Background Total knee arthroplasty (TKA) is one of the most resource-intensive, high-
volume surgical procedures. Two drivers of the cost of TKAs are duration of surgery (DOS) …

Causal effect estimation is important for many tasks in the natural and social sciences. We
design algorithms for the continuous partial identification problem: bounding the effects of …

Causal treatment effect estimation is a key problem that arises in a variety of real-world
settings, from personalized medicine to governmental policy making. There has been a flurry …

We present fast implementations of linear interpolation operators for piecewise linear
functions and multi-dimensional look-up tables. These operators are common for efficient …

We consider the problem of estimating a good maximizer of a black-box function given noisy
examples. We propose to fit a new type of function called a global optimization network …

Shape-constrained Symbolic Regression integrates prior knowledge about the function
shape into the symbolic regression model. This can be used to enforce that the model has …

In this paper, we study the problem of establishing the accountability and fairness of
transparent machine learning models through monotonicity. Although there have been …

Over the past few decades, machine learning models have been extremely successful. As a
result of axiomatic attribution methods, feature contributions have been explained more …

Submodular functions and variants, through their ability to characterize diversity and
coverage, have emerged as a key tool for data selection and summarization. Many recent …

In recent years, explainable machine learning methods have been very successful. Despite
their success, most explainable machine learning methods are applied to black-box models …