Search Results for author:
Found 10 papers, 7 papers with code
Estimating the Causal Effect of Early ArXiving on Paper Acceptance
However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
This paper proposes a new task to detect commonsense causation between two events in an event sequence (i. e., context), called contextualized commonsense causal reasoning.
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach
We distill several insights from our analysis on study the peer review process with the help of large LMs.
High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration
We propose a novel approach to compute high-resolution (2048x1024 and higher) depths for panoramas that is significantly faster and qualitatively and qualitatively more accurate than the current state-of-the-art method (360MonoDepth).
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses.
Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts
We examine the role of textual data as study units when conducting causal inference by drawing parallels between human subjects and organized texts.
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person.
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Our main finding uncovers a sharp phase transition phenomenon regarding the {intra-class impact: if the SDEs are locally elastic in the sense that the impact is more significant on samples from the same class as the input, the features of the training data become linearly separable, meaning vanishing training loss; otherwise, the features are not separable, regardless of how long the training time is.
Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture
The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision.
Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning
We hope to inspire practitioners in different fields to adopt the powerful tool of Grassmannian learning in their research.
