A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Reduces variance in noisy social outcomes to improve policy evaluation and optimization.
We show social events can be accurately predicted, but often undesirably.
A new framework for causal inference in networked settings.
Social learning can make financial markets inefficient, but individual learning can fix this.
Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of soc…
Optimizes data labeling for causal effect estimation with missing outcomes.
Predictions can shape outcomes, study helps predict these effects.
We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal…
Estimates peer influence effects using embeddings for social networks.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to pred…
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. …
This work shifts focus from prediction to intervention in social systems.
New method estimates treatment effects in complex interference settings.
New algorithm ensures fairness without sacrificing accuracy.
Algorithm improves learning by integrating diverse agents' behaviors.
The paper analyzes frameworks for integrating sustainability into investment decisions.
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Austral…
New method for online learning IC models with node-level feedback.
The damaging effects of hate speech on social media are evident during the last few years, and several organizations, researchers and social media platforms tried to harness them in various ways. Despite these efforts, social media users are still affected by hate speech. The problem is even more apparent to social gro…
The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervis…
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
New algorithm improves group fairness in social classification problems by exploiting performativity.
We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predic…
StepMix estimates mixture models with covariates for social science applications.
New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.
New findings show AI models can't be validated in complex social systems.
Study finds corruption negatively impacts firm performance.
Fairness in algorithmic decision-making processes is attracting increasing concern. When an algorithm is applied to human-related decision-making an estimator solely optimizing its predictive power can learn biases on the existing data, which motivates us the notion of fairness in machine learning. while several differ…
New decompositions misattribute differences between populations, even when outcomes are identical.
The paper examines insurance market dynamics and optimal regulation.
Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting ou…
New method discovers context effects in choice data.
We look at a collection of conjectures with the unifying message that smaller social systems, tend to be less complex and can be aligned better, towards fulfilling their intended objectives. We touch upon a framework, referred to as the four pronged approach that can aid the analysis of social systems. The four prongs …
In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-perf…
We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the…
Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.
Model shows how confidence feedback can lead to different crisis outcomes.
The study redefines algorithmic fairness as a sociotechnical concept.
The high level of attrition and low rate of certification in Massive Open Online Courses (MOOCs) has prompted a great deal of research. Prior researchers have focused on predicting dropout based upon behavioral features such as student confusion, click-stream patterns, and social interactions. However, few studies have…
Paper presents a privacy-preserving algorithm for estimating peer effects using the Ising model.
Develops algorithm to reduce real-world inequality.
New method evaluates multiple social disparities using machine learning.
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to im…
New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences with discovered measures as a treatment or outcome. Our framewo…
Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many users or stakeholders, no fairness consideration is generally taken into account in their design, which …