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Found 5 papers, 1 papers with code
Certifiable Robustness for Naive Bayes Classifiers
We present (i) a linear time algorithm in the number of entries in the dataset that decides whether a test point is certifiably robust for NBC, (ii) an algorithm that counts for each label, the number of cleaned datasets on which the NBC can be trained to predict that label, and (iii) an efficient optimal algorithm that poisons a clean dataset by inserting the minimum number of missing values such that a test point is not certifiably robust for NBC.
Certifiable Robustness for Nearest Neighbor Classifiers
For this setting, we establish a dichotomy in the complexity of certifying robustness w. r. t.
Scalable inference of topic evolution via models for latent geometric structures
We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference.
The Effect of Network Width on the Performance of Large-batch Training
Distributed implementations of mini-batch stochastic gradient descent (SGD) suffer from communication overheads, attributed to the high frequency of gradient updates inherent in small-batch training.
Model-based Pricing for Machine Learning in a Data Marketplace
Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.
