2 citations · 3 across the 11 of their papers we have counts for
11 papers
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
Yotam Eshel, Guy Hadad, Guy Feigenblat +3
We explore predicting eCommerce user preferences for product aspects such as brand, size, and color - a task we define as Aspect Affinity. Solving this task improves customer under…
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)…
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…
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…
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…
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation
Guy Hadad, Haggai Roitman, Yotam Eshel +2
As new products are emerging daily, recommendation systems are required to quickly adapt to possible new domains without needing extensive retraining. This work presents ``X-Cross'…
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance
Seffi Cohen, Niv Goldshlager, Nurit Cohen-Inger +2
Large Language Models (LLMs) have shown remarkable capabilities across various natural language processing tasks but often struggle to excel uniformly in diverse or complex domains…
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…
FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification
Nurit Cohen-Inger, Lior Rokach, Bracha Shapira +1
Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged…
BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
Nurit Cohen-Inger, Seffi Cohen, Neomi Rabaev +2
As machine learning (ML) systems increasingly impact critical sectors such as hiring, financial risk assessments, and criminal justice, the imperative to ensure fairness has intens…