Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Academic · 1 min

Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds

arXiv:2602.17798v1 Announce Type: new Abstract: Mixture-of-Experts models rely on learned routers to assign tokens to experts, yet standard softmax gating provides no principled mechanism to …

6 views Feb 24

Academic · 1 min

Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning

arXiv:2602.17809v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA enable practical adaptation of large language models but provide no principled uncertainty estimates, leading …

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

7 views Feb 24

Academic · 1 min

On the Sparsifiability of Correlation Clustering: Approximation Guarantees under Edge Sampling

arXiv:2602.13684v1 Announce Type: new Abstract: Correlation Clustering (CC) is a fundamental unsupervised learning primitive whose strongest LP-based approximation guarantees require $\Theta(n^3)$ triangle inequality constraints and …

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

5 views Feb 18

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Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Articles by Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds

Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning

On the Sparsifiability of Correlation Clustering: Approximation Guarantees under Edge Sampling

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