7 papers
Probabilistic Recurrent Intention Switching Model
Wenyuan Sheng, Hao Zhu, Joschka Boedecker
Inverse reinforcement learning (IRL) recovers reward functions from observed behavior, yet traditional methods assume a single stationary reward that cannot capture goal switching…
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Seyed Mahdi B. Azad, Jasper Hoffmann, Iman Nematollahi +3
Forward-backward (FB) representations provide a powerful framework for learning the successor representation (SR) in continuous spaces by enforcing a low-rank factorization. Howeve…
Fitting Reinforcement Learning Model to Behavioral Data under Bandits
Hao Zhu, Jasper Hoffmann, Baohe Zhang +1
We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attenti…
Disciplined Biconvex Programming
Hao Zhu, Joschka Boedecker
We introduce disciplined biconvex programming (DBCP), a modeling framework for specifying and solving biconvex optimization problems. Biconvex optimization problems arise in variou…
Multi-convex Programming for Discrete Latent Factor Models Prototyping
Hao Zhu, Shengchao Yan, Jasper Hoffmann +1
Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some datas…
Solving Inverse Problem for Multi-armed Bandits via Convex Optimization
Hao Zhu, Joschka Boedecker
We consider the inverse problem of multi-armed bandits (IMAB) that are widely used in neuroscience and psychology research for behavior modelling. We first show that the IMAB probl…