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Found 6 papers, 1 papers with code
Efficient Quantum Agnostic Improper Learning of Decision Trees
Our algorithm is the first algorithm (classical or quantum) for learning decision trees in polynomial time without membership queries.
Dimensionality Reduction for Categorical Data
We show that FSketch is significantly faster, and the accuracy obtained by using its sketches are among the top for the standard unsupervised tasks of RMSE, clustering and similarity search.
Efficient Binary Embedding of Categorical Data using BinSketch
In this work, we present a dimensionality reduction algorithm, aka.
Quantum Boosting using Domain-Partitioning Hypotheses
Izdebski et al. posed an open question on whether we can boost quantum weak learners that output non-binary hypothesis.
QUINT: Node embedding using network hashing
QUINT is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks.
Efficient Sketching Algorithm for Sparse Binary Data
We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking.
