1 citations · 1 across the 12 of their papers we have counts for
13 papers
Gate-Based Feature Importance in Simulation-Based Inference for EEG: A Cautionary Tale
Shrivatsa Deshmukh, Sasha Brenner, Thomas R. Knösche +1
Simulation-based inference (SBI) provides a powerful way to fit mechanistic models to data, but offers little insight into which information in the data shapes the inferred paramet…
The Dually Flat Geometry of Planning as Inference
Nikola Milosevic, Asaki Kataoka, Nicolas Hinrichs +2
We present an alternative characterization of the occupancy measure of reinforcement learning, obtained by embedding the planning criterion into the dynamics through a resetting pl…
Active Inference as a Convex Markov Decision Process
Nikola Milosevic, Nicolás Hinrichs, Nico Scherf
Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principl…
Visual Bias in Simulated Users: The Impact of Luminance and Contrast on Reinforcement Learning-based Interaction
Hannah Selder, Charlotte Beylier, Nico Scherf +1
Reinforcement learning (RL) enables simulations of HCI tasks, yet their validity is questionable when performance is driven by visual rendering artifacts distinct from interaction…
Predictive Statistics Shape Emergent World Representations of Grid Walkers
Sasha Brenner, Thomas R. Knösche, Nico Scherf
Next-token predictors often appear to develop internal representations of the latent world and its rules. The probabilistic nature of these models suggests a deep connection betwee…
Stochastic Decision Horizons for Survival-Based Constrained Reinforcement Learning
Nikola Milosevic, Leonard Franz, Daniel Haeufle +3
We propose stochastic decision horizons (SDH), a theoretically grounded framework for survival-based constrained reinforcement learning from per-step violation signals. Rather than…
Predicting Microbial Interactions Using Graph Neural Networks
Elham Gholamzadeh, Kajal Singla, Nico Scherf
Predicting interspecies interactions is a key challenge in microbial ecology, as these interactions are critical to determining the structure and activity of microbial communities.…
The Geometry of Nonlinear Reinforcement Learning
Nikola Milosevic, Nico Scherf
Reward maximization, safe exploration, and intrinsic motivation are often studied as separate objectives in reinforcement learning (RL). We present a unified geometric framework, t…
Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter
Diptabrata Paul, Nikola Milosevic, Nico Scherf +1
Living microorganisms have evolved dedicated sensory machinery to detect environmental perturbations, processing these signals through biochemical networks to guide behavior. Repli…
Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)
Semih Eren, Deniz Kucukahmetler, Nico Scherf
Accurately predicting distributed cortical responses to naturalistic stimuli requires models that integrate visual, auditory and semantic information over time. We present a hierar…
Central Path Proximal Policy Optimization
Nikola Milosevic, Johannes Müller, Nico Scherf
In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be i…
Embedding Safety into RL: A New Take on Trust Region Methods
Nikola Milosevic, Johannes Müller, Nico Scherf
Reinforcement Learning (RL) agents can solve diverse tasks but often exhibit unsafe behavior. Constrained Markov Decision Processes (CMDPs) address this by enforcing safety constra…
Open Problem: Active Representation Learning
Nikola Milosevic, Gesine Müller, Jan Huisken +1
In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observ…