no code implementations • 23 May 2015 • Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojtek Moczydlowski, Alex van Esbroeck
Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable.
no code implementations • NeurIPS 2016 • Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta
For many machine learning problems, there are some inputs that are known to be positively (or negatively) related to the output, and in such cases training the model to respect that monotonic relationship can provide regularization, and makes the model more interpretable.
no code implementations • NeurIPS 2016 • Mahdi Milani Fard, Quentin Cormier, Kevin Canini, Maya Gupta
Practical applications of machine learning often involve successive training iterations with changes to features and training examples.
no code implementations • NeurIPS 2017 • Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network.
no code implementations • NeurIPS 2018 • Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini
We investigate machine learning models that can provide diminishing returns and accelerating returns guarantees to capture prior knowledge or policies about how outputs should depend on inputs.
no code implementations • 25 Sep 2019 • Nathan Zhang, Kevin Canini, Sean Silva, and Maya R. Gupta
We present fast implementations of linear interpolation operators for both piecewise linear functions and multi-dimensional look-up tables.
no code implementations • 9 Feb 2021 • Taman Narayan, Serena Wang, Kevin Canini, Maya Gupta
We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile.