Intelligence Tutoring Syst... |
Samy Bengio : According to DBLP, Samy Bengio has authored around 250 scientific papers on neural networks, machine learning, deep learning, statistics, computer vision and natural language processing. The most cited of these include some of the early works sparking the 2010s deep learning revolution by showing how to e... |
Samy Bengio : Bengio is senior director of Artificial Intelligence and Machine Learning Research at Apple and adjunct professor at École Polytechnique Fédérale de Lausanne. He was formerly a longtime scientist at Google, where he led a large group of researchers working in machine learning, including adversarial settin... |
Samy Bengio : Samy Bengio was born to two Moroccan Jews who emigrated to France and Canada. He is the brother of Turing Award winner Yoshua Bengio. Both of them lived in Morocco for a year during their father's military service there. His father, Carlo Bengio, was a pharmacist who wrote theatre pieces and ran a Sephard... |
Yoshua Bengio : Yoshua Bengio (born March 5, 1964) is a Canadian-French computer scientist, and a pioneer of artificial neural networks and deep learning. He is a professor at the Université de Montréal and scientific director of the AI institute MILA. Bengio received the 2018 ACM A.M. Turing Award, often referred to a... |
Yoshua Bengio : Bengio was born in France to a Jewish family who had emigrated to France from Morocco. The family then relocated to Canada. He received his Bachelor of Science degree (electrical engineering), MSc (computer science) and PhD (computer science) from McGill University. Bengio is the brother of Samy Bengio,... |
Yoshua Bengio : After his PhD, Bengio was a postdoctoral fellow at MIT (supervised by Michael I. Jordan) and AT&T Bell Labs. Bengio has been a faculty member at the Université de Montréal since 1993, heads the MILA (Montreal Institute for Learning Algorithms) and is co-director of the Learning in Machines & Brains prog... |
Yoshua Bengio : Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning (Adaptive Computation and Machine Learning), MIT Press, Cambridge (USA), 2016. ISBN 978-0262035613. Dzmitry Bahdanau; Kyunghyun Cho; Yoshua Bengio (2014). "Neural Machine Translation by Jointly Learning to Align and Translate". arXiv:1409.... |
Abeba Birhane : Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including Image... |
Abeba Birhane : Birhane was born in Ethiopia. She received her Bachelors of Science in Psychology and a Bachelors of Arts in Philosophy from The Open University. In 2015, she completed her Master of Science in Cognitive Science and, in 2021, her Ph.D. at the Complex Software Lab in the School of Computer Science at Uni... |
Abeba Birhane : Birhane studied the impacts of emerging AI technologies and how they shape individuals and local communities. She found that AI algorithms tend to disproportionately impact vulnerable groups such as older workers, trans people, immigrants, and children. Her research on relational ethics won the best pap... |
Abeba Birhane : 2019 NeurIPS Black in AI Workshop Best Paper Award 2020 Venture Beat AI Innovations Award in the category Computer Vision Innovation (received with Vinay Prabhu) 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2022 Lero Director’s Prize for PhD/PostDoctoral Contribution. 2023 100 Most Influen... |
Black in AI : Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases the presence and inclusion ... |
Black in AI : Black in AI was created in 2017 to address issues of lack of diversity in AI workshops, and was started as its own workshop within the Conference on Neural Information Processing Systems (NeurIPS) conference. Because of algorithmic bias, ethical issues, and underrepresentation of Black people in AI roles;... |
Black in AI : Rediet Abebe is an Ethiopian computer scientist who specializes in algorithms and artificial intelligence. She is a Computer Science Assistant Professor at the University of California, Berkeley. She was previously a Junior Fellow at Harvard's Society of Fellows. She was the first Black woman to receive a... |
Black in AI : Black in AI received grants and support from private foundations like MacArthur Foundation and Rockefeller Foundation. The organization received $10,000 in 2018 for its annual workshop and $150,000 in 2019 for its long-term organizational planning. In 2020, during the pandemic, the organization received a... |
Black in AI : "Black in AI works in academics, advocacy, entrepreneurship, financial support, and summer research programs." The Black in AI Academic Program is a resource for Black junior researchers applying to graduate schools, navigating graduate school, and transitioning into the postgraduate employment market. Th... |
Black in AI : At NeurIPS 2017, the first Black in AI event took place in December 8, 2017 in Long Beach, California. The goal was to bring together experts in the area to share ideas and debate efforts aimed at increasing the participation of Black people in artificial intelligence, both for diversity and to avoid data... |
Black in AI : African-American women in computer science Algorithmic bias Data for Black Lives Ethics of artificial intelligence Data Science Africa |
Black in AI : Official website |
David Blei : David Meir Blei is a professor in the Statistics and Computer Science departments at Columbia University. Prior to fall 2014 he was an associate professor in the Department of Computer Science at Princeton University. His work is primarily in machine learning. |
David Blei : His research interests include topic models and he was one of the original developers of latent Dirichlet allocation, along with Andrew Ng and Michael I. Jordan. As of June 18, 2020, his publications have been cited 109,821 times, giving him an h-index of 97. |
David Blei : Blei received the ACM Infosys Foundation Award in 2013. (This award is given to a computer scientist under the age of 45. It has since been renamed the ACM Prize in Computing.) He was named Fellow of ACM "For contributions to the theory and practice of probabilistic topic modeling and Bayesian machine lear... |
