• Corpus ID: 219636105

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

@article{Bassily2020StabilityOS,
  title={Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses},
  author={Raef Bassily and Vitaly Feldman and Crist'obal Guzm'an and Kunal Talwar},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.06914}
}
Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. (2016) provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to… 

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References

SHOWING 1-10 OF 50 REFERENCES

Data-Dependent Stability of Stochastic Gradient Descent

A data-dependent notion of algorithmic stability for Stochastic Gradient Descent is established, and novel generalization bounds are developed that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient.

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent

This paper introduces a new stability measure called on-average model stability, for which novel bounds controlled by the risks of SGD iterates are developed, which gives the first-ever-known stability and generalization bounds for SGD with even non-differentiable loss functions.

Stability and Generalization of Learning Algorithms that Converge to Global Optima

This work derives black-box stability results that only depend on the convergence of a learning algorithm and the geometry around the minimizers of the loss function that establish novel generalization bounds for learning algorithms that converge to global minima.

Stability and Convergence Trade-off of Iterative Optimization Algorithms

This paper shows that for any iterative algorithm at any iteration, the overall performance is lower bounded by the minimax statistical error over an appropriately chosen loss function class and provides stability upper bounds for the quadratic loss function.

Train faster, generalize better: Stability of stochastic gradient descent

We show that parametric models trained by a stochastic gradient method (SGM) with few iterations have vanishing generalization error. We prove our results by arguing that SGM is algorithmically

Generalization Bounds for Uniformly Stable Algorithms

A tight bound of $O(\gamma^2 + 1/n)$ on the second moment of the generalization error is proved and these results imply substantially stronger generalization guarantees for several well-studied algorithms.

Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

This work provides new algorithms and matching lower bounds for differentially private convex empirical risk minimization assuming only that each data point's contribution to the loss function is Lipschitz and that the domain of optimization is bounded.

Private stochastic convex optimization: optimal rates in linear time

Two new techniques for deriving DP convex optimization algorithms both achieving the optimal bound on excess loss and using O(min{n, n 2/d}) gradient computations are described.

(Near) Dimension Independent Risk Bounds for Differentially Private Learning

This paper shows that under certain assumptions, variants of both output and objective perturbation algorithms have no explicit dependence on p; the excess risk depends only on the L2-norm of the true risk minimizer and that of training points.

Private Convex Empirical Risk Minimization and High-dimensional Regression

This work significantly extends the analysis of the “objective perturbation” algorithm of Chaudhuri et al. (2011) for convex ERM problems, and gives the best known algorithms for differentially private linear regression.