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Christos Thrampoulidis

5 papers hereh-index 13 citations5 works total

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

author position
  • middle author3
  • last author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL1
same name
  • Christos Thrampoulidis — 26 papers, h 21
  • Christos Thrampoulidis — 16 papers, h 8
  • Christos Thrampoulidis — 11 papers, h 8
  • Christos Thrampoulidis — 8 papers, h 9
  • Christos Thrampoulidis — 8 papers, h 7
  • Christos Thrampoulidis — 5 papers, h 7

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

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Structure Before Collapse: Transient semantic geometry in next-token prediction

Yize Zhao, Isabel Papadimitriou, Christos Thrampoulidis

Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the out…

cs.LG2026

Directional Alignment Mitigates Reward Hacking in Reinforcement Learning for Language Models

Wenlong Deng, Jiaji Huang, Kaan Ozkara +4

Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinfor…

cs.LG2025

Diagonalizing the Softmax: Hadamard Initialization for Tractable Cross-Entropy Dynamics

Connall Garrod, Jonathan P. Keating, Christos Thrampoulidis

Cross-entropy (CE) training loss dominates deep learning practice, yet existing theory often relies on simplifications, either replacing it with squared loss or restricting to conv…

cs.LG2025

Thumb on the Scale: Optimal Loss Weighting in Last Layer Retraining

Nathan Stromberg, Christos Thrampoulidis, Lalitha Sankar

While machine learning models become more capable in discriminative tasks at scale, their ability to overcome biases introduced by training data has come under increasing scrutiny.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.