Search Results for author:
Found 8 papers, 5 papers with code
Blind quantum computation for QFT on multi-qubit states
If a client wants to perform quantum computing but she does not have quantum computers with relevant quantum technologies.
Quantum Physics
Towards Improving Spatiotemporal Action Recognition in Videos
Spatiotemporal action recognition deals with locating and classifying actions in videos.
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data.
Point3D: tracking actions as moving points with 3D CNNs
Our Point3D consists of a Point Head for action localization and a 3D Head for action classification.
Combining Doubly Robust Methods and Machine Learning for Estimating Average Treatment Effects for Observational Real-world Data
Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression.
RISE: Robust Individualized Decision Learning with Sensitive Variables
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is prohibited due to reasons such as delayed availability or fairness concerns.
Causal Inference under Data Restrictions
In the third project, we propose a robust individualized decision learning framework with sensitive variables to improve the worst-case outcomes of individuals caused by sensitive variables that are unavailable at the time of decision.
ROBBIE: Robust Bias Evaluation of Large Generative Language Models
In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
