48 citations · 235 across the 17 of their papers we have counts for
17 papers
Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods
Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4
Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate opt…
BMFM-RNA: whole-cell expression decoding improves transcriptomic foundation models
Michael M. Danziger, Bharath Dandala, Viatcheslav Gurev +12
Transcriptomic foundation models pretrained with masked language modeling can achieve low pretraining loss yet produce poor cell representations for downstream tasks. We introduce…
Does your model understand genes? A benchmark of gene properties for biological and text models
Yoav Kan-Tor, Michael Morris Danziger, Eden Zohar +2
The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying…
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani +2
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in…
Propensity score models are better when post-calibrated
Rom Gutman, Ehud Karavani, Yishai Shimoni
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, es…
A discriminative approach for finding and characterizing positivity violations using decision trees
Ehud Karavani, Peter Bak, Yishai Shimoni
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds…