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Found 8 papers, 6 papers with code
TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility
We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off.
BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-Task Large Language Models
This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs).
Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study
In this work, we develop a hierarchical Bayesian model of subpopulation dynamics that uses baseline covariate information to predict cancer dynamics under treatment, inspired by cancer dynamics in multiple myeloma (MM), where serum M protein is a well-known proxy of tumor burden.
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case
During the first months, the Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population.
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research
Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction.
A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy
Coupling discrete cell-based models with continuous models using hybrid cellular automata is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales.
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations.
Towards personalized computer simulations of breast cancer treatment
Cancer pathology is unique to a given individual, and developing personalized diagnostic and treatment protocols are a primary concern.
