Bayesian Inference
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Creating an agent that performs well across multiple tasks and continuously incorporates new knowledge has been a longstanding goal of research on artificial intelligence.
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu
We theoretically prove that RIL is able to incentivize rational workers to provide high-quality labels.
Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Event datasets involve irregular occurrences of events over the timeline and are prevalent in numerous domains.
Parsimonious Bayesian deep networks
Neural Information Processing Systems http://nips.cc/
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
Existing approaches rely on various local heuristics for enforcing the acyclicity constraint.
Dynamic Network Model from Partial Observations
Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
Neural Information Processing Systems http://nips.cc/
Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides
Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
Remaining a gradient-based method, it is also the first Bayesian model-agnostic meta-learning method applicable to various tasks including reinforcement learning.
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi, Nisheeth Vishnoi
Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning.
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).