3 citations · 9 across the 9 of their papers we have counts for
10 papers
MCGrad: Multicalibration at Web Scale
Niek Tax, Lorenzo Perini, Fridolin Linder +5
We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machin…
Measuring multi-calibration
Ido Guy, Daniel Haimovich, Fridolin Linder +4
A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to corresponding predicted probabilities,…
Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives
Ander Artola Velasco, Stratis Tsirtsis, Nastaran Okati +1
State-of-the-art large language models require specialized hardware and substantial energy to operate. As a consequence, cloud-based services that provide access to large language…
Root Cause Analysis of Outliers with Missing Structural Knowledge
William Roy Orchard, Nastaran Okati, Sergio Hernan Garrido Mejia +2
The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting f…
Towards Human-AI Complementarity with Prediction Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi +2
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, t…
On the Within-Group Fairness of Screening Classifiers
Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier…
Improving Expert Predictions with Conformal Prediction
Eleni Straitouri, Lequn Wang, Nastaran Okati +1
Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require…
Differentiable Learning Under Triage
Nastaran Okati, Abir De, Manuel Gomez-Rodriguez
Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage. Under algorithmic triage, a predictive model does not predict all instances but inste…
Classification Under Human Assistance
Abir De, Nastaran Okati, Ali Zarezade +1
Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by…
Computational Approaches for Stochastic Shortest Path on Succinct MDPs
Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady +1
We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic ru…