4 papers
Mixing Makes Markovian Contexts Cheap for Linear Bandits
Kaan Buyukkalayci, Osama Hanna, Christina Fragouli
Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts are cheap'' perspective is highly…
Best-Arm Identification with Noisy Actuation
Merve Karakas, Osama Hanna, Lin F. Yang +1
In this paper, we consider a multi-armed bandit (MAB) instance and study how to identify the best arm when arm commands are conveyed from a central learner to a distributed agent o…
ICQuant: Index Coding enables Low-bit LLM Quantization
Xinlin Li, Osama Hanna, Christina Fragouli +1
The rapid deployment of Large Language Models (LLMs) highlights the need for efficient low-bit post-training quantization (PTQ), due to their high memory costs. A key challenge in…
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Tomoyoshi Kimura, Xinlin Li, Osama Hanna +10
Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on…