Publications by Igor Krawczuk | EPFL Graph Search

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Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs t ...

Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal ad ...

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in ...

Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this wo ...

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...

We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors. This setting includes a broad range of important problems from distributed convex minimi ...

In this paper, we investigate different methods and approaches in order to improve the electrical characteristics of Pt/HfOx/TiN ReRAM devices. We discuss the improvement of the ReRAM electrical characteristics after the insertion of a Hf and Ti buffer lay ...

Igor Krawczuk

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Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Finding Actual Descent Directions For Adversarial Training

DiGress: Discrete Denoising diffusion for graph generation

Graph generative deep learning models with an application to circuit topologies

A Computational Turn in Policy Process Studies: Coevolving Network Dynamics of Policy Change

Multi-ReRAM synapses for artificial neural network training

Proximal Point Imitation Learning

Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

Effect of metal buffer layer and thermal annealing on HfOx-based ReRAMs

Filling gaps in trustworthy development of AI