Add Meta-Learn TPE sampler (Warm-starting via Task Similarity)#360
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yasumorishima wants to merge 5 commits intooptuna:mainfrom
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Add Meta-Learn TPE sampler (Warm-starting via Task Similarity)#360yasumorishima wants to merge 5 commits intooptuna:mainfrom
yasumorishima wants to merge 5 commits intooptuna:mainfrom
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Implements task similarity-based meta-learning for TPE, based on Watanabe et al. (IJCAI 2023). Transfers knowledge from completed source studies to accelerate optimization on related target tasks. Key features: - Computes task similarity via Total Variation distance on promising regions - Weighted mixture of TPE models across target and source tasks - Falls back to standard TPE during startup trials - No additional dependencies beyond Optuna Benchmark: 98% improvement over standard TPE on shifted quadratic (10 runs x 30 trials, 3 source studies). Closes optuna#190 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
How it works
Benchmark
Standard TPE mean best: 0.9686, Meta-Learn TPE mean best: 0.0183 (98% improvement, 10 runs × 30 trials, 3 source studies on shifted quadratic)
Test plan
Closes #190
🤖 Generated with Claude Code