David Blei : Homepage Latent Dirichlet Allocation (PDF) Publications ACM-Infosys Foundation Award, 2013 |
Karsten Borgwardt : Karsten Borgwardt (born 1980) is a German computer scientist and biologist specializing in machine learning and computational biology. Since February 2023, he has been a director at the Max Planck Institute of Biochemistry in Martinsried, Germany, where he leads the Department of Machine Learning an... |
Karsten Borgwardt : Borgwardt was born in Kaiserslautern. He obtained a Diplom (equivalent to a master’s degree) in computer science from LMU Munich in 2004 and a Master of Science in biology from the University of Oxford in 2003. In 2007, he obtained his PhD from LMU Munich in computer science. Following a postdoctora... |
Karsten Borgwardt : Borgwardt’s research integrates big data analysis with biomedical research. He develops novel machine learning algorithms to detect patterns and statistical dependencies in large biological and medical datasets. His work aims to enable the automatic generation of new knowledge from big data and to u... |
Karsten Borgwardt : During his studies, he was a scholar of the Stiftung Maximilianeum, and the Bavarian Foundation for the Promotion of the Gifted. Borgwardt received scholarships from the Studienstiftung des deutschen Volkes in 2002 and 2007. His PhD dissertation received the Heinz Schwärtzel Dissertation Award for F... |
Karsten Borgwardt : Weisfeiler-Lehman Graph Kernels (’‘Journal of Machine Learning Research’’, 2011): Introduced an efficient graph kernel based on the Weisfeiler-Lehman algorithm. “Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning” (’‘Nature Medicine’’, 2022): showc... |
Léon Bottou : Léon Bottou (French pronunciation: [leɔ̃ bɔtu]; born 1965) is a researcher best known for his work in machine learning and data compression. His work presents stochastic gradient descent as a fundamental learning algorithm. He is also one of the main creators of the DjVu image compression technology (toge... |
Léon Bottou : Léon Bottou was born in France in 1965. He obtained the Diplôme d'Ingénieur from École Polytechnique in 1987, a Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Supérieure in 1988 and a PhD from Université Paris-Sud in 1991. He then joined the Adaptive Systems Re... |
Léon Bottou : Léon Bottou's personal website Léon Bottou publications indexed by Google Scholar |
Katie Bouman : Katherine Louise Bouman (; born 1989) is an American engineer and computer scientist working in the field of computational imaging. She led the development of an algorithm for imaging black holes, known as Continuous High-resolution Image Reconstruction using Patch priors (CHIRP), and was a member of the... |
Katie Bouman : Bouman grew up in West Lafayette, Indiana. Her father, Charles Bouman, is a professor of electrical and computer engineering and biomedical engineering at Purdue University. As a high school student, Bouman conducted imaging research at Purdue University. She graduated from West Lafayette Junior-Senior H... |
Katie Bouman : After earning her doctorate, Bouman joined Harvard University as a postdoctoral fellow on the Event Horizon Telescope Imaging team. Bouman joined Event Horizon Telescope project in 2013. She led the development of an algorithm for imaging black holes, known as Continuous High-resolution Image Reconstruct... |
Katie Bouman : She was recognized as one of the BBC's 100 women of 2019. In 2024, Bouman was awarded a Sloan Research Fellowship. |
Katie Bouman : Katie Bouman publications indexed by Google Scholar Event Horizon Telescope, C-SPAN, May 16, 2019 |
Leo Breiman : Leo Breiman (January 27, 1928 – July 5, 2005) was a statistician at the University of California, Berkeley. He was the recipient of numerous honors and awards, and was a member of the United States National Academy of Sciences. Breiman's work helped to bridge the gap between statistics and computer scienc... |
Leo Breiman : Shannon–McMillan–Breiman theorem |
Leo Breiman : Leo Breiman obituary, from the University of California, Berkeley Richard A. Olshen "A Conversation with Leo Breiman," Statistical Science Volume 16, Issue 2, 2001 Breiman, L. (2001). "Statistical Modeling: the Two Cultures". Statistical Science. 16 (3): 199–215. doi:10.1214/ss/1009213725. JSTOR 2676681. ... |
Leo Breiman : Leo Breiman at the Mathematics Genealogy Project Leo Breiman from PORTRAITS OF STATISTICIANS A video record of a Leo Breiman's lecture about one of his machine learning techniques |
Selmer Bringsjord : Selmer Bringsjord (born November 24, 1958) is a professor of Computer Science and Cognitive Science and a former chair of the Department of Cognitive Science at Rensselaer Polytechnic Institute. He also holds an appointment in the Lally School of Management & Technology and teaches artificial Intell... |
Selmer Bringsjord : Bringsjord's education includes a B.A. in Philosophy from the University of Pennsylvania and a Ph.D. in Philosophy from Brown University where he studied under Roderick Chisholm. He conducts research in AI as the director of the Rensselaer AI & Reasoning (RAIR) Laboratory. He specializes in the logi... |
Selmer Bringsjord : with Yang, Y. Mental Metalogic: A New, Unifying Theory of Human and Machine Reasoning (Mahwah, NJ: Lawrence Erlbaum).(2007) with Zenzen, M. Superminds: People Harness Hypercomputation, and More (Dordrecht, The Netherlands: Kluwer). (2003) ISBN 978-1402010958 with Ferrucci, D. Artificial Intelligence... |
Selmer Bringsjord : Department of Cognitive Science at Rensselaer Polytechnic Institute Rensselaer AI & Reasoning Lab Selmer Bringsjord Personal web site at Rensselaer Polytechnic Institute |
Tamara Broderick : Tamara Ann Broderick is an American computer scientist at the Massachusetts Institute of Technology. She works on machine learning and Bayesian inference. |
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