Adaptive Learning Rates for Gradient Boosting Machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gradient Boosting Machines (GBM) is a widely applicable machine learning algorithm that has demonstrated top performance in a variety of fields. In this paper, we explore the potential of adaptive learning rates to achieve accelerated convergence in GBMs. We introduce a novel boosting algorithm called Delta-Bar-Delta (DBD) Boosting that leverages insights from the steepest-descent algorithm of the same name. We show improved performance over the baseline GBM model through a series of experiments. We also show that our proposed DBD boosting algorithm can be conveniently combined with other optimization improvements, such as momentum and Nesterov's Accelerated Gradient. We perform hyperparameter tuning and evaluate our algorithm on series of classification and regression tasks. Our findings demonstrate empirically improved convergence rate compared to existing approaches. Furthermore, we observe and discuss intriguing behaviors related to adaptive learning rates within the context of GBMs, highlighting the intricate dynamics of our proposed method. This research contributes to the ongoing advancement of gradient boosting techniques in machine learning, offering new perspectives and tools for improved convergence and faster training.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it