Oceania
Metric-Free Individual Fairness in Online Learning
Our results resolve an open question by Gillen et al. (2018) by showing that online learning under an unknown individual fairness constraint is possible even without assuming a strong parametric form of the
Metric-Free Individual Fairness in Online Learning
Our results resolve an open question by Gillen et al. (2018) by showing that online learning under an unknown individual fairness constraint is possible even without assuming a strong parametric form of the
Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' feature information and the nodes' latent representations. Our contribution is two-fold: (1) We incorporate high-order neighbourhood structure information to generate the latent representations at each node, which vary smoothly over the network.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To alleviate this problem, Neal and Hinton [1998] have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations. Another approach has been proposed by Capp e and Moulines [2009] in which the E-step is replaced by a stochastic approximation step, closely related to stochastic gradient. In this paper, we analyze incremental and stochastic version of the EM algorithm as well as the variance reduced-version of [Chen et al., 2018] in a common unifying framework. We also introduce a new version incremental version, inspired by the SAGA algorithm by Defazio et al. [2014]. We establish non-asymptotic convergence bounds for global convergence. Numerical applications are presented in this article to illustrate our findings.
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Due to state transitions, it is challenging to balance the contribution from each dimension for achieving near-optimality.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Ping Li, Xiaoyun Li, Cun-Hui Zhang
Jaccard similarity to be practical in large-scale settings.
Bridging Machine Learning and Logical Reasoning by Abductive Learning
Wang-Zhou Dai, Qiuling Xu, Yang Yu, Zhi-Hua Zhou
Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes.
