543 citations · 612 across the 34 of their papers we have counts for
7 papers · 1 filter
What Information Buys in Active Inference
Melih Kandemir, Mustafa Mert Çelikok
Information-theoretic exploration is of primary interest in methodological studies of reinforcement learning (RL) and computational cognitive neuroscience, while the established RL…
Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions
Manuel Haußmann, Melih Kandemir
Decisions that weigh several reward signals against each other require the joint distribution of the return vector. Categorical critics scale exponentially in the reward dimension,…
Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors
Emma Lei Hovmand, Jonas Elsborg, Melih Kandemir +1
De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue that discovery is better served…
Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
Magnus Victor Boock, Abdullah Akgül, Mustafa Mert Çelikok +1
We present a new operator-theoretic representation learning framework for offline reinforcement learning that recovers the directed temporal geometry of a controlled Markov process…
Fitted Q-Iteration with Continuous Actions and Adaptive Data: Smoothed Coverage and Minimax Rates
Manuel Haußmann, Manuel Haussmann, Mustafa Mert Çelikok +1
Fitted Q-iteration (FQI) is well understood when the data come from a fixed distribution and the action set is finite, whereas value-based continuous control collects its data adap…
Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards
Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis
Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and div…
Distributional Active Inference
Abdullah Akgül, Gulcin Baykal, Manuel Haußmann +2
Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted…