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Found 29 papers, 5 papers with code
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Latent diffusion models excel at producing high-quality images from text.
PrivacyGAN: robust generative image privacy
Classical techniques for protecting facial image privacy typically fall into two categories: data-poisoning methods, exemplified by Fawkes, which introduce subtle perturbations to images, or anonymization methods that generate images resembling the original only in several characteristics, such as gender, ethnicity, or facial expression. In this study, we introduce a novel approach, PrivacyGAN, that uses the power of image generation techniques, such as VQGAN and StyleGAN, to safeguard privacy while maintaining image usability, particularly for social media applications.
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
BO-based algorithms are popular in the ML community, as they are used for hyperparameter optimization and more generally for algorithm configuration.
Fairness in generative modeling
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling.
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
Benchmarking Algorithms from Machine Learning for Low-Budget Black-Box Optimization
Machine learning has invaded various domains of computer science, including black-box optimization.
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.
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.
Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants
In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games.
Deep Learning for General Game Playing with Ludii and Polygames
Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games.
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.
EvolGAN: Evolutionary Generative Adversarial Networks
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both.
Tarsier: Evolving Noise Injection in Super-Resolution GANs
More generally, our approach can be used to optimize any method based on noise injection.
Population Control meets Doob's Martingale Theorems: the Noise-free Multimodal Case
We study a test-based population size adaptation (TBPSA) method, inspired from population control, in the noise-free multimodal case.
Versatile Black-Box Optimization
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization.
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.
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 $\mu=1$.
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.
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
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.
Inspirational Adversarial Image Generation
We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image.
Exact Distributed Training: Random Forest with Billions of Examples
We introduce an exact distributed algorithm to train Random Forest models as well as other decision forest models without relying on approximating best split search.
PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application
This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory.
Critical Hyper-Parameters: No Random, No Cry
The selection of hyper-parameters is critical in Deep Learning.
Toward Optimal Run Racing: Application to Deep Learning Calibration
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand.
Better Text Understanding Through Image-To-Text Transfer
Generic text embeddings are successfully used in a variety of tasks.
Automatically Reinforcing a Game AI
In addition, we consider the case in which only one GPP is available - by decomposing this single GPP into several ones through the use of parameters or even simply random seeds.
Learning opening books in partially observable games: using random seeds in Phantom Go
Many artificial intelligences (AIs) are randomized.
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits
Motivated by applications in energy management, this paper presents the Multi-Armed Risk-Aware Bandit (MARAB) algorithm.
