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06/06/2023
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Research on adversarial robustness is primarily focused on image and tex...
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05/25/2023
Sharpness-Aware Minimization Leads to Low-Rank Features
Sharpness-aware minimization (SAM) is a recently proposed method that mi...
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04/02/2023
Saddle-to-Saddle Dynamics in Diagonal Linear Networks
In this paper we fully describe the trajectory of gradient flow over dia...
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03/02/2023
Penalising the biases in norm regularisation enforces sparsity
Controlling the parameters' norm often yields good generalisation when t...
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03/02/2023
Model agnostic methods meta-learn despite misspecifications
Due to its empirical success on few shot classification and reinforcemen...
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03/02/2023
Quantum Channel Certification with Incoherent Strategies
In the problem of quantum channel certification, we have black box acces...
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02/24/2023
Linearization Algorithms for Fully Composite Optimization
In this paper, we study first-order algorithms for solving fully composi...
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02/17/2023
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability
In this paper, we investigate the impact of stochasticity and large step...
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02/14/2023
A modern look at the relationship between sharpness and generalization
Sharpness of minima is a promising quantity that can correlate with gene...
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10/11/2022
SGD with large step sizes learns sparse features
We showcase important features of the dynamics of the Stochastic Gradien...
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06/20/2022
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation
Understanding the implicit bias of training algorithms is of crucial imp...
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06/13/2022
Towards Understanding Sharpness-Aware Minimization
Sharpness-Aware Minimization (SAM) is a recent training method that reli...
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06/02/2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
The training of neural networks by gradient descent methods is a corners...
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05/12/2022
Sequential algorithms for testing identity and closeness of distributions
What advantage do sequential procedures provide over batch algorithms fo...
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03/03/2022
Accelerated SGD for Non-Strongly-Convex Least Squares
We consider stochastic approximation for the least squares regression pr...
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02/25/2022
ARIA: Adversarially Robust Image Attribution for Content Provenance
Image attribution – matching an image back to a trusted source – is an e...
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02/14/2022
Trace norm regularization for multi-task learning with scarce data
Multi-task learning leverages structural similarities between multiple t...
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11/10/2021
Linear Speedup in Personalized Collaborative Learning
Personalization in federated learning can improve the accuracy of a mode...
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06/17/2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
Understanding the implicit bias of training algorithms is of crucial imp...
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06/10/2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
We introduce the continuized Nesterov acceleration, a close variant of N...
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03/03/2021
On the effectiveness of adversarial training against common corruptions
The literature on robustness towards common corruptions shows no consens...
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02/11/2021
A Continuized View on Nesterov Acceleration
We introduce the "continuized" Nesterov acceleration, a close variant of...
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02/05/2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime
Motivated by the recent successes of neural networks that have the abili...
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10/19/2020
RobustBench: a standardized adversarial robustness benchmark
Evaluation of adversarial robustness is often error-prone leading to ove...
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07/16/2020
Optimal Robust Linear Regression in Nearly Linear Time
We study the problem of high-dimensional robust linear regression where ...
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07/06/2020
Understanding and Improving Fast Adversarial Training
A recent line of work focused on making adversarial training computation...
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07/01/2020
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Constant step-size Stochastic Gradient Descent exhibits two phases: a tr...
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07/01/2020
Online Robust Regression via SGD on the l1 loss
We consider the robust linear regression problem in the online setting w...
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06/23/2020
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
A large body of research has focused on adversarial attacks which requir...
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11/29/2019
Square Attack: a query-efficient black-box adversarial attack via random search
We propose the Square Attack, a new score-based black-box l_2 and l_∞ ad...
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10/01/2019
An Efficient Sampling Algorithm for Non-smooth Composite Potentials
We consider the problem of sampling from a density of the form p(x) ∝(-f...
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07/25/2019
Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity
We present an improved analysis of the Euler-Maruyama discretization of ...
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06/18/2019
Escaping from saddle points on Riemannian manifolds
We consider minimizing a nonconvex, smooth function f on a Riemannian ma...
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02/06/2019
Fast Mean Estimation with Sub-Gaussian Rates
We propose an estimator for the mean of a random vector in R^d that can ...
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02/04/2019
Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...
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11/20/2018
Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provide...
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11/20/2018
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation
In this paper, we study the problems of principal Generalized Eigenvecto...
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02/26/2018
Averaging Stochastic Gradient Descent on Riemannian Manifolds
We consider the minimization of a function defined on a Riemannian manif...
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02/15/2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety ...
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02/21/2017
Stochastic Composite Least-Squares Regression with convergence rate O(1/n)
We consider the minimization of composite objective functions composed o...
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08/29/2016
Robust Discriminative Clustering with Sparse Regularizers
Clustering high-dimensional data often requires some form of dimensional...
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07/08/2016
Optimal Rates of Statistical Seriation
Given a matrix the seriation problem consists in permuting its rows in s...
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02/17/2016
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
We consider the optimization of a quadratic objective function whose gra...
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04/07/2015
