7 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…
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…
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 +1
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…