1 citations · 1 across the 12 of their papers we have counts for
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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…