Search Results for author:
Found 7 papers, 3 papers with code
Scribble-Supervised RGB-T Salient Object Detection
To further polish the expanded labels, we propose a prediction module to alleviate the sharpness of boundary.
Adaptively Exploiting d-Separators with Causal Bandits
In this work, we formalize and study this notion of adaptivity, and provide a novel algorithm that simultaneously achieves (a) optimal regret when a d-separator is observed, improving on classical minimax algorithms, and (b) significantly smaller regret than recent causal bandit algorithms when the observed variables are not a d-separator.
Causal Inference on Distribution Functions
Understanding causal relationships is one of the most important goals of modern science.
Causal Inference
Methodology
The Promises of Parallel Outcomes
In contrast to existing proposals in the literature, the roles of multiple outcomes in our key identification assumption are symmetric, hence the name parallel outcomes.
Causal Inference
Methodology
In Search of Robust Measures of Generalization
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
Multi-cause causal inference with unmeasured confounding and binary outcome
Unobserved confounding presents a major threat to causal inference in observational studies.
Methodology
Viewpoint Selection for Photographing Architectures
Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality.
