1 code implementation • 7 Dec 2023 • Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.
no code implementations • 29 Aug 2023 • Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step.
1 code implementation • 15 Jun 2023 • Rahil Mehrizi, Arash Mehrjou, Maryana Alegro, Yi Zhao, Benedetta Carbone, Carl Fishwick, Johanna Vappiani, Jing Bi, Siobhan Sanford, Hakan Keles, Marcus Bantscheff, Cuong Nguyen, Patrick Schwab
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function.
3 code implementations • 7 Nov 2022 • Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou
We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
2 code implementations • 31 Oct 2022 • Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou, Jure Leskovec, Patrick Schwab
Traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems.
1 code implementation • 25 Oct 2022 • Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou
Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task.
1 code implementation • 15 Jun 2022 • Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi
By varying the mask we condition on, the model is able to perform video prediction, infilling, and upsampling.
Ranked #2 on Video Generation on BAIR Robot Pushing
no code implementations • 20 Apr 2022 • Arash Mehrjou, Ashkan Soleymani, Annika Buchholz, Jürgen Hetzel, Patrick Schwab, Stefan Bauer
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data.
no code implementations • 15 Jan 2022 • Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf
Model-free and model-based reinforcement learning are two ends of a spectrum.
no code implementations • 29 Oct 2021 • Sumedh A Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf
Extending the successes in supervised learning methods to the reinforcement learning (RL) setting, however, is difficult due to the data generating process - RL agents actively query their environment for data, and the data are a function of the policy followed by the agent.
Out of Distribution (OOD) Detection Reinforcement Learning (RL)
2 code implementations • ICLR 2022 • Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
no code implementations • 8 Jul 2021 • Arash Mehrjou
We establish a connection between federated learning, a concept from machine learning, and mean-field games, a concept from game theory and control theory.
no code implementations • ICML Workshop INNF 2021 • Korbinian Abstreiter, Stefan Bauer, Arash Mehrjou
Score-based methods represented as stochastic differential equations on a continuous time domain have recently proven successful as a non-adversarial generative model.
no code implementations • 29 May 2021 • Korbinian Abstreiter, Sarthak Mittal, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou
In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising.
1 code implementation • 24 Mar 2021 • Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak.
1 code implementation • 7 Oct 2020 • Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf
Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.
no code implementations • 31 Aug 2020 • Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.
no code implementations • 23 Aug 2020 • Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf
A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.
1 code implementation • 6 Jun 2020 • Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf
We provide theoretical results on the class of systems that can be treated with the proposed algorithm and empirically evaluate the effectiveness of our method using an exemplary dynamical system.
no code implementations • 29 Oct 2019 • Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance.
1 code implementation • NeurIPS 2020 • Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV, which simplifies traditional two-stage methods via a dual formulation.
no code implementations • 25 Sep 2019 • Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf
Model-free and model-based reinforcement learning are two ends of a spectrum.
Model-based Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 16 May 2019 • Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf
In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available.
no code implementations • ICLR Workshop LLD 2019 • Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf
Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.
no code implementations • 26 Jan 2019 • Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance.
no code implementations • ICLR 2020 • Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data.
no code implementations • 14 Nov 2018 • Arash Mehrjou, Bernhard Schölkopf
Filtering is a general name for inferring the states of a dynamical system given observations.
no code implementations • 27 May 2018 • Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe, Bernhard Schölkopf
Apart from its application for encoding a sequence of observations, we propose to use the compression achieved by this encoding as a criterion for model selection.
no code implementations • 23 May 2018 • Arash Mehrjou, Mehran Khodabandeh, Greg Mori
This strategy does not make good use of the structure of the dataset at hand and is prone to be misguided by outliers.
1 code implementation • 21 May 2018 • Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen
We present the utility of DEEN in learning the energy, the score function, and in single-step denoising experiments for synthetic and high-dimensional data.
no code implementations • 13 Mar 2018 • Arash Mehrjou
In this note, we show by a simple example how annealing strategy works in GANs.
no code implementations • ICML 2018 • Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf
A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.
no code implementations • ICLR 2018 • Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf
To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
no code implementations • 17 Aug 2017 • Arash Mehrjou
The aim of this paper is to automate the diagnosis process and minimize the human intervention.
no code implementations • 21 May 2017 • Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi
We introduce a novel framework for adversarial training where the target distribution is annealed between the uniform distribution and the data distribution.