Faster Boosting with Smaller Memory

Julaiti Alafate; Yoav Freund

RECORD

Faster Boosting with Smaller Memory

Julaiti Alafate, Yoav Freund

Machine Learning · Submitted 25 Jan 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,230 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.14 · human participants 0.03 · controlled comparison 0.83 · randomized assignment 0.02 · states quantitative result 0.98 · compares with prior results 0.98 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; show every role.

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small. This is achieved using a combination of three techniques: early stopping, effective sample size, and stratified sampling. Our experiments demonstrate a 10-100 speedup over XGBoost when the training data is too large to fit in memory.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast.	background 1.00	0.06	0.03	asserted 1.00
s1	However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size.	background 0.85	0.12	0.03	asserted 1.00
s2	This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small.	contribution 1.00	0.85	0.83	asserted 1.00
s3	This is achieved using a combination of three techniques: early stopping, effective sample size, and stratified sampling.	method 0.99	0.70	0.07	asserted 0.99
s4	Our experiments demonstrate a 10-100 speedup over XGBoost when the training data is too large to fit in memory.	result 1.00	0.96	0.52	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

What was examined, and what was not

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arXiv listing abstract v3

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full text
figures and tables
references and citing sentences
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P abstract

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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small. This is achieved using a combination of three techniques: early stopping, effective sample size, and stratified sampling. Our experiments demonstrate a 10-100 speedup over XGBoost when the training data is too large to fit in memory.

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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

These methods mostly rely on weights of instances defined in the loss function at each iteration of boosting [ref] .

Minimal Variance Sampling in Stochastic Gradient Boosting · Related work · 0.94

What it cites · 1 sentence without a confident label

Uses the method or tool of 1 paper

We uses a stopping rule based on Theorem 1 in Appendix [ref], which depends on both the mean and the variance of the weighted correlation [ref] .

Sharp Finite-Time Iterated-Logarithm Martingale Concentration · Sequential Analysis · 0.89

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