1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs
Tristan Cinquin, Geoff Pleiss, Agustinus Kristiadi
While chain-of-thought prompting with Best-of-N (BoN) selection has become popular for mathematical reasoning in large language models (LLMs), its linear structure fails to capture…
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin +2
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained…
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Tristan Cinquin, Robert Bamler
Bayesian neural networks (BNN) promise to combine the predictive performance of neural networks with principled uncertainty modeling important for safety-critical systems and decis…
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…
Pathologies in priors and inference for Bayesian transformers
Tristan Cinquin, Alexander Immer, Max Horn +1
In recent years, the transformer has established itself as a workhorse in many applications ranging from natural language processing to reinforcement learning. Similarly, Bayesian…