no code implementations • 31 Jan 2023 • Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp, Baharan Mirzasoleiman
Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality.
no code implementations • 16 Sep 2022 • Diana M. Negoescu, Pasha Khosravi, Shadow Zhao, Nanyu Chen, Parvez Ahammad, Humberto Gonzalez
This opens questions regarding not only which decision-making policies would perform best in practice, but also regarding the impact of different data collection protocols on the performance of various policies trained on the data, or the robustness of policy performance with respect to changes in problem characteristics such as action- or reward- specific delays in observing outcomes.
1 code implementation • 25 Apr 2022 • Siyue Wang, Xiaoyin Chen, Brent J. Frederisy, Benedict A. Mbakogu, Amy D. Kanne, Pasha Khosravi, Wayne B. Hayes
The function of a protein is defined by its interaction partners.
1 code implementation • 21 May 2021 • Eric Wang, Pasha Khosravi, Guy Van Den Broeck
Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed.
no code implementations • 29 Jun 2020 • Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van Den Broeck
As such, handling missing data in decision trees is a well studied problem.
1 code implementation • NeurIPS 2019 • Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van Den Broeck
In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations, as well as moments of any order, of the latter with respect to the former in case of regression.
1 code implementation • 5 Mar 2019 • Pasha Khosravi, Yitao Liang, YooJung Choi, Guy Van Den Broeck
While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge.