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Found 12 papers, 2 papers with code
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0. 1\% change can cause an overwhelming number of false positives.
Exploring the Sharpened Cosine Similarity
Convolutional layers have long served as the primary workhorse for image classification.
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile
Differential privacy (DP) is the prevailing technique for protecting user data in machine learning models.
Sparse Private LASSO Logistic Regression
LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions.
The Challenge of Differentially Private Screening Rules
Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data analysis, especially in information retrieval problems where n-grams over text with TF-IDF or Okapi feature values are a strong and easy baseline.
A Coreset Learning Reality Check
Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets.
A General Framework for Auditing Differentially Private Machine Learning
We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice.
Neural Bregman Divergences for Distance Learning
Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e. g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space.
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data.
Continuously Generalized Ordinal Regression for Linear and Deep Models
Our method, which we term continuously generalized ordinal logistic, significantly outperforms the standard ordinal logistic model over a thorough set of ordinal regression benchmark datasets.
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
The merit of the proposed method includes: (1) flexible modelling of the non-linear effect of genetic variants to improve statistical power; (2) multiple knockoffs in the input layer to rigorously control false discovery rate; (3) hierarchical layers to substantially reduce the number of weight parameters and activations to improve computational efficiency; (4) de-randomized feature selection to stabilize identified signals.
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
