Research map / Optimization / Nonconvex and smooth optimization
Stochastic Frank-Wolfe methods
Develops stochastic, variance-reduced and zeroth-order Frank-Wolfe algorithms with convergence rates.
43 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Nonconvex and smooth optimization, keyword cluster “frank · wolfe · conditional”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
2 papers in 2023–24 and 8 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 0.6% to 2.3%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Variance-Reduced and Projection-Free Stochastic Optimization (ICML 2016 · 74 citations)
- 2017 · Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls (NeurIPS 2017 · 34 citations)
- 2018 · Frank-Wolfe with Subsampling Oracle (ICML 2018 · 6 citations)
- 2019 · Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator (ICML 2019 · 18 citations)
- 2020 · Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity (NeurIPS 2020 · 12 citations)
- 2021 · Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets (ICML 2021 · 4 citations)
- 2022 · Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings (ICML 2022 · 1 citation)
- 2024 · Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features (ICML 2024 · 2 citations)
- 2025 · Efficient Quadratic Corrections for Frank-Wolfe Algorithms (NeurIPS 2025 · 0 citations)
- 2026 · Lower Bounds for Frank-Wolfe on Strongly Convex Sets (ICML 2026 · 0 citations)
Earliest papers here
- Variance-Reduced and Projection-Free Stochastic Optimization (ICML 2016 · 74 citations)
- Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes (NeurIPS 2016 · 31 citations)
- Learning Infinite RBMs with Frank-Wolfe (NeurIPS 2016 · 5 citations)
What has been tried
35 papers here report 260 measured changes (a component added, removed or swapped, compared with a baseline): helped 64%, no clear effect 14%, hurt 15%, mixed 7%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Related approaches
Same keyword cluster
- Projection-free conditional gradient (23 papers)
Elsewhere in the topic
- Adaptive first-order convex methods (190 papers)
- Coordinate and Adam convergence analysis (188 papers)
- Stochastic gradient last-iterate analysis (178 papers)
- Nonconvex minimax and constrained methods (100 papers)
- Stochastic convex optimization complexity (94 papers)
- Acceleration and Nesterov dynamics (91 papers)
- Equilibrium learning in zero-sum games (88 papers)
- Stochastic variance reduction (86 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).