3 papers
cs.LG2026
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet, Aleksandar TerziÄ, Michael Hersche +2
Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We prop…
cs.AI2025
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
Aleksandar TerziÄ, Nicolas Menet, Michael Hersche +2
Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the…
cs.LG2025
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Aleksandar TerziÄ, Michael Hersche, Giacomo Camposampiero +3
Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these mode…