A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey Spence, Paul Jenkins, Sara Mathieson, Yun Song
Neural Information Processing Systems http://nips.cc/
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Rico Angell, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
Information-theoretic Limits for Community Detection in Network Models
We analyze the information-theoretic limits for the recovery of node labels in several network models. This includes the Stochastic Block Model, the Exponential Random Graph Model, the Latent Space Model, the Directed Preferential Attachment Model, and the Directed Small-world Model. For the Stochastic Block Model, the non-recoverability condition depends on the probabilities of having edges inside a community, and between different communities. For the Latent Space Model, the non-recoverability condition depends on the dimension of the latent space, and how far and spread are the communities in the latent space. For the Directed Preferential Attachment Model and the Directed Small-world Model, the non-recoverability condition depends on the ratio between homophily and neighborhood size. We also consider dynamic versions of the Stochastic Block Model and the Latent Space Model.
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello, Lorenzo Rosasco
We investigate the efficiency of k-means in terms of both statistical and computational requirements. More precisely, we study a Nystrรถm approach to kernel k-means. We analyze the statistical properties of the proposed method and show that it achieves the same accuracy of exact kernel k-means with only a fraction of computations. Indeed, we prove under basic assumptions that sampling n Nystrรถm landmarks allows to greatly reduce computational costs without incurring in any loss of accuracy. To the best of our knowledge this is the first result of this kind for unsupervised learning.
Provably Correct Automatic Sub-Differentiation for Qualified Programs
Neural Information Processing Systems http://nips.cc/
Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients?
Neural Information Processing Systems http://nips.cc/
Total stochastic gradient algorithms and applications in reinforcement learning
Neural Information Processing Systems http://nips.cc/
Deep State Space Models for Unconditional Word Generation
Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models. However, such feedback is known to introduce systematic biases into the training process and it obscures a principle of generation: committing to global information and forgetting local nuances. We show that a non-autoregressive deep state space model with a clear separation of global and local uncertainty can be built from only two ingredients: An independent noise source and a deterministic transition function. Recent advances on flowbased variational inference can be used to train an evidence lower-bound without resorting to annealing, auxiliary losses or similar measures. The result is a highly interpretable generative model on par with comparable auto-regressive models on the task of word generation.
Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga, Stefanie Jegelka, Andreas Krause
Neural Information Processing Systems http://nips.cc/
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
Neural Information Processing Systems http://nips.cc/