Europe
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
These matrix ensembles are defined precisely in section 2.1, but the reader can keep in
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known in advance.
On Robustness to Adversarial Examples and Polynomial Optimization
Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
Neural Information Processing Systems http://nips.cc/
Lexinvariant Language Models Qian Huang
Empirically, we demonstrate that it can indeed attain perplexity comparable to that of a standard language model, given a sufficiently long context.
Hyperbolic Graph Neural Networks
Qi Liu, Maximilian Nickel, Douwe Kiela
Motivatedbyrecent advances ingeometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian manifolds with differentiable exponential and logarithmic maps.
CertifyingRobustnesstoProgrammableDataBiasin DecisionTrees
Datasets can be biased due to societal inequities, human biases, underrepresentation of minorities, etc.
Möbius Transformation for Fast Inner Product Search on Graph
Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li
Neural Information Processing Systems http://nips.cc/
BeyondBlackBoxDensities: ParameterLearningfor theDeviatedComponents
It takes considerable resources to train complex models on a rich data population.
