Learning Graphical Models
9dc5accb1e4f4a9798eae145f2e4869b-Supplemental-Conference.pdf
Both challenges involve modeling long-term dependencies.
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization Clément Bénard 1 Brian Staber 1 Sébastien Da Veiga 2 1
Stein thinning is a promising algorithm proposed by Riabiz et al. [2022] for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference.
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen
We study causal representation learning, the task of inferring latent causal variables and their causal relations from high-dimensional functions ("mixtures") of the variables.
Credal Marginal MAP
Marginal MAP is a widely applicable mixed inference task that identifies the most likely assignment for a subset of variables (called MAP variables).
Structured Prediction with Stronger Consistency Guarantees
In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.
