Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees
Many scientific and decision-making tasks require learning complex relationships between a set of 𝑝 covariates and a target response, from 𝑁 observed datapoints with 𝑁 ≪ 𝑝. For example, in genomics and precision medicine, there may be thousands or millions of genetic and environmental covariates but...
Main Author: | Agrawal, Raj |
---|---|
Other Authors: | Broderick, Tamara |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/139350 |
Similar Items
-
Provable guarantees on the robustness of decision rules to causal interventions
by: Wang, B, et al.
Published: (2021) -
Practical data-dependent metric compression with provable guarantees
by: Indyk, Piotr, et al.
Published: (2019) -
Robustness evaluation of deep neural networks with provable guarantees
by: Wu, M
Published: (2020) -
Reachability analysis of deep neural networks with provable guarantees
by: Ruan, W, et al.
Published: (2018) -
Safety verification for deep neural networks with provable guarantees
by: Kwiatkowska, M
Published: (2019)