Search Results for author:
Found 23 papers, 6 papers with code
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation
(I) As the influence of the interaction term between $x$ and $y$ (e. g., $x^\mathrm{T} B y$) on the Lipschitz smooth and strongly convex-concave function $f$ increases, the approaches converge to an optimal solution at a slower rate.
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space.
Ranked #1 on
Model Poisoning
on Fashion-MNIST
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
The challenge is that we have to discover in an unsupervised manner a set of concepts, i. e., A, B and C, that is useful for the explaining the classifier.
Application of Adversarial Examples to Physical ECG Signals
To the best of our knowledge, our work is the first in evaluating the proficiency of adversarial examples for ECGs in a physical setup.
Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution
When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels.
Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions
The convergence rate, that is, the decrease rate of the distance from a search point $m_t$ to the optimal solution $x^*$, is proven to be in $O(\exp( - L / \mathrm{Tr}(H) ))$, where $L$ is the smallest eigenvalue of $H$ and $\mathrm{Tr}(H)$ is the trace of $H$.
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment
In this paper, we propose a novel search strategy for one-shot and sparse propagation NAS, namely AdvantageNAS, which further reduces the time complexity of NAS by reducing the number of search iterations.
Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs
We also carefully design a feature selection method to select proper search terms to predict each component.
Generate (non-software) Bugs to Fool Classifiers
We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model.
Locally Differentially Private Minimum Finding
Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum.
General-to-Detailed GAN for Infrequent Class Medical Images
Deep learning has significant potential for medical imaging.
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples.
Robust Audio Adversarial Example for a Physical Attack
We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world.
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values
A drawback of this naive approach is that the uncertainty in the missing entries is not properly incorporated in the prediction.
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier
In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.
Differentially Private Empirical Risk Minimization with Input Perturbation
Existing differentially private ERM implicitly assumed that the data contributors submit their private data to a database expecting that the database invokes a differentially private mechanism for publication of the learned model.
Differentially Private Chi-squared Test by Unit Circle Mechanism
This paper develops differentially private mechanisms for $\chi^2$ test of independence.
Recommendation with k-anonymized Ratings
In this way, the predictive performance of recommendations based on anonymized ratings can be improved in some settings.
Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier.
Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization
The key property of SAG method is that, given an arbitrary approximate solution, it can provide a non-probabilistic assumption-free bound on the approximation quality under cryptographically secure computation framework.
Neutralized Empirical Risk Minimization with Generalization Neutrality Bound
Currently, machine learning plays an important role in the lives and individual activities of numerous people.
Differentially Private Analysis of Outliers
One is the query to count outliers, which reports the number of outliers that appear in a given subspace.
Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences
In this paper, we propose a general framework for fairness-aware learning that uses f-divergences and that covers most of the dependency measures employed in the existing methods.
