Learning Graphical Models
Transfer of Value Functions via Variational Methods Andrea Tirinzoni Politecnico di Milano nullnullnullnullnullnullnullnullnullnullnullnullnullnullnull
We consider the problem of transferring value functions in reinforcement learning.
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily Fox, Christopher Nemeth
When applied to distributions defined on a constrained space the time-discretization error can dominate when we are near the boundary of the space.
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.
Does mitigating ML's impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, Alexandra Chouldechova
Neural Information Processing Systems http://nips.cc/
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Implicit feedback is widely used in collaborative filtering methods for recommendation.
Rectangular Bounding Process
Xuhui Fan, Bin Li, Scott SIsson
By tailoring a multi-dimensional space (or multi-dimensional array) into a number of rectangular regions, the partition model can fit data using these "blocks" such that the data within each block
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
JING LI, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet
Neural Information Processing Systems http://nips.cc/
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a DRL policy into behaving sub optimally.
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes Andrea Tirinzoni Politecnico di Milano andrea.tirinzoni@polimi.it Xiangli Chen Amazon Robotics
What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization's answer to this question is to use rectangular
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments
Daniel Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit
We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data.