2008 - Fellow of Alfred P. Sloan Foundation
Adam Tauman Kalai spends much of his time researching Artificial intelligence, Mathematical optimization, Embedding, Constant and Machine learning. Adam Tauman Kalai has researched Artificial intelligence in several fields, including Euclidean space and Natural language processing. His Mathematical optimization research focuses on subjects like Convex set, which are linked to Random coordinate descent, Proximal Gradient Methods, Convex combination and Stochastic gradient descent.
His Embedding research is multidisciplinary, relying on both Word and Programmer. His work on Transfer of learning, Stability, Active learning and Semi-supervised learning as part of general Machine learning research is frequently linked to Decoupling, bridging the gap between disciplines. His Discrete mathematics research incorporates themes from Uniform distribution and Combinatorics, Unit sphere.
His primary scientific interests are in Artificial intelligence, Machine learning, Mathematical optimization, Theoretical computer science and Algorithm. His work deals with themes such as Crowdsourcing and Natural language processing, which intersect with Artificial intelligence. Many of his research projects under Mathematical optimization are closely connected to Lipschitz continuity, Constant and Polynomial with Lipschitz continuity, Constant and Polynomial, tying the diverse disciplines of science together.
The Algorithm study combines topics in areas such as Test data, Computational learning theory and VC dimension. His Unsupervised learning study integrates concerns from other disciplines, such as Supervised learning and Cluster analysis. His research integrates issues of Method of moments, Univariate and Estimator in his study of Applied mathematics.
Adam Tauman Kalai mainly investigates VC dimension, Artificial intelligence, Theoretical computer science, BIOS and Classifier. His study looks at the intersection of VC dimension and topics like Adversarial system with Transduction, Binary classification, Covariate shift and Test. His research ties Machine learning and Artificial intelligence together.
His biological study spans a wide range of topics, including Linear programming, Online algorithm, String searching algorithm and Pruning. Adam Tauman Kalai combines subjects such as Crowdsourcing and Natural language processing with his study of Word. His Natural language processing research is multidisciplinary, relying on both Embedding and Cluster analysis.
His primary scientific interests are in Theoretical computer science, BIOS, Artificial intelligence, Word embedding and Proxy. His Theoretical computer science study incorporates themes from Efficient algorithm, Standard algorithms and String searching algorithm. His BIOS research incorporates a variety of disciplines, including Classifier, Machine learning, Cognitive psychology, Gender bias and Supervised learning.
His research on Artificial intelligence frequently links to adjacent areas such as Natural language processing. Adam Tauman Kalai frequently studies issues relating to Crowdsourcing and Word embedding. Proxy and Semantic representation are frequently intertwined in his study.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi;Kai-Wei Chang;James Zou;Venkatesh Saligrama.
neural information processing systems (2016)
Noise-tolerant learning, the parity problem, and the statistical query model
Avrim Blum;Adam Kalai;Hal Wasserman.
Journal of the ACM (2003)
Online convex optimization in the bandit setting: gradient descent without a gradient
Abraham D. Flaxman;Adam Tauman Kalai;H. Brendan McMahan.
symposium on discrete algorithms (2005)
Efficient algorithms for online decision problems
Adam Kalai;Santosh Vempala.
Journal of Computer and System Sciences (2005)
Beating the hold-out: bounds for K-fold and progressive cross-validation
Avrim Blum;Adam Kalai;John Langford.
conference on learning theory (1999)
Agnostically Learning Halfspaces
Adam Tauman Kalai;Adam R. Klivans;Yishay Mansour;Rocco A. Servedio.
SIAM Journal on Computing (2008)
Trust-based recommendation systems: an axiomatic approach
Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige.
the web conference (2008)
Logarithmic regret algorithms for online convex optimization
Elad Hazan;Adam Kalai;Satyen Kale;Amit Agarwal.
conference on learning theory (2006)
Universal portfolios with and without transaction costs
Avrim Blum;Adam Kalai.
conference on learning theory (1997)
Adaptively Learning the Crowd Kernel
Omer Tamuz;Ce Liu;Serge Belongie;Ohad Shamir.
arXiv: Learning (2011)
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