Konstantin Voevodski
Authored Publications
Google Publications
Other Publications
Sort By
Local Algorithms for Interactive Clustering
Pranjal Awasthi
Maria Florina Balcan
Journal of Machine Learning Research, 18(2017), pp. 1-35
Preview abstract
We study the design of interactive semi-supervised clustering algorithms. The user supervision that we consider is in the form of cluster split/merge requests; such feedback is easy for users to provide because it only requires a high-level understanding of the clusters. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable properties in many applications. Local changes are desirable because in practice edits of other parts of the clustering are considered churn - changes that are perceived as quality-neutral or quality-negative. We show that in this framework we can still design provably correct algorithms given that our data satisfies natural separability properties. We also show that our framework works well in practice.
View details
Monotonic Calibrated Interpolated Look-Up Tables
Maya Gupta
Andrew Cotter
Jan Pfeifer
Alexander Mangylov
Wojciech Moczydlowski
Alexander van Esbroeck
Journal Machine Learning Research (JMLR)(2016)
Preview abstract
Real-world machine learning applications may require functions to be interpretable and fast to evaluate, in addition to accurate. In particular, guaranteed monotonicity of the learned function can be critical to user trust. We propose meeting these three goals for low-dimensional machine learning problems by learning flexible, monotonic functions using calibrated interpolated look-up tables. We extend the structural risk minimization framework
of lattice regression to train monotonic look-up tables by solving a convex prob-
lem with appropriate linear inequality constraints. In addition, we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data, though this changes the optimization problem to be non-convex. We address large-scale learning through parallelization, mini-batching, and propose random sampling of additive regularizer terms. Experiments on seven real-world problems with five to sixteen features and thousands to millions of training samples show the proposed monotonic functions can achieve state-of-the-art accuracy on practical problems while providing greater transparency to users.
View details
No Results Found
