activity
20192026
most citedA statistical framework for efficient out of distribution detection in deep neural networks

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation

Gili Lior, Tzviel Frostig, Gabriel Stanovsky +1

Multilingual benchmarks are central to evaluating large language models (LLMs) across languages, but they suffer from three issues: exhaustive evaluation scales linearly with the n…

stat.ME2024

Robust CATE Estimation Using Novel Ensemble Methods

Oshri Machluf, Tzviel Frostig, Gal Shoham +3

The estimation of Conditional Average Treatment Effects (CATE) is crucial for understanding the heterogeneity of treatment effects in clinical trials. We evaluate the performance o…

stat.ME2024

Causal Responder Detection

Tzviel Frostig, Oshri Machluf, Amitay Kamber +2

We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging rec…

stat.ME2024

Direction Preferring Confidence Intervals

Tzviel Frostig, Yoav Benjamini, Ruth Heller

Confidence intervals (CIs) are instrumental in statistical analysis, providing a range estimate of the parameters. In modern statistics, selective inference is common, where only c…

stat.ME2022

Inferring on joint associations from marginal associations and a reference sample

Tzviel Frostig, Ruth Heller

We present a method to infer on joint regression coefficients obtained from marginal regressions using a reference panel. This type of scenario is common in genetic fine-mapping, w…

cs.LG2021★ 1 cited

A statistical framework for efficient out of distribution detection in deep neural networks

Matan Haroush, Tzviel Frostig, Ruth Heller +1

Background. Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a distribution similar to that of the training set. However, DNNs' predictions are brittle a…