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Found 13 papers, 7 papers with code
Data-Driven Estimation of Heterogeneous Treatment Effects
In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect.
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model.
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM).
Quantifying How Hateful Communities Radicalize Online Users
In this paper, we measure the impact of joining fringe hateful communities in terms of hate speech propagated to the rest of the social network.
Inferring topological transitions in pattern-forming processes with self-supervised learning
The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains.
Emergent Instabilities in Algorithmic Feedback Loops
In this work, we explore algorithmic confounding in collaborative filtering-based recommendation algorithms through teacher-student learning simulations.
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
Detecting Anti-Vaccine Users on Twitter
The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narratives.
DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data.
Inherent Trade-offs in the Fair Allocation of Treatments
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups.
Origins of Algorithmic Instabilities in Crowdsourced Ranking
We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options.
Social and Information Networks Human-Computer Interaction
Learning Fair and Interpretable Representations via Linear Orthogonalization
To reduce human error and prejudice, many high-stakes decisions have been turned over to machine algorithms.
The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange
Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept.
