42 citations · 319 across the 36 of their papers we have counts for
6 papers · 2 filters
TriMap: Large-scale Dimensionality Reduction Using Triplets
Ehsan Amid, Manfred K. Warmuth
We introduce "TriMap"; a dimensionality reduction technique based on triplet constraints, which preserves the global structure of the data better than the other commonly used metho…
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint
Ehsan Amid, Manfred K. Warmuth
We shed new insights on the two commonly used updates for the online -PCA problem, namely, Krasulina's and Oja's updates. We show that Krasulina's update corresponds to a projec…
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. Warmuth, Rohan Anil +1
We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the l…
Adaptive scale-invariant online algorithms for learning linear models
Michał Kempka, Wojciech Kotłowski, Manfred K. Warmuth
We consider online learning with linear models, where the algorithm predicts on sequentially revealed instances (feature vectors), and is compared against the best linear function…
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney +1
In experimental design, we are given a large collection of vectors, each with a hidden response value that we assume derives from an underlying linear model, and we wish to pick a…
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
Ehsan Amid, Manfred K. Warmuth
Expectation-Maximization (EM) is a prominent approach for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the…