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Found 19 papers, 5 papers with code
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.
Faking Fairness via Stealthily Biased Sampling
The focus of this study is to raise an awareness of the risk of malicious decision-makers who fake fairness by abusing the auditing tools and thereby deceiving the social communities.
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set.
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Therefore, we propose a loss function that can improve the separation of the important class by setting the margin only for the important class, called Class-sensitive Additive Angular Margin Loss (CAMRI Loss).
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.
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.
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.
Differentially Private Chi-squared Test by Unit Circle Mechanism
This paper develops differentially private mechanisms for $\chi^2$ test of independence.
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.
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 $H$.
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.
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.
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient and their monotonic transformations
Evolution strategy (ES) is one of promising classes of algorithms for black-box continuous optimization.
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
To reduce the number of simulations required and increase the number of restarts for better local optimum solutions, we propose a new approach referred to as adaptive scenario subset selection (AS3).
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning
We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents.
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
In this study, we consider a continuous min--max optimization problem $\min_{x \in \mathbb{X} \max_{y \in \mathbb{Y}}}f(x, y)$ whose objective function is a black-box.
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems.
