STIMULUS adapts the SPIDER variance-reduction estimator to multi-gradient descent, achieving O(1/T) non-convex convergence and O(n + sqrt(n)/epsilon) sample complexity for multi-objective learning.
Uncertainty-aware search framework for multi-objective bayesian optimization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning
STIMULUS adapts the SPIDER variance-reduction estimator to multi-gradient descent, achieving O(1/T) non-convex convergence and O(n + sqrt(n)/epsilon) sample complexity for multi-objective learning.