DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
- URL: http://arxiv.org/abs/2304.11208v1
- Date: Fri, 21 Apr 2023 18:43:37 GMT
- Title: DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
- Authors: Qiaoyue Tang, Mathias L\'ecuyer
- Abstract summary: We observe that the traditional use of DP with the Adam introduces a bias in the second moment estimation, due to the addition of independent noise in the gradient computation.
This bias leads to a different scaling for low variance parameter updates, that is inconsistent with the behavior of non-private Adam, and Adam's sign descent interpretation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We observe that the traditional use of DP with the Adam optimizer introduces
a bias in the second moment estimation, due to the addition of independent
noise in the gradient computation. This bias leads to a different scaling for
low variance parameter updates, that is inconsistent with the behavior of
non-private Adam, and Adam's sign descent interpretation. Empirically,
correcting the bias introduced by DP noise significantly improves the
optimization performance of DP-Adam.
Related papers
- Technical Report: Full Version of Analyzing and Optimizing Perturbation of DP-SGD Geometrically [7.905629859216635]
We first generalize DP-SGD and theoretically derive the impact of DP noise on the training process.
Our analysis reveals that, in terms of a perturbed gradient, only the noise on direction has eminent impact on the model efficiency.
We design a geometric strategy GeoDP within the DP framework, which perturbs the direction and the magnitude of a gradient.
arXiv Detail & Related papers (2025-04-08T02:26:10Z) - CAdam: Confidence-Based Optimization for Online Learning [35.84013976735154]
We introduce CAdam, a confidence-based optimization strategy that assesses the consistence between the momentum and the gradient for each parameter dimension before deciding on updates.
Our experiments with both synthetic and real-world datasets demonstrate that CAdam surpasses other well-known systems.
In large-scale A/B testing within a live recommendation system, CAdam significantly enhances model performance compared to Adam.
arXiv Detail & Related papers (2024-11-29T12:00:27Z) - DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias
Correction) [0.0]
We propose DP-AdamBC, an optimization algorithm which removes the bias in the second moment estimation and retrieves the expected behaviour of Adam.
DP-AdamBC significantly improves the optimization performance of DP-Adam by up 3.5% in final accuracy in image, text, and graph node classification tasks.
arXiv Detail & Related papers (2023-12-21T23:42:00Z) - DPVIm: Differentially Private Variational Inference Improved [13.761202518891329]
Differentially private (DP) release of multidimensional statistics typically considers an aggregate sensitivity.
Different dimensions of that vector might have widely different magnitudes and therefore DP perturbation disproportionately affects the signal across dimensions.
We observe this problem in the gradient release of the DP-SGD algorithm when using it for variational inference (VI)
arXiv Detail & Related papers (2022-10-28T07:41:32Z) - Understanding AdamW through Proximal Methods and Scale-Freeness [57.47324825501137]
Adam is a generalization of the $ell$ regularizer Adam-$ell$.
AdamW decouples the gradient of Adam-$ell$ from the update rule of Adam-$ell$.
We show that AdamW exhibits an advantage over Adam-$ell$ and the degree to which we expect the gradients of the network to exhibit multiple scales.
arXiv Detail & Related papers (2022-01-31T21:00:55Z) - AdamD: Improved bias-correction in Adam [0.0]
With the default bias-correction, Adam may actually make larger than requested gradient updates early in training.
The default implementation of Adam may be as sensitive as it is to the hyperparameters $beta_1, beta$ partially due to the originally proposed bias correction procedure, and its behavior in early steps.
arXiv Detail & Related papers (2021-10-20T23:55:23Z) - Understanding the Generalization of Adam in Learning Neural Networks
with Proper Regularization [118.50301177912381]
We show that Adam can converge to different solutions of the objective with provably different errors, even with weight decay globalization.
We show that if convex, and the weight decay regularization is employed, any optimization algorithms including Adam will converge to the same solution.
arXiv Detail & Related papers (2021-08-25T17:58:21Z) - Optimization Variance: Exploring Generalization Properties of DNNs [83.78477167211315]
The test error of a deep neural network (DNN) often demonstrates double descent.
We propose a novel metric, optimization variance (OV), to measure the diversity of model updates.
arXiv Detail & Related papers (2021-06-03T09:34:17Z) - Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate
Estimation [29.27760413892272]
Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems.
Currently, most existing methods utilize counterfactual learning to debias recommender systems.
We propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation.
arXiv Detail & Related papers (2021-05-28T06:59:49Z) - Adam$^+$: A Stochastic Method with Adaptive Variance Reduction [56.051001950733315]
Adam is a widely used optimization method for deep learning applications.
We propose a new method named Adam$+$ (pronounced as Adam-plus)
Our empirical studies on various deep learning tasks, including image classification, language modeling, and automatic speech recognition, demonstrate that Adam$+$ significantly outperforms Adam.
arXiv Detail & Related papers (2020-11-24T09:28:53Z) - Private Stochastic Non-Convex Optimization: Adaptive Algorithms and
Tighter Generalization Bounds [72.63031036770425]
We propose differentially private (DP) algorithms for bound non-dimensional optimization.
We demonstrate two popular deep learning methods on the empirical advantages over standard gradient methods.
arXiv Detail & Related papers (2020-06-24T06:01:24Z) - MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of
Gradients [112.00379151834242]
We propose adaptive learning rate principle, in which the running mean of squared gradient in Adam is replaced by a weighted mean, with weights chosen to maximize the estimated variance each coordinate.
This results in faster adaptation, which leads more desirable empirical convergence behaviors.
arXiv Detail & Related papers (2020-06-21T21:47:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.