Statistical Learning
Model Agnostic Multilevel Explanations
In recent years, post-hoc local instance-level and global dataset-level explainability of black-box models has received a lot of attention.
Random Reshuffling is Not Always Better
In this case, random reshuffling ensures that the noise in some sense "cancels out" over
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
GPUs, as opposed to only CPUs, are often substantial.
Competitive Gradient Descent
Florian Schaefer, Anima Anandkumar
Neural Information Processing Systems http://nips.cc/
Stochastic Normalizing Flows
By invoking ideas from non-equilibrium statistical mechanics we derive an efficient training procedure by which both the sampler's and the flow's parameters can be optimized end-to-end, and by which we can compute
Demystifying Black-box Models with Symbolic Metamodels
Ahmed M. Alaa, Mihaela van der Schaar
Neural Information Processing Systems http://nips.cc/
[ [ Reviewer 1 ] ]
Thank you for your feedback. The loss function should be selected based on the application, e.g., if We will add a discussion on loss functions and regularization in the final manuscript. Thank you for your helpful comments and suggestions. As mentioned in lines 75 and 89, different functional forms are deemed interpretable in different applications. Reviewer 3.) The theoretical justification of our framework was provided in Section 3.1, where we have shown that Our algorithm explores the Pareto front of simplicity vs. predictivity systematically We will add all the suggested references in the final the manuscript.
Mutually Regressive Point Processes
Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Neural Information Processing Systems http://nips.cc/