Statistical Learning
fa3060edb66e6ff4507886f9912e1ab9-AuthorFeedback.pdf
We will also address the concerns shared by R1 and R3 regarding the colors of the graphs (thanks for catching that!).
The Broad Optimality of Profile Maximum Likelihood
Neural Information Processing Systems http://nips.cc/
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Linear regression is one of the most widely used statistical methods, especially in the social sciences [Agresti and Finlay, 2009] and other domains where data comes from humans.
Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
Neural Information Processing Systems http://nips.cc/
Verified Uncertainty Calibration
Ananya Kumar, Percy S. Liang, Tengyu Ma
Applications such as weather forecasting and personalized medicine demand models that output calibrated probability estimates--those representative of the true likelihood of a prediction.
Multi-relational Poincarรฉ Graph Embeddings
In this paper, we propose MuRP, a theoretically inspired method to embed hierarchical multi-relational data in the Poincarรฉ ball model of hyperbolic space. By considering the surface area of a hypersphere of increasing radius centered at a particular point, Euclidean space can be seen to "grow" polynomially,
Multiclass Learning from Contradictions
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
We introduce the notion of learning from contradictions, a.k.a
