Directed Networks
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
Neural Information Processing Systems http://nips.cc/
Automating Bayesian optimization with Bayesian optimization
Gustavo Malkomes, Roman Garnett
Global optimization of expensive, potentially gradient-free functions has long been a critical component of many complex problems in science and engineering.
CanITrustMyFairnessMetric?AssessingFairness withUnlabeledDataandBayesianInference
We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
Suppose an online platform wants to compare a treatment and control policy, e.g.,
697200c9d1710c2799720b660abd11bb-Paper-Conference.pdf
Bayesian model evidence gives a clear criteria for such model selection. However, computing model evidence requires integration over the likelihood, which is challenging, particularly when the likelihood is non-closed-form and/or expensive.
Understanding Square Lossin Training Overparameterized Neural Network Classifiers
Let defined denote conditional (x)= P(y =1|x).
