Apr 21, 2023 · In this work, we propose a general class of unbiased online evolution strategies methods. We analytically and empirically characterize the ...

For first order method, reusing noise always lead to a larger variance (If the noise is reused, averaging gradients cannot reduce the variance of the noise).

Dec 9, 2023 · Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient ...

Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies ... Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies.

May 30, 2024 · In this work, we propose a general class of unbiased online evolution strategies methods. We analytically and empirically characterize the ...

Noise-Reuse Evolution Strategies (NRES) is proposed, a general class of unbiased online evolution strategies methods that show faster convergence than ...

In this work, we propose a general class of unbiased online evolution strategies methods. We analytically and empirically characterize the variance of this ...

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution. A ... 2024. Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies.

Jascha Sohl-Dickstein · Towards General-Purpose In-Context Learning Agents · Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies.

Abstract. We propose an evolution strategies-based algo- rithm for estimating gradients in unrolled com- putation graphs, called ES-Single. Similarly to.