Abstract: In some aspects, the techniques described herein relate to a method including: providing a machine unlearning algorithm, wherein the machine unlearning algorithm is configured to: approximate a final training state of model parameters trained with an unfiltered dataset; approximate a final training state of model parameters trained with a retain dataset; and compute a vector for shifting parameter weights from the final training state of model parameters trained with the unfiltered dataset to the final training state of model parameters trained with the retain dataset; tuning a batch normalization layer of a convolutional neural network included in a machine learning model with the machine unlearning algorithm, wherein parameters of a convolution layer of the convolutional neural network remain fixed; and tuning prompt parameters of a transformer model included in the machine learning model with the machine unlearning algorithm, wherein other parameters of the transformer model remain fixed.

Abstract: In some aspects, the techniques described herein relate to a method including: providing a first datum to a target model, wherein the first datum is retrieved from a forget dataset; providing a sample drawn from Gaussian noise to an original model; computing a first loss, wherein the first loss is based on target model output from processing the first datum and original model output from processing the sample drawn from Gaussian noise; providing a second datum to the target model, wherein the second datum is retrieved from a retain dataset; providing the second datum to the original model as input to the original model; computing a second loss, wherein the second loss is based on target model output from processing the second datum and original model output from processing the second datum; and combining the first loss and the second loss with an alpha weighting to generate a weighted combination.

Abstract: Systems and methods for efficient test-time prediction of model arbitrariness are disclosed. According to an embodiment, a method for efficient test-time estimation of predictive multiplicity may include: (1) receiving, by arbitrariness prediction computer program, a trained machine learning model, wherein the trained machine learning model comprises a plurality of nodes, and each node has a weight; (2) determining, by the arbitrariness prediction computer program, a number of dropout models for the trained machine learning model to generate; (3) creating, by the arbitrariness prediction computer program, the number of dropout models; (4) providing, by the arbitrariness prediction computer program, sample data to each of the dropout models; (5) receiving, by the arbitrariness prediction computer program, an output from each of the dropout models; and (6) determining, by the arbitrariness prediction computer program, an arbitrariness for the trained machine learning model based on the outputs.

Abstract: A mechanism to control the stability and performance of weight-sharing methods for designing neural networks is provided. Network weights and architecture parameters of a super-net, including multiple sub-networks, are adjusted to reduce a loss determined, at least in part, from a sum, over layers of the sub-network, of measures of smoothness based on network weights in the layers. A sub-network of the super-net is selected dependent upon the adjusted architectural parameters.