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Z. Abdallah

24 papers hereh-index 121.1k citations52 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author10
  • last author11

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • cs.LG13
  • cs.CL2
  • cs.CV2
  • q-bio.NC2
  • cs.AI1
  • cs.HC1
same name
  • Z. Abdallah — 1 paper, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedTemporal patterns in insulin needs for Type 1 diabetes

8 citations · 23 across the 24 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

VASAE: Naming SAE Dictionary Directions with Vocabulary-Aligned Anchoring

Kairui Zhang, Ziwen Yu, Zahraa S. Abdallah +1

Sparse autoencoders (SAEs) provide useful decompositions of Transformer residual streams, but their learned features are usually named post hoc rather than directly connected to th…

cs.LG2026

Exposure Bias as Epistemic Underidentification in Recursive Forecasting

Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho

Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions. We show this framing is in…

cs.LG2026

Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty

Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho

Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a suffic…

cs.LG2026★ 1 cited

A Regime-Aware Fusion Framework for Time Series Classification

Honey Singh Chauhan, Zahraa S. Abdallah

Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all…

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