Statistical Learning
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Neural Information Processing Systems http://nips.cc/
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
The stochastic game (SG) is a classical multi-agent model that has received extensive attention in recent MARL studies.
Probabilistic Logic Neural Networks for Reasoning
However, the inference in MLNs is usually very difficult due to the complicated graph structures.
Statistical-Computational Tradeoffs in High-Dimensional Single Index Models
For the first question, we introduce the notions of first-and second-order Stein's associations which
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
Takashi Takenouchi, Takafumi Kanamori
In this paper, we propose a novel parameter estimator for probabilistic models on discrete space. The proposed estimator is derived from minimization of homogeneous divergence and can be constructed without calculation of the normalization constant, which is frequently infeasible for models in the discrete space. We investigate statistical properties of the proposed estimator such as consistency and asymptotic normality, and reveal a relationship with the information geometry. Some experiments show that the proposed estimator attains comparable performance to the maximum likelihood estimator with drastically lower computational cost.
Learning Representations for Time Series Clustering
Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell
Neural Information Processing Systems http://nips.cc/
Reply to Reviewer # 1
Q1: What other ways to generate fake sequences may be suitable for this problem? A1: That is a good question. GAN to generate some more difficult fake sequences to further improve the ability of the encoder. Q1: Comparison with other state-of-the-art deep clustering methods which are not designed for time-series. A1: Following your suggestion, we compare our method with two state-of-the-art deep clustering methods (i.e., DEC (Xie et al., Table 1: Comparisons on 36 time series datasets (The No. of datasets is consistent with the one in Table 2 in main text)Dataset DEC(RI) IDEC(RI) DTCR(RI) DTCR(NMI) DTCR(ACC) Dataset DEC(RI) IDEC(RI) DTCR(RI) DTCR(NMI) DTCR(ACC)1 0.5817 0.6210 0.6868(0.0026)
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS Han Shi
However, it is computationally very costly. As a remedy, the one-shot approach has emerged as a popular technique for accelerating NAS using weight-sharing.