Search Results for author: Yashas Annadani

Found 13 papers, 5 papers with code

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery

1 code implementation NeurIPS 2023 Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.

Causal Discovery Variational Inference

Differentiable Multi-Target Causal Bayesian Experimental Design

1 code implementation21 Feb 2023 Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.

Causal Discovery Experimental Design

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations NeurIPS 2023 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.

Causal Discovery Experimental Design

Learning Latent Structural Causal Models

no code implementations24 Oct 2022 Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.

Bayesian Inference Image Generation +1

Interventions, Where and How? Experimental Design for Causal Models at Scale

1 code implementation3 Mar 2022 Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer

Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.

Causal Discovery Experimental Design

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Structure by Architecture: Structured Representations without Regularization

no code implementations14 Jun 2020 Felix Leeb, Guilia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling.


Noise Contrastive Variational Autoencoders

no code implementations23 Jul 2019 Octavian-Eugen Ganea, Yashas Annadani, Gary Bécigneul

We take steps towards understanding the "posterior collapse (PC)" difficulty in variational autoencoders (VAEs),~i. e.

Preserving Semantic Relations for Zero-Shot Learning

no code implementations CVPR 2018 Yashas Annadani, Soma Biswas

We devise objective functions to preserve these relations in the embedding space, thereby inducing semanticity to the embedding space.

Attribute Zero-Shot Learning

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

no code implementations14 Nov 2016 Yashas Annadani, Vijayakrishna Naganoor, Akshay Kumar Jagadish, Krishnan Chemmangat

We investigate and analyse the performance of popular CNN architectures (GoogleNet, AlexNet), used for other image classification tasks, when subjected to the task of detecting the selfies on the multimedia platform.

Image Classification

Sliding Dictionary Based Sparse Representation For Action Recognition

no code implementations1 Nov 2016 Yashas Annadani, D L Rakshith, Soma Biswas

This is used to compute the sparse coefficients of the input action sequence which is divided into overlapping windows and each window gives a probability score for each action class.

Action Recognition Temporal Action Localization

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