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researcher

M. Khalili

11 papers hereh-index 547 citations12 works total

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

author position
  • middle author3
  • last author8

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.AI1
  • cs.CL1
  • eess.SP1
same name
  • M. Khalili — 7 papers, h 4
  • M. Khalili — 5 papers, h 12
  • M. Khalili — 2 papers, h 4
  • M. Khalili — 2 papers, h 2
  • M. Khalili — 2 papers, h 2
  • M. Khalili — 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
20242026
most citedECG Signal Denoising Using Multi-scale Patch Embedding and Transformers

1 citations · 2 across the 9 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

WAQS: Weight Absorption for Quadratic Probing and Affine Steering

Ding Zhu, Mohammad Mahdi Khalili

Activation steering provides a lightweight alternative to fine-tuning for controlling large language model behavior. Existing methods, however, often face a trade-off between effic…

cs.LG2026

When Pruning Meets Interpretability: Preserving Sparse Autoencoder Robustness in LLMs

Suchit Gupte, Xueru Zhang, Mohammad Mahdi Khalili

Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large language models (LLMs), yet their reliability under post-hoc model compression remains…

cs.LG2026

tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions

Alessio Mazzetto, Mohammad Mahdi Khalili, Laura Fee Nern +3

We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attribute…

cs.LG2026

Individual Fairness In Strategic Classification

Zhiqun Zuo, Mohammad Mahdi Khalili

Strategic classification, where individuals modify their features to influence machine learning (ML) decisions, presents critical fairness challenges. While group fairness in this…

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