8 citations · 31 across the 13 of their papers we have counts for
16 papers · 1 filter
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performanc…
Provably Robust Model-Centric Explanations for Critical Decision-Making
Cecilia G. Morales, Nicholas Gisolfi, Robert Edman +2
We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be glean…
Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data. In DP, users encode domain knowledge into \emph{labeling functions} (LF), heuristi…
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Cristian Challu, Kin G. Olivares, Gus Welter +1
Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems…
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Benedikt Boecking, Willie Neiswanger, Eric Xing +1
Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alterna…
Self-Reflective Variational Autoencoder
Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski
The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models. However, typical assumptions on the approximate posterior di…