5 citations · 14 across the 14 of their papers we have counts for
15 papers
Smoothed Elicitation Complexity for Approximate -calibration of Discrete Classification Tasks
Jessica Finocchiaro, Victor Ganson, Drona Khurana
One prominent method of evaluating machine learning model trustworthiness is the notion of calibration. In the binary outcome setting, a probabilistic predictor is calibrated if ou…
Structured Prediction with Abstention via the Lovász Hinge
Jessie Finocchiaro, Rafael Frongillo, Enrique Nueve
The Lovász hinge is a convex loss function proposed for binary structured classification, in which k related binary predictions jointly evaluated by a submodular function. Despite…
Three Types of Calibration with Properties and their Semantic and Formal Relationships
Rabanus Derr, Jessie Finocchiaro, Robert C. Williamson
Fueled by discussions around "trustworthiness" and algorithmic fairness, calibration of predictive systems has regained scholars attention. The vanilla definition and understanding…
Contrasting Cost-Agnostic and Cost-Sensitive Losses under Limited Model Capacity via -consistency
Jessica Finocchiaro, Sanket Shah, Milind Tambe
There is a prevalent debate in machine learning about whether practitioners should train models to optimize a task-agnostic objective (e.g., cross entropy) or incorporate the downs…
Bridging Research and Practice Through Conversation: Reflecting on Our Experience
Mayra Russo, Mackenzie Jorgensen, Kristen M. Scott +4
While some research fields have a long history of collaborating with domain experts outside academia, many quantitative researchers do not have natural avenues to meet experts in a…
Robustness of voting mechanisms to external information in expectation
Yiling Chen, Jessie Finocchiaro
Analyses of voting algorithms often overlook informational externalities shaping individual votes. For example, pre-polling information often skews voters towards candidates who ma…
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
Enrique Nueve, Bo Waggoner, Dhamma Kimpara +1
In multiclass classification over outcomes, the outcomes must be embedded into the reals with dimension at least in order to design a consistent surrogate loss that leads…
Using Property Elicitation to Understand the Impacts of Fairness Regularizers
Jessie Finocchiaro
Predictive algorithms are often trained by optimizing some loss function, to which regularization functions are added to impose a penalty for violating constraints. As expected, th…
Consistent Polyhedral Surrogates for Top- Classification and Variants
Jessie Finocchiaro, Rafael Frongillo, Emma Goodwill +1
Top- classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Sever…
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates
Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner
We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In…
The Structured Abstain Problem and the Lovász Hinge
Jessie Finocchiaro, Rafael Frongillo, Enrique Nueve
The Lovász hinge is a convex surrogate recently proposed for structured binary classification, in which binary predictions are made simultaneously and the error is judged by a…
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner
Given a prediction task, understanding when one can and cannot design a consistent convex surrogate loss, particularly a low-dimensional one, is an important and active area of mac…
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Jessie Finocchiaro, Roland Maio, Faidra Monachou +4
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost imp…
An Embedding Framework for Consistent Polyhedral Surrogates
Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner
We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In…
Egocentric Height Estimation
Jessica Finocchiaro, Aisha Urooj Khan, Ali Borji
Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology, is different than exocentric (third-person) vision in some distinguis…