123 research outputs found

    OPT-GAN: Black-Box Global Optimization via Generative Adversarial Nets

    Full text link
    Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles to these methods. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g. Gaussian assumption or separability. Experiments conducted on BBO benchmarking problems and several other benchmarks with diversified landscapes exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.Comment: M. Lu and S. Ning contribute equally. Submitted to IEEE transactions on Neural Networks and Learning System

    Greedy Bayesian Posterior Approximation with Deep Ensembles

    Full text link
    Ensembles of independently trained neural networks are a state-of-the-art approach to estimate predictive uncertainty in Deep Learning, and can be interpreted as an approximation of the posterior distribution via a mixture of delta functions. The training of ensembles relies on non-convexity of the loss landscape and random initialization of their individual members, making the resulting posterior approximation uncontrolled. This paper proposes a novel and principled method to tackle this limitation, minimizing an ff-divergence between the true posterior and a kernel density estimator in a function space. We analyze this objective from a combinatorial point of view, and show that it is submodular with respect to mixture components for any ff. Subsequently, we consider the problem of ensemble construction, and from the marginal gain of the total objective, we derive a novel diversity term for training ensembles greedily. The performance of our approach is demonstrated on computer vision out-of-distribution detection benchmarks in a range of architectures trained on multiple datasets. The source code of our method is publicly available at https://github.com/MIPT-Oulu/greedy_ensembles_training

    International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

    Get PDF
    The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

    Modeling Mutual Influence in Multi-Agent Reinforcement Learning

    Get PDF
    In multi-agent systems (MAS), agents rarely act in isolation but tend to achieve their goals through interactions with other agents. To be able to achieve their ultimate goals, individual agents should actively evaluate the impacts on themselves of other agents' behaviors before they decide which actions to take. The impacts are reciprocal, and it is of great interest to model the mutual influence of agent's impacts with one another when they are observing the environment or taking actions in the environment. In this thesis, assuming that the agents are aware of each other's existence and their potential impact on themselves, I develop novel multi-agent reinforcement learning (MARL) methods that can measure the mutual influence between agents to shape learning. The first part of this thesis outlines the framework of recursive reasoning in deep multi-agent reinforcement learning. I hypothesize that it is beneficial for each agent to consider how other agents react to their behavior. I start from Probabilistic Recursive Reasoning (PR2) using level-1 reasoning and adopt variational Bayes methods to approximate the opponents' conditional policies. Each agent shapes the individual Q-value by marginalizing the conditional policies in the joint Q-value and finding the best response to improving their policies. I further extend PR2 to Generalized Recursive Reasoning (GR2) with different hierarchical levels of rationality. GR2 enables agents to possess various levels of thinking ability, thereby allowing higher-level agents to best respond to less sophisticated learners. The first part of the thesis shows that eliminating the joint Q-value to an individual Q-value via explicitly recursive reasoning would benefit the learning. In the second part of the thesis, in reverse, I measure the mutual influence by approximating the joint Q-value based on the individual Q-values. I establish Q-DPP, an extension of the Determinantal Point Process (DPP) with partition constraints, and apply it to multi-agent learning as a function approximator for the centralized value function. An attractive property of using Q-DPP is that when it reaches the optimum value, it can offer a natural factorization of the centralized value function, representing both quality (maximizing reward) and diversity (different behaviors). In the third part of the thesis, I depart from the action-level mutual influence and build a policy-space meta-game to analyze agents' relationship between adaptive policies. I present a Multi-Agent Trust Region Learning (MATRL) algorithm that augments single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game. The algorithm aims to find a game-theoretic mechanism to adjust the policy optimization steps that force the learning of all agents toward the stable point

    Distribution-Dissimilarities in Machine Learning

    Get PDF
    Any binary classifier (or score-function) can be used to define a dissimilarity between two distributions. Many well-known distribution-dissimilarities are actually classifier-based: total variation, KL- or JS-divergence, Hellinger distance, etc. And many recent popular generative modeling algorithms compute or approximate these distribution-dissimilarities by explicitly training a classifier: e.g. generative adversarial networks (GAN) and their variants. This thesis introduces and studies such classifier-based distribution-dissimilarities. After a general introduction, the first part analyzes the influence of the classifiers' capacity on the dissimilarity's strength for the special case of maximum mean discrepancies (MMD) and provides applications. The second part studies applications of classifier-based distribution-dissimilarities in the context of generative modeling and presents two new algorithms: Wasserstein Auto-Encoders (WAE) and AdaGAN. The third and final part focuses on adversarial examples, i.e. targeted but imperceptible input-perturbations that lead to drastically different predictions of an artificial classifier. It shows that adversarial vulnerability of neural network based classifiers typically increases with the input-dimension, independently of the network topology

    New Directions for Contact Integrators

    Get PDF
    Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282
    corecore