3 citations · 3 across the 8 of their papers we have counts for
10 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
We propose a novel doubly robust neural mean-embedding framework for proxy causal learning (PCL) for continuous and structured treatments. PCL uses proxy variables and bridge funct…
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…
Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Estimating the distribution of outcomes under counterfactual policies is critical for decision-making in domains such as recommendation, advertising, and healthcare. We propose and…
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…
Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation
Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan +1
The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks th…
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan
Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algor…
On Identifiable Polytope Characterization for Polytopic Matrix Factorization
Bariscan Bozkurt, Alper T. Erdogan
Polytopic matrix factorization (PMF) is a recently introduced matrix decomposition method in which the data vectors are modeled as linear transformations of samples from a polytope…