Stochastic Gradient Descent
A variant of gradient descent that computes gradients and updates parameters using one sample or a small mini-batch at a time. It is more computationally efficient than full-batch gradient descent and is well suited for training on large-scale datasets.
Core Facts
Timeline (last 90 days)
The newly derived stability conditions are complementary to but conceptually distinct from the stability conditions of stochastic gradient descent (SGD).
SGD stability typically concerns step size and the covariance structure of gradient noise, whereas the new conditions directly relate to the frequency and information content of feedback.
For stochastic gradient descent (SGD), the framework either recovers or significantly sharpens all existing bounds known to the authors.
The online estimation algorithm studied in the paper is a single-pass stochastic gradient algorithm with a constant learning rate γ, in the recursive form θ_t = θ_{t-1} + γ * ψ(Y_t, θ_{t-1}), where ψ is the expectile score function.
Stochastic gradient descent (SGD) uses only one sample at a time, which is fast but produces noisy update directions.
Extending the AMP framework from full-batch gradient descent to stochastic gradient descent (SGD) requires handling the additional sampling randomness and data reuse bias.
All Facts (6)
Stochastic gradient descent (SGD) uses only one sample at a time, which is fast but produces noisy update directions.
75%UnverifiedThe newly derived stability conditions are complementary to but conceptually distinct from the stability conditions of stochastic gradient descent (SGD).
50%UnverifiedSGD stability typically concerns step size and the covariance structure of gradient noise, whereas the new conditions directly relate to the frequency and information content of feedback.
50%UnverifiedFor stochastic gradient descent (SGD), the framework either recovers or significantly sharpens all existing bounds known to the authors.
50%UnverifiedThe online estimation algorithm studied in the paper is a single-pass stochastic gradient algorithm with a constant learning rate γ, in the recursive form θ_t = θ_{t-1} + γ * ψ(Y_t, θ_{t-1}), where ψ is the expectile score function.
50%UnverifiedExtending the AMP framework from full-batch gradient descent to stochastic gradient descent (SGD) requires handling the additional sampling randomness and data reuse bias.
50%