Uncertainty
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
In this paper, we focus on the problem of community detection for a simple two-class Stochastic Blockmodel (SBM).
Bandit Learning with Implicit Feedback Yi Qi
Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's
Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Russ R. Salakhutdinov, LIANHUI Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing
The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs.
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
A contextual bandit is an online learning framework for modeling sequential decision-making problems.
Geometry Based Data Generation
Ofir Lindenbaum, Jay Stanley, Guy Wolf, Smita Krishnaswamy
Geometry-based data generation with SUGAR is motivated by numerous uses in data exploration.
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Potential-based reward shaping incorporates prior domain knowledge in the form of additional rewards provided during training to speed up convergence of reinforcement learning algorithms, without changing the optimal policies (Ng et al. [1999]).
Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E. Turner
This work was undertaken whilst AS was a Visiting Research Fellow with University of Cambridge.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Neural Information Processing Systems http://nips.cc/