10 papers
Cluster Frequency Conformal Prediction for Local Coverage
Tomer Lavi, Bracha Shapira, Nadav Rappoport
Conformal prediction provides distribution-free coverage guarantees, but in many-class classification it may still under-cover specific classes or subpopulations, preventing safe d…
Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data
Tomer Lavi, Bracha Shapira, Nadav Rappoport
Gradient-boosted trees achieve strong performance on tabular data, yet often leave a long tail of poorly predicted instances. We introduce a Trajectory-based Difficulty Score (TDS)…
Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations
Tomer Lavi, Bracha Shapira, Nadav Rappoport
Ensuring that predicted probabilities align with observed frequencies is critical in high-stakes domains such as clinical decision support, autonomous driving and financial risk as…
EncodeRec: An Embedding Backbone for Recommendation Systems
Guy Hadad, Neomi Rabaev, Bracha Shapira
Recent recommender systems increasingly leverage embeddings from large pre-trained language models (PLMs). However, such embeddings exhibit two key limitations: (1) PLMs are not ex…
SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values
Tomer D. Meirman, Bracha Shapira, Noa Dagan +1
Interpretable risk scores play a vital role in clinical decision support, yet traditional methods for deriving such scores often rely on manual preprocessing, task-specific modelin…
Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira +2
Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language u…