1 citations · 4 across the 23 of their papers we have counts for
12 papers · 1 filter
COCOLogic-V2: Identifying Logical Inconsistencies via Truly Hard-Negatives
David Steinmann, Antonia Wüst, Kristian Kersting +1
While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evaluation is typically limited to s…
Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?
Nils Grandien, David Steinmann, Felix Friedrich +1
Sparse autoencoders (SAEs) have become an important tool for unsupervised concept discovery in large models. To make the resulting feature spaces more interpretable and manageable,…
Imitation learning for clinical decision support in pediatric ECMO
Fateme Golivand, Michael Skinner, Saurabh Mathur +5
Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interventions is crucial for effec…
Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach
Nikhilesh Prabhakar, Varun Balaji, Athresh Karanam +2
Imitation learning is widely used for learning to act in complex environments. While pure neural-based methods handle high dimensional data effectively, they suffer from the requir…
LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking
Lukas Helff, Quentin Delfosse, David Steinmann +6
As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode emerges: LLMs gaming verifi…
Recursive Inference Machines for Neural Reasoning
Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2
Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a ce…
Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining
Rupert Mitchell, Kristian Kersting
Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs. We present Multipole Semantic…
Object Centric Concept Bottlenecks
David Steinmann, Wolfgang Stammer, Antonia Wüst +1
Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandab…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
Where is the Truth? The Risk of Getting Confounded in a Continual World
Florian Peter Busch, Roshni Kamath, Rupert Mitchell +3
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data. In this work, we show that, in a continual learning setting w…
BOWL: A Deceptively Simple Open World Learner
Roshni . R. Kamath, Rupert Mitchell, Subarnaduti Paul +2
Traditional machine learning excels on static benchmarks, but the real world is dynamic and seldom as carefully curated as test sets. Practical applications may generally encounter…
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel +2
Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn opt…