MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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BoostLoRA grows effective adapter rank linearly via iterative boosting on hard examples with orthogonal low-rank updates, outperforming both single-shot ultra-low-rank adapters and full fine-tuning on math and code tasks with zero added inference overhead.
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Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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BoostLoRA: Growing Effective Rank by Boosting Adapters
BoostLoRA grows effective adapter rank linearly via iterative boosting on hard examples with orthogonal low-rank updates, outperforming both single-shot ultra-low-rank adapters and full fine-tuning on math and code tasks with zero added inference overhead.