5 papers
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal, Yoshua Bengio, Nikolay Malkin +1
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random variables, incorporating priors and updating…
Mixtures of In-Context Learners
Giwon Hong, Emile van Krieken, Edoardo Ponti +2
In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…
On Generalization for Generative Flow Networks
Anas Krichel, Nikolay Malkin, Salem Lahlou +1
Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an unnormalized probability distribution, ca…
Machine learning and information theory concepts towards an AI Mathematician
Yoshua Bengio, Nikolay Malkin
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be…
GFlowNet-EM for learning compositional latent variable models
Edward J. Hu, Nikolay Malkin, Moksh Jain +3
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the l…