Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

LH941

書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡公開度




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡公開度學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用學科排名




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋學科排名

胡言亂語

Conservative Online Convex Optimizationical applications might dissuade potential users from deploying such solutions. In this paper, we study a novel setting, namely ., in which we are optimizing a sequence of convex loss functions under the constraint that we have to perform at least as well as a known default strategy throughout the e

resistant

Knowledge Infused Policy Gradients with?Upper Confidence Bound for?Relational Banditsr, most of the algorithms use flat feature vectors to represent context whereas, in the real world, there is a varying number of objects and relations among them to model in the context. For example, in a music recommendation system, the user context contains what music they listen to, which artists

cinder

Exploiting History Data for?Nonstationary Multi-armed Banditckle the nonstationary MAB setting, i.e., algorithms capable of detecting changes in the environment and re-configuring automatically to the change, has been widening the areas of application of MAB techniques. However, such approaches have the drawback of not reusing information in those settings w

grotto

Deference

hegemony

Learning to Build High-Fidelity and?Robust Environment Modelssimulator) for serving diverse downstream tasks. Different from the environment learning in model-based RL, where the learned dynamics model is only appropriate to provide simulated data for the specific policy, the goal of RL2S is to build a simulator that is of high fidelity when interacting with

Cirrhosis

Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learningbetween these two methods is not well studied, particularly in the context of deep reinforcement learning. In this paper, we study the effects of ensemble and auxiliary tasks when combined with the deep Q-learning algorithm. We perform a case study on ATARI games under limited data constraint. Moreo

CLAN

Multi-agent Imitation Learning with Copulasns, which is essential for understanding physical, social, and team-play systems. However, most existing works on modeling multi-agent interactions typically assume that agents make independent decisions based on their observations, ignoring the complex dependence among agents. In this paper, we pro

Phonophobia

CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraintsts, while acting in a decentralized manner. In this paper, we consider the problem of multi-agent reinforcement learning for a constrained, partially observable Markov decision process – where the agents need to maximize a global reward function subject to both peak and average constraints. We propo