Amortized sampling of the posterior over data is studied, and the asymptotic correctness of a data-free learning objective, relative trajectory balance, is proved for training a diffusion model that samples from this posterior, a problem that existing methods solve only approximately or in restricted cases.
S. Venkatraman, Moksh Jain, Luca Scimeca et al.· Neural Information Processin...· 75 citations· ⚡5
This work benchmarks several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks), and proposes a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer.
Marcin Sendera, Minsu Kim, Sarthak Mittal et al.· Neural Information Processin...· 52 citations· ⚡7
This paper proposes a method to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions, using a single GFlowNet whose sampling policy follows a two-phase process.
T. Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian et al.· Neural Information Processin...· 65 citations· ⚡4
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
This work formalises LH in a core probabilistic programming language (PPL) and gives sufficient syntactic conditions for its prevention, proving that a safe language fragment satisfying these conditions cannot produce likelihood-hacking programs.
Jacek Karwowski, Y. Kaddar, Zihuiwen Ye et al.· arXiv.org· 2 citations