1 code implementation • 31 Jan 2022 • Jayanta Dey, Will LeVine, Haoyin Xu, Ashwin De Silva, Tyler M. Tomita, Ali Geisa, Tiffany Chu, Jacob Desman, Joshua T. Vogelstein
However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.
no code implementations • 27 Jul 2020 • Tyler M. Tomita, Joshua T. Vogelstein
Many algorithms have been proposed for automated learning of suitable distances, most of which employ linear methods to learn a global metric over the feature space.
no code implementations • 25 Sep 2019 • Ronan Perry, Tyler M. Tomita, Jesse Patsolic, Benjamin Falk, Joshua Vogelstein
In particular, DFs dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to permuting feature indices.
1 code implementation • 25 Sep 2019 • Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesus Arroyo, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein
In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices.
2 code implementations • 10 Jun 2015 • Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.