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Found 7 papers, 1 papers with code
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal
By leveraging the FIM diagonal, our approach provides an interpretable, lightweight, and efficient solution for machine unlearning with practical privacy benefits.
Estimating Class Separability of Datasets Using Persistent Homology with Application to LLM Fine-Tuning
This paper proposes a method to estimate the class separability of an unlabeled text dataset by inspecting the topological characteristics of sentence-transformer embeddings of the text.
Learning medical triage from clinicians using Deep Q-Learning
We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases.
Masking schemes for universal marginalisers
We consider the effect of structure-agnostic and structure-dependent masking schemes when training a universal marginaliser (arXiv:1711. 00695) in order to learn conditional distributions of the form $P(x_i |\mathbf x_{\mathbf b})$, where $x_i$ is a given random variable and $\mathbf x_{\mathbf b}$ is some arbitrary subset of all random variables of the generative model of interest.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty.
