no code implementations • 5 May 2024 • Kwangho Kim, Jisu Kim, Edward H. Kennedy
Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects.
1 code implementation • 1 Nov 2023 • Kwangho Kim, Bijan A. Niknam, José R. Zubizarreta
Weighting is a general and often-used method for statistical adjustment.
1 code implementation • 6 Jun 2023 • Kwangho Kim, José R. Zubizarreta
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints.
no code implementations • 15 Jan 2023 • Kwangho Kim, Edward H. Kennedy, José R. Zubizarreta
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios.
1 code implementation • NeurIPS 2020 • Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Kim, Frederic Chazal, Larry Wasserman
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure.
2 code implementations • NeurIPS 2020 • Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frederic Chazal, Larry Wasserman
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure.
Video-based Generative Performance Benchmarking (Contextual Understanding)
1 code implementation • 9 Jul 2019 • Kwangho Kim, Edward H. Kennedy, Ashley I. Naimi
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size.
no code implementations • 7 Dec 2018 • Kwangho Kim, Jisu Kim, Alessandro Rinaldo
We develop a novel algorithm for feature extraction in time series data by leveraging tools from topological data analysis.
no code implementations • 8 Jun 2018 • Kwangho Kim, Jisu Kim, Edward H. Kennedy
In this paper we develop a framework for characterizing causal effects via distributional distances.