text:   Integration and Fairness",9,"['Our new paper is now out on Arxiv! <LINK> Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. A project started by Adam Foster a fantastic intern last year at @benevolent_ai who continued to work with us on this.', 'In the paper we demonstrate that counterfactual inference, batch correction, data integration and learning fair representations in a CVAE framework, can all be seen as learning representations that are conditionally independent of a covariate.', 'Previous methods have tried to tackle this problem, such as the CVAE, FairVAE &amp; TrVAE from @fabian_theis lab. Whilst the CVAE does condition on a known label (c), either the encoder or decoder can choose to ignore this, leading to a latent space that separates on condition (c)', 'TrVAE and FairVAE do better than CVAE by introducing an MMD penalty. For CoMP, inspired by the VaMP prior and contrastive learning, we remove the need for any external discrepancy metric (e.g MMD) and use mixtures of the variational posterior alone, demonstrating better mixing! https://t.co/2FhCSLCYUy', 'We apply CoMP to three problems:\n\n1. Aligning cancer cell lines with tumors (RNA-seq data - Celligner problem). CoMP can align latent representations of cancer cell lines and their tumour equivalents. CoMP preserves tumour type and subtype clustering and has better sensitivity. https://t.co/09N22aDq5U', '2. Counterfactual inference.\nWe use PBMC scRNA-seq data treated and untreated with IFNg to ask: ""What would an untreated cell look like if perturbed with IFNg?"" CoMP successfully infers this - Whilst other methods overestimate underexpression or underestimate over expression. https://t.co/bntzSJP4Ri', '3. Fair representation learning \nWhilst solving these biological problems, we noticed parallels with fair representation learning and demonstrate that we can learn fair representations of biased income data that are invariant to Sex, yet still expressive (able to predict income). https://t.co/BHoLqhR1fC', ""The paper has many other neat findings including nice theoretical results demonstrating that CoMP is actually equivalent to an upper bound on a weighted sum of KL-divergences p(c)KL[q(z|c) | q(z|c')].\nCheck it out: https://t.co/7WSnrz8Qvf"", '@jmtomczak VamP prior getting some love.']",21,06,2182