We provide a wide range of nearly optimal guarantees for several fundamental problems in robust supervised learning based on a single iterative polynomial filtering algorithm.

In the paper, we study the convergence rates of the maximum likelihood estimator of gating and prompt parameters of the softmax-contaminated MoE.

We introduce MathArena, a new benchmark for evaluating LLMs on recurring math competitions which provide a stream of high-quality uncontaminated problems.