1 code implementation • 6 Nov 2024 • Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham
We design a new deep Q-learning algorithm, Artificial Dopamine, to computationally demonstrate that synchronously distributed, per-layer TD errors may be sufficient to learn surprisingly complex RL tasks.
1 code implementation • 25 Jun 2024 • Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-Massoud Farahmand
We provide a theoretical analysis of the learning dynamics of observation reconstruction, latent self-prediction, and TD learning in the presence of distractions and observation functions under linear model assumptions.
no code implementations • 9 Mar 2024 • Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-Massoud Farahmand, Eric Eaton
We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence.
no code implementations • 29 Mar 2023 • Organizers Of QueerInAI, :, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubička, Hang Yuan, Hetvi J, huan zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, ST John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dǒng, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
We present Queer in AI as a case study for community-led participatory design in AI.
1 code implementation • ICLR 2022 • Claas Voelcker, Victor Liao, Animesh Garg, Amir-Massoud Farahmand
However, they tend to be inferior in practice to commonly used maximum likelihood (MLE) based approaches.
1 code implementation • ICLR 2020 • Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting
When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions.