R
Ran El-Yaniv
Researcher at Technion – Israel Institute of Technology
Publications - 138
Citations - 14744
Ran El-Yaniv is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Support vector machine & Competitive analysis. The author has an hindex of 40, co-authored 133 publications receiving 12684 citations. Previous affiliations of Ran El-Yaniv include University of Toronto & Google.
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Deep Active Learning over the Long Tail.
Yonatan Geifman,Ran El-Yaniv +1 more
TL;DR: A novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer shows consistent and overwhelming improvement in sample complexity over passive learning (random sampling) for three datasets: MNIST, CIFar-10, and CIFAR-100.
Proceedings Article
Concept Drift Detection Through Resampling
TL;DR: This work devise a procedure for detecting concept drifts in data-streams that relies on analyzing the empirical loss of learning algorithms by obtaining statistics from the loss distribution by reusing the data multiple times via resampling.
Proceedings ArticleDOI
Competitive analysis of financial games
TL;DR: In the unidirectional conversion problem an on-line player is given the task of converting dollars to yen over some period of time, each day, a new exchange rate is announced and the player must decide how many dollars to convert to minimize the competitive ratio.
Journal ArticleDOI
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests
TL;DR: Novel model transfer-learning methods that refine a decision forest model by considering an ensemble that contains the union of the two forests and exhibit impressive experimental results over a range of problems are proposed.
Journal ArticleDOI
Synthesizing sound textures through wavelet tree learning
TL;DR: A statistical learning algorithm for synthesizing new random instances of natural sounds using a granular method of sonic analysis, which views sound as a series of short, distinct bursts of energy.