Research
Conformal Prediction for Simulation Models
We proposed an approach for conformal based prediction regions when one has a simulator, and observe pairs (X, Y) exchangeable. We use split conformal and nested conformal inference and tools from set estimation to provide prediction regions even for complex outcome spaces with only a distance measure and some notation of “small ball” structure.
Conformal Prediction for Simulation Models,
Benjamin LeRoy and Chad Schafer.
ICML Workshop on “Distribution-free Uncertainty Quantification” July 2021. Paper: local version (pre-publication)
Conformal Prediction for Simulation Models
We proposed an approach for conformal based prediction regions when one has a simulator, and observe pairs (X, Y) exchangeable. We use split conformal and nested conformal inference and tools from set estimation to provide prediction regions even for complex outcome spaces with only a distance measure and some notation of “small ball” structure.
Conformal Prediction for Simulation Models,
Benjamin LeRoy and Chad Schafer.
ICML Workshop on “Distribution-free Uncertainty Quantification” July 2021. Local version (pre-publication):pdf
Practical Local Conformal Inference
This is on-going work on defining local partitions of the X space to use with local conformal inference to get as close as possible to conditional conformal inference. We utilize recent work on model diagnostics to partition the X space, allowing for application with poor CDE fits (and it also applicable to high dimensional X spaces).
MD-split+
: Practical Local Conformal Inference in High Dimensions,
Benjamin LeRoy* and David Zhao* (*equal contribution).
ICML Workshop on “Distribution-free Uncertainty Quantification” July 2021. ArXiv: 2107.03280
Practical Local Conformal Inference
This is on-going work on defining local partitions of the X space to use with local conformal inference to get as close as possible to conditional conformal inference. We utilize recent work on model diagnostics to partition the X space, allowing for application with poor CDE fits (and it also applicable to high dimensional X spaces).
MD-split+
: Practical Local Conformal Inference in High Dimensions,
Benjamin LeRoy* and David Zhao* (*equal contribution).
ICML Workshop on “Distribution-free Uncertainty Quantification” July 2021. ArXiv: 2107.03280
Tropical Cyclone Prediction Bands
Using data relative to tracks of a little less than 1000 storms from National Oceanic and Atmospheric Administration (
NOAA) we develop a fully data-driven statistical process for the creation of prediction bands around paths. In a parametric boostrap framework, first we simulate potential curves from a noisy extension to a linear model and then leverage statistical depth and geometric structures to create different version of prediction bands. This work is joint with
Niccolò Dalmasso and
Robin Dunn.
A Flexible Pipeline for Prediction of Tropical Cyclone Paths,
Niccolò Dalmasso*, Robin Dunn*, Benjamin LeRoy*, Chad Schafer (* equal contribution).
ICML Workshop (RESEARCH Track) “Climate Change: How can AI Help?” June 1019. ArXiv: 1906.08832
View work on
github, as well a
R
package:
TCpredictionbands
.
Tropical Cyclone Prediction Bands
Using data relative to tracks of a little less than 1000 storms from National Oceanic and Atmospheric Administration (
NOAA) we develop a fully data-driven statistical process for the creation of prediction bands around paths. In a parametric boostrap framework, first we simulate potential curves from a noisy extension to a linear model and then leverage statistical depth and geometric structures to create different version of prediction bands. This work is joint with
Niccolò Dalmasso and
Robin Dunn.
A Flexible Pipeline for Prediction of Tropical Cyclone Paths,
Niccolò Dalmasso*, Robin Dunn*, Benjamin LeRoy*, Chad Schafer (* equal contribution).
ICML Workshop (RESEARCH Track) “Climate Change: How can AI Help?” June 1019. ArXiv: 1906.08832
View work on
github, as well a
R
package:
TCpredictionbands
.
Additional Research
A novel record linkage interface that incorporates group structure to rapidly collect richer labels,
Kayla Frisoli,
Benjamin LeRoy, Rebecca Nugent. In:
2019 IEEE International Conference on Data Science and Advanced Analysics (DSAA),
Paper.
Immune cellular homeostasis in early life is determined by genetic variants of cellular production and turnover, Tania Dubovik, Elina Starosvetsky,
Benjamin LeRoy, Rachelly Normand, Yasmin Admon, Ayelet Alpert, Yishai Ofran, Max G'Sell, Shai S. Shen-Orr, bioRxiv:
256073.
Software
cowpatch
(python
Package)
cowpatch
brings plot aggregation like seen in
R
packages like
cowplot
,
gridExtra
and
patchwork
to
python
, specifically relative to the
ggplot
implimentation in
plotnine
. This package internally leverages svg objects to provide a flexibible but powerful framework to accomplish it's goals.
This package is in collaboration with
Mallory Wang a Statistics Ph.D. student at the University of Michigan.
View package website at
benjaminleroy.github.io/cowpatch, as well as the
python
package on
github
.
cowpatch
(python
Package)
cowpatch
brings plot aggregation like seen in
R
packages like
cowplot
,
gridExtra
and
patchwork
to
python
, specifically relative to the
ggplot
implimentation in
plotnine
. This package internally leverages svg objects to provide a flexibible but powerful framework to accomplish it's goals.
This package is in collaboration with
Mallory Wang a Statistics Ph.D. student at the University of Michigan.
View package website at
benjaminleroy.github.io/cowpatch/, as well as the
python
package on
github
.
EpiCompare
(R
Package)
The goal of
EpiCompare
is to provide the epidemiology community with easy-to-use tools to encourage comparing and assessing epidemics and epidemiology models in a "Time-Free" manner. This package provides the user the ability to compare epidemics and epidemiology models types (across both the "Agent"/"Aggregate" paradigm and the specifical models). All tools attempt to adhere to
tidyverse
/
ggplot2
style to enhance easy of use.
This package is in collaboration with
Shannon Gallagher, Ph.D. at NIH's National Institute of Allergy and Infectious Diseases.
View package website at
skgallagher.github.io/EpiCompare, or the
R
package on
github
.
EpiCompare
(R
Package)
The goal of
EpiCompare
is to provide the epidemiology community with easy-to-use tools to encourage comparing and assessing epidemics and epidemiology models in a "Time-Free" manner. This package provides the user the ability to compare epidemics and epidemiology models types (across both the "Agent"/"Aggregate" paradigm and the specifical models). All tools attempt to adhere to
tidyverse
/
ggplot2
style to enhance easy of use.
This package is in collaboration with
Shannon Gallagher, Ph.D. at NIH's National Institute of Allergy and Infectious Diseases.
View package website at
skgallagher.github.io/EpiCompare, or the
R
package on
github
.