Tag: machine-learning
All the writing and notes filed under "machine-learning" — 3 entries.
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First-Class Per-Site Importance in Materials: Attention-MIL on a Frozen Pretrained Backbone
Gated attention-MIL on a frozen UMA-s-1p2 backbone for materials property prediction. Top-3 recall 70.3% on a 12-entry curated active-site set vs 9.8% null; AOPC faithfulness win over post-hoc methods. Manuscript in prep for ICML 2027.
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Beyond 37%: When Optimal Stopping Stops Being Useful
The classical secretary problem gives 1/e ≈ 37%, but the rule answers a question almost no one actually asks. Six simulations covering recall, cardinal payoffs, full information, two-sided choice, learned priors, and a market-timing backtest where the naive no-policy baseline beats every stopping rule.
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TempoSurfViT: Surface-Aligned, Temporally-Aware Transformers for Realized Volatility Forecasting
A masked-autoencoder-pretrained ViT that tokenizes IV surfaces along financial axes and consumes a stack of past surfaces to produce calibrated distributional forecasts of realized volatility. Evaluates whether the representation improves on HAR-RV-IV and supports a profitable variance-swap rule. Synthetic-data pipeline validation only, real OptionMetrics results pending.