Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …
Towards a theoretical framework of out-of-distribution generalization
Generalization to out-of-distribution (OOD) data is one of the central problems in modern
machine learning. Recently, there is a surge of attempts to propose algorithms that mainly …
machine learning. Recently, there is a surge of attempts to propose algorithms that mainly …
Can subnetwork structure be the key to out-of-distribution generalization?
Can models with particular structure avoid being biased towards spurious correlation in out-
of-distribution (OOD) generalization? Peters et al.(2016) provides a positive answer for …
of-distribution (OOD) generalization? Peters et al.(2016) provides a positive answer for …
Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Normalizing flows for interventional density estimation
V Melnychuk, D Frauen… - … Conference on Machine …, 2023 - proceedings.mlr.press
Existing machine learning methods for causal inference usually estimate quantities
expressed via the mean of potential outcomes (eg, average treatment effect). However, such …
expressed via the mean of potential outcomes (eg, average treatment effect). However, such …
WOODS: Benchmarks for out-of-distribution generalization in time series
Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …
Understanding and overcoming these failures have led to a research field of Out-of …
Domain Generalization--A Causal Perspective
Machine learning models rely on various assumptions to attain high accuracy. One of the
preliminary assumptions of these models is the independent and identical distribution, which …
preliminary assumptions of these models is the independent and identical distribution, which …
On the benefits of representation regularization in invariance based domain generalization
A crucial aspect of reliable machine learning is to design a deployable system for
generalizing new related but unobserved environments. Domain generalization aims to …
generalizing new related but unobserved environments. Domain generalization aims to …
Whitening convergence rate of coupling-based normalizing flows
Coupling-based normalizing flows (eg RealNVP) are a popular family of normalizing flow
architectures that work surprisingly well in practice. This calls for theoretical understanding …
architectures that work surprisingly well in practice. This calls for theoretical understanding …