Bayesian Learning
Does mitigating ML's impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, Alexandra Chouldechova
Neural Information Processing Systems http://nips.cc/
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/
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.
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes.
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Chaitanya Ryali, Gautam Reddy, Angela J. Yu
It has been found that DBM captures human behavior better than FBM, even though the latter more veridically captures experimental design in a variety of tasks, e.g.
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
In many areas of science, we strive to answer questions that are fundamentally causal in nature.
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model.