Research map / Optimization / Optimizers
Safe Adaptive Importance Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use safe adaptive importance sampling instead of fixed importance sampling | Coordinate descent · GLM tasks on rcv1, real-sim, and news20 datasets | objective convergence per iteration | Helped |
| Use safe adaptive importance sampling instead of fixed importance sampling | Coordinate descent · GLM tasks on full datasets | time to reach objective accuracy | Helped |
| Use safe adaptive importance sampling instead of uniform sampling | SGD · Square-loss tasks on rcv1, real-sim, and news20 | objective convergence per iteration | Mixed |
| Use optimal gradient-based sampling instead of uniform sampling | Coordinate descent · GLM tasks | objective convergence per iteration | Helped |
| Use optimal gradient-based sampling instead of uniform sampling | SGD · GLM tasks on the three evaluated datasets | objective convergence per iteration | Helped |
| Use an adaptive stepsize rather than a fixed conservative stepsize (ablation) | Coordinate descent with proposed sampling · GLM tasks | objective convergence per iteration | Helped |
| Use safe gradient bounds to guide sampling rather than sampling without gradient information | Safe adaptive importance sampling · GLM tasks | objective convergence per iteration | Helped |
| Initialize gradient bounds with no special initialization (ablation) | Safe adaptive importance sampling · GLM optimization | objective convergence per iteration | No clear effect |
Closest papers by meaning
- Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes (NeurIPS 2020 · similarity 0.91)
- A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control (AAAI 2020 · similarity 0.90)
- The Sample Complexity of Gradient Descent in Stochastic Convex Optimization (NeurIPS 2024 · similarity 0.86)
- An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions (ICML 2025 · similarity 0.86)
- Sample complexity of stochastic optimization with integer variables (NeurIPS 2026 · similarity 0.85)
- Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems (ICML 2025 · similarity 0.85)
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