16 citations · 33 across the 13 of their papers we have counts for
13 papers
Quantifying Ranking Uncertainty in LLM Benchmarks
Bitya Neuhof, Yuval Benjamini
Pretrained models are typically ranked on multi-task leaderboards to assess their effectiveness across diverse tasks. Rank confidence intervals were recently introduced as a method…
Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation
Bitya Neuhof, Yuval Benjamini
Pretrained models are often evaluated on multi-task leaderboards to measure their applicability in diverse contexts. However, current methods for aggregating performance across tas…
CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
Lee Carlin, Yuval Benjamini
High-cardinality categorical features are a common characteristic of mixed-type tabular datasets. Existing generative model architectures struggle to learn the complexities of such…
Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations
Yuli Slavutsky, Yuval Benjamini
Zero-shot learning methods typically assume that the new, unseen classes encountered during deployment come from the same distribution as the the classes in the training set. Howev…
Confident Feature Ranking
Bitya Neuhof, Yuval Benjamini
Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to th…
Logistic Regression Equivalence: A Framework for Comparing Logistic Regression Models Across Populations
Guy Ashiri-Prossner, Yuval Benjamini
In this paper we discuss how to evaluate the differences between fitted logistic regression models across sub-populations. Our motivating example is in studying computerized diagno…
Addressing the Between-Group Comparison Problem: Detecting Differences Between Correlation Matrix Populations due to Single-variable Perturbations for Resting State fMRI
Itamar Faran, Michael Peer, Shahar Arzy +1
Resting-state fMRI has been known for decades as a promising method for evaluating cognitive and mental states, both in health and especially in disease, due to its ease of impleme…
Predicting Classification Accuracy When Adding New Unobserved Classes
Yuli Slavutsky, Yuval Benjamini
Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In…
Extrapolating Expected Accuracies for Large Multi-Class Problems
Charles Zheng, Rakesh Achanta, Yuval Benjamini
The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scal…
Better-Than-Chance Classification for Signal Detection
Jonathan D. Rosenblatt, Yuval Benjamini, Roee Gilron +2
The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is signifi…
How many faces can be recognized? Performance extrapolation for multi-class classification
Charles Y. Zheng, Rakesh Achanta, Yuval Benjamini
The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scal…
Estimating mutual information in high dimensions via classification error
Charles Y. Zheng, Yuval Benjamini
Multivariate pattern analyses approaches in neuroimaging are fundamentally concerned with investigating the quantity and type of information processed by various regions of the hum…
The shuffle estimator for explainable variance in fMRI experiments
Yuval Benjamini, Bin Yu
In computational neuroscience, it is important to estimate well the proportion of signal variance in the total variance of neural activity measurements. This explainable variance m…