1 citations · 1 across the 15 of their papers we have counts for
16 papers
Limitations of Synthetic Data Generation in Specialized Data-Scarce Domains
Edward Zhang, Marcel Hussing, Tanay Tandon +12
Advances in diffusion-based generative models have motivated the use of synthetic image generation to alleviate data scarcity in vision tasks. While this strategy has shown promise…
Behavior-Consistent Deep Reinforcement Learning
Marcel Hussing, Liv G. d'Aliberti, Claas Voelcker +2
Reinforcement learning (RL) often exhibits high variance across training runs, leading to unreliable performance and posing a major challenge to deployment in real-world domains. I…
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2
Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years…
Model Agreement via Anchoring
Eric Eaton, Surbhi Goel, Marcel Hussing +4
Numerous lines of aim to control -- the extent to which two machine learning models disagree in their predictions. We adopt a simple and standard noti…
A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models
Jason Hughes, Marcel Hussing, Edward Zhang +24
This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmann…
Iterative Compositional Data Generation for Robot Control
Anh-Quan Pham, Marcel Hussing, Shubhankar P. Patankar +3
Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of tasks that arise in multi-object, multi-ro…
Replicable Reinforcement Learning with Linear Function Approximation
Eric Eaton, Marcel Hussing, Michael Kearns +3
Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learnin…
Future Slot Prediction for Unsupervised Object Discovery in Surgical Video
Guiqiu Liao, Matjaz Jogan, Marcel Hussing +3
Object-centric slot attention is an emerging paradigm for unsupervised learning of structured, interpretable object-centric representations (slots). This enables effective reasonin…
FORLA: Federated Object-centric Representation Learning with Slot Attention
Guiqiu Liao, Matjaz Jogan, Eric Eaton +1
Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require fea…
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
Eric Eaton, Marcel Hussing, Michael Kearns +3
In traditional reinforcement learning (RL), the learner aims to solve a single objective optimization problem: find the policy that maximizes expected reward. However, in many real…
Slot-BERT: Self-supervised Object Discovery in Surgical Video
Guiqiu Liao, Matjaz Jogan, Marcel Hussing +5
Object-centric slot attention is a powerful framework for unsupervised learning of structured and explainable representations that can support reasoning about objects and actions,…
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
Claas A Voelcker, Marcel Hussing, Eric Eaton
Empirical, benchmark-driven testing is a fundamental paradigm in the current RL community. While using off-the-shelf benchmarks in reinforcement learning (RL) research is a common…
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas A Voelcker, Marcel Hussing, Eric Eaton +2
Building deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sample efficiency, recent work has explored…
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video
Guiqiu Liao, Matjaz Jogan, Sai Koushik +2
Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring an extensive training dataset of object masks, relying instead…
Distributed Continual Learning
Long Le, Marcel Hussing, Eric Eaton
This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share know…
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence
Marcel Hussing, Claas Voelcker, Igor Gilitschenski +2
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…