Statistical Learning
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
However, only the smooth setting was considered. To close this gap, we introduce and analyze O-ZD, the first structured finite-difference algorithm for non-smooth black-box optimization.
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
In real-world sequential prediction scenarios, the features (or attributes) of examples are typically high-dimensional and construction of the all features for each example may be expensive or impossible.
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
Instead ofsampling andscoring allpossible structures individually,we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar
Neural Information Processing Systems http://nips.cc/
Thinning for Accelerating the Learning of Point Processes
This paper discusses one of the most fundamental issues about point processes that what is the best sampling method for point processes. We propose thinning as a downsampling method for accelerating the learning of point processes.
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
Key attributes (cf. Figure 1) include the challenging task of classifying doppelganger animals,