Hamid Eghbal-zadeh — accepted AI-conference papers

2023

Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation

Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck, Hoan Duc Nguyen, Andrea Huber, Hamid Eghbal-zadeh +4

ICLR 2023top-5%

We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameter…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗

2022

History Compression via Language Models in Reinforcement Learning

Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner +2

ICML 2022spotlight

In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency. To avoid tr…

Cited by 50 Source ↗PDF ↗code ↗Scholar ↗

2021

The balancing principle for parameter choice in distance-regularized domain adaptation

Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler +2

NeurIPS 2021poster

We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the sour…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2019

Mixture Density Generative Adversarial Networks

Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer

CVPR 2019poster

Generative Adversarial Networks have a surprising ability to generate sharp and realistic images, but they are known to suffer from the so-called mode collapse problem. In this paper, we propose a new GAN variant called Mixture Density GAN that overcomes this problem by encouraging the Discriminator…

Cited by 49 PDF ↗code ↗Scholar ↗