57 citations · 69 across the 6 of their papers we have counts for
5 papers · 1 filter
Global Optimization Networks
Sen Zhao, Erez Louidor, Olexander Mangylov +1
We consider the problem of estimating a good maximizer of a black-box function given noisy examples. To solve such problems, we propose to fit a new type of function which we call…
Regularization Strategies for Quantile Regression
Taman Narayan, Serena Wang, Kevin Canini +1
We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinb…
To Trust Or Not To Trust A Classifier
Heinrich Jiang, Been Kim, Melody Y. Guan +1
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has…
Metric-Optimized Example Weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan +1
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections b…
Deep Lattice Networks and Partial Monotonic Functions
Seungil You, David Ding, Kevin Canini +2
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…