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Found 8 papers, 0 papers with code
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
We show that an attention unit learns a window that it uses to implement a nearest-neighbors predictor adapted to the landscape of the pretraining tasks.
InfoNCE Loss Provably Learns Cluster-Preserving Representations
The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e. g. the ``dogness'' of a dog, close to each other in the space generated by the representation.
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models
To the best of our knowledge, this is the first linear convergence result for a diffusion based image inpainting algorithm.
PAC Generalization via Invariant Representations
These are representations of the covariates such that the best model on top of the representation is invariant across training environments.
Improved Algorithms for Misspecified Linear Markov Decision Processes
(P1) Its regret after $K$ episodes scales as $K \max \{ \varepsilon_{\text{mis}}, \varepsilon_{\text{tol}} \}$, where $\varepsilon_{\text{mis}}$ is the degree of misspecification and $\varepsilon_{\text{tol}}$ is a user-specified error tolerance.
L1 Regression with Lewis Weights Subsampling
We consider the problem of finding an approximate solution to $\ell_1$ regression while only observing a small number of labels.
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
We propose an algorithm that uses linear function approximation (LFA) for stochastic shortest path (SSP).
Stochastic Linear Bandits with Protected Subspace
We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we interpret as a \textit{protected space}) given only zero-order stochastic oracle access to both the objective itself and protected subspace.
