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Found 17 papers, 3 papers with code
Towards Consistency in Adversarial Classification
In this paper, we expose some pathological behaviors specific to the adversarial problem, and show that no convex surrogate loss can be consistent or calibrated in this context.
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
We propose a new conditional dependence measure and a statistical test for conditional independence.
A Dynamical System Perspective for Lipschitz Neural Networks
The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples.
Asymptotic convergence rates for averaging strategies
Averaging the $\mu$ best individuals among the $\lambda$ evaluations is known to provide better estimates of the optimum of a function than just picking up the best.
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients
Robustness to adversarial attacks is typically obtained through expensive adversarial training with Projected Gradient Descent.
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial attacks.
Mixed Nash Equilibria in the Adversarial Examples Game
This paper tackles the problem of adversarial examples from a game theoretic point of view.
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients
In the white-box setting, our defense works by obfuscating the parameters of the random projection.
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to protect neural networks against $\ell_\infty$ adversarial examples offer poor performance against $\ell_2$ adversarial examples and vice versa.
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard.
Equitable and Optimal Transport with Multiple Agents
When there is only one agent, we recover the Optimal Transport problem.
On averaging the best samples in evolutionary computation
With our choice of selection rate, we get a provable regret of order $O(\lambda^{-1})$ which has to be compared with $O(\lambda^{-2/d})$ in the case where $\mu=1$.
Variance Reduction for Better Sampling in Continuous Domains
Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm use a sample drawn according to some probability distribution for approximating the location of an optimum.
Adversarial Attacks on Linear Contextual Bandits
In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior.
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies
In the targeted setting, we are able to reach, with a limited budget of $100, 000$, $100\%$ of success rate with a budget of $6, 662$ queries on average, i. e. we need $800$ queries less than the current state of the art.
Robust Neural Networks using Randomized Adversarial Training
This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular $\ell_\infty$ and $\ell_2$ bounded adversarial examples).
Theoretical evidence for adversarial robustness through randomization
This paper investigates the theory of robustness against adversarial attacks.
