2 citations · 4 across the 7 of their papers we have counts for
10 papers
Manifold Regularization for Kernelized LSTD
Xinyan Yan, Krzysztof Choromanski, Byron Boots +1
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL). It is a necessary component of policy iteration and can be used f…
TripleSpin - a generic compact paradigm for fast machine learning computations
Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler +3
We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss o…
On the boosting ability of top-down decision tree learning algorithm for multiclass classification
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski
We analyze the performance of the top-down multiclass classification algorithm for decision tree learning called LOMtree, recently proposed in the literature Choromanska and Langfo…
Fast nonlinear embeddings via structured matrices
Krzysztof Choromanski, Francois Fagan
We present a new paradigm for speeding up randomized computations of several frequently used functions in machine learning. In particular, our paradigm can be applied for improving…
-freeness implies small dichromatic number
Krzysztof Choromanski
We propose a purely combinatorial quadratic time algorithm that for any -vertex -free tournament , where is a directed path of length , finds in a trans…
Fast Online Clustering with Randomized Skeleton Sets
Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu
We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online…