2 papers
cs.LG2026
Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference
Alexander Yukhimchuk, Andrey Shulga, Mladen Kolar +1
Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers. Split inference partially mitigates this by keeping tokens…
cs.LG2026
Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters
Alexander Yukhimchuk, Mladen Kolar, Martin TakÃ¡Ä +1
Gradient clipping is a standard safeguard for training neural networks under noisy, heavy-tailed stochastic gradients; yet, most clipping rules treat all parameters as vectors and…