7 papers
Normative Networks for Source Separation via Local Plasticity and Dendritic Computation
Bariscan Bozkurt, Efe Ali Gorguner, Francesco Innocenti +1
Blind source separation (BSS) is a natural framework for studying how latent causes may be recovered from sensory mixtures, but deriving online and biologically plausible algorithm…
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
Bariscan Bozkurt, Alexandre Galashov, Dimitri Meunier +3
Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning addresses this problem thro…
Kernel Treatment Effects with Adaptively Collected Data
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asympt…
Density Ratio-Free Doubly Robust Proxy Causal Learning
Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier +2
We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. T…
Density Ratio-based Proxy Causal Learning Without Density Ratios
Bariscan Bozkurt, Ben Deaner, Dimitri Meunier +2
We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accom…
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
Bariscan Bozkurt, Cengiz Pehlevan, Alper T Erdogan
The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticiz…