6 papers
From Score Approximation to Distribution Approximation in Score-Based Diffusion Models
Lan V. Truong
Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain incomplete. In particular, alth…
Asymptotic Signal Subspace Recovery in Softmax Attention Models
Lan V. Truong
Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theoretical principles underlying…
On the Induced Norms of Matrices and Grothendieck problems
Lan V. Truong, M. H. Duong
We study the induced matrix norm $\|\bA\|_{q \to r}$, whose exact value has been known only in a few classical cases. Determining this norm has long been regarded as difficult due…
Transformers Learn Robust In-Context Regression under Distributional Uncertainty
Hoang T. H. Cao, Hai D. V. Trinh, Tho Quan +1
Recent work has shown that Transformers can perform in-context learning for linear regression under restrictive assumptions, including i.i.d. data, Gaussian noise, and Gaussian reg…
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima
Lan V. Truong
We study best-arm identification in stochastic multi-armed bandits under the fixed-confidence setting, focusing on instances with multiple optimal arms. Unlike prior work that addr…
On Generalisation Error Bounds for Transformers
Lan V. Truong
In this paper, we establish a collection of covering number bounds for linear function classes under various norm constraints on the inputs and matrices. We then combine these resu…