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Ted Zadouri

2 accepted papers

2024

Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

ICLR 2024poster

The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. However, conventional MoEs pose challenges at scale due to the need to store all experts in memory. In this paper, we push…

2023

High Probability Bounds for Stochastic Continuous Submodular Maximization

AISTATS 2023poster

We consider maximization of stochastic monotone continuous submodular functions (CSF) with a diminishing return property. Existing algorithms only guarantee the performance in expectation, and do not bound the probability of getting a bad solution. This implies that for a particular run of the algor…

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