Elvis Dohmatob

Florian Bordes

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Michal Drozdzal

Adriana Romero Soriano

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (see more)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30 percent reduction in iterations, all while achieving superior performance compared to prior work.

2025-02-21

ArXiv (preprint)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reyhane Askari Hemmat

Mohammad Pezeshki

Florian Bordes

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Michal Drozdzal

Adriana Romero Soriano

2025-02-01

arXiv (published)

The Pitfalls of Memorization: When Memorization Hurts Generalization

Reza Bayat

Mohammad Pezeshki

David Lopez-Paz

Pascal Vincent

Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (see more)ations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net

The Pitfalls of Memorization: When Memorization Hinders Generalization

Reza Bayat

Mohammad Pezeshki

David Lopez-Paz

Pascal Vincent

Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (see more)ations. This leads to poor generalization when the learned explanations are spurious. In this work, we formalize

2024-10-10

NeurIPS.cc/2024/Workshop/SciForDL (poster)

openreview.net

The Pitfalls of Memorization: When Memorization Hinders Generalization

Reza Bayat

Mohammad Pezeshki

David Lopez-Paz

Pascal Vincent

Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (see more)ations. This leads to poor generalization when the learned explanations are spurious. In this work, we formalize

2024-10-10

NeurIPS.cc/2024/Workshop/SciForDL (poster)

openreview.net

An Effective Theory of Bias Amplification

Arjun Subramonian

Samuel J. Bell

Levent Sagun

Machine learning models may capture and amplify biases present in data, leading to disparate test performance across social groups. To bette… (see more)r understand, evaluate, and mitigate these possible biases, a deeper theoretical understanding of how model design choices and data distribution properties could contribute to bias is needed. In this work, we contribute a precise analytical theory in the context of ridge regression, both with and without random projections, where the former models neural networks in a simplified regime. Our theory offers a unified and rigorous explanation of machine learning bias, providing insights into phenomena such as bias amplification and minority-group bias in various feature and parameter regimes. For example, we demonstrate that there may be an optimal regularization penalty or training time to avoid bias amplification, and there can be fundamental differences in test error between groups that do not vanish with increased parameterization. Importantly, our theoretical predictions align with several empirical observations reported in the literature. We extensively empirically validate our theory on diverse synthetic and semi-synthetic datasets.

2024-10-07

ArXiv (preprint)

An Effective Theory of Bias Amplification

Arjun Subramonian

Samuel J. Bell

Levent Sagun

Machine learning models may capture and amplify biases present in data, leading to disparate test performance across social groups. To bette… (see more)r understand, evaluate, and mitigate these possible biases, a deeper theoretical understanding of how model design choices and data distribution properties could contribute to bias is needed. In this work, we contribute a precise analytical theory in the context of ridge regression, both with and without random projections, where the former models neural networks in a simplified regime. Our theory offers a unified and rigorous explanation of machine learning bias, providing insights into phenomena such as bias amplification and minority-group bias in various feature and parameter regimes. For example, we demonstrate that there may be an optimal regularization penalty or training time to avoid bias amplification, and there can be fundamental differences in test error between groups that do not vanish with increased parameterization. Importantly, our theoretical predictions align with several empirical observations reported in the literature. We extensively empirically validate our theory on diverse synthetic and semi-synthetic datasets.

2024-10-07

ArXiv (preprint)

Strong Model Collapse

Yunzhen Feng

Arjun Subramonian

Julia Kempe

Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised reg… (see more)ression setting and establish the existance of a strong form of the model collapse phenomenon, a critical performance degradation due to synthetic data in the training corpus. Our results show that even the smallest fraction of synthetic data (e.g., as little as 1\% of the total training dataset) can still lead to model collapse: larger and larger training sets do not enhance performance. We further investigate whether increasing model size, an approach aligned with current trends in training large language models, exacerbates or mitigates model collapse. In a simplified regime where neural networks are approximated via random projections of tunable size, we both theoretically and empirically show that larger models can amplify model collapse. Interestingly, our theory also indicates that, beyond the interpolation threshold (which can be extremely high for very large datasets), larger models may mitigate the collapse, although they do not entirely prevent it. Our theoretical findings are empirically verified through experiments on language models and feed-forward neural networks for images.

2024-10-07

ArXiv (preprint)

Strong Model Collapse

Yunzhen Feng

Arjun Subramonian

Julia Kempe

Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised reg… (see more)ression setting and establish the existance of a strong form of the model collapse phenomenon, a critical performance degradation due to synthetic data in the training corpus. Our results show that even the smallest fraction of synthetic data (e.g., as little as 1\% of the total training dataset) can still lead to model collapse: larger and larger training sets do not enhance performance. We further investigate whether increasing model size, an approach aligned with current trends in training large language models, exacerbates or mitigates model collapse. In a simplified regime where neural networks are approximated via random projections of tunable size, we both theoretically and empirically show that larger models can amplify model collapse. Interestingly, our theory also indicates that, beyond the interpolation threshold (which can be extremely high for very large datasets), larger models may mitigate the collapse, although they do not entirely prevent it. Our theoretical findings are empirically verified through experiments on language models and feed-forward neural networks for images.

2024-10-07

ArXiv (preprint)

Strong Model Collapse

Yunzhen Feng

Arjun Subramonian

Julia Kempe

2024-10-07

ArXiv (preprint)