Sarthak Mittal
@sarthmit.bsky.social
🚀 New Preprint! 🚀
In-Context Parametric Inference: Point or Distribution Estimators?
Thrilled to share our work on inferring probabilistic model parameters explicitly conditioned on data, in collab with @yoshuabengio.bsky.social, Nikolay Malkin & @glajoie.bsky.social!
🔗 arxiv.org/abs/2502.11617
In-Context Parametric Inference: Point or Distribution Estimators?
Thrilled to share our work on inferring probabilistic model parameters explicitly conditioned on data, in collab with @yoshuabengio.bsky.social, Nikolay Malkin & @glajoie.bsky.social!
🔗 arxiv.org/abs/2502.11617
In-Context Parametric Inference: Point or Distribution Estimators?
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random variables, incorporating priors and updating beliefs via Bayes' ...
arxiv.org
February 28, 2025 at 3:24 PM
🚀 New Preprint! 🚀
In-Context Parametric Inference: Point or Distribution Estimators?
Thrilled to share our work on inferring probabilistic model parameters explicitly conditioned on data, in collab with @yoshuabengio.bsky.social, Nikolay Malkin & @glajoie.bsky.social!
🔗 arxiv.org/abs/2502.11617
In-Context Parametric Inference: Point or Distribution Estimators?
Thrilled to share our work on inferring probabilistic model parameters explicitly conditioned on data, in collab with @yoshuabengio.bsky.social, Nikolay Malkin & @glajoie.bsky.social!
🔗 arxiv.org/abs/2502.11617
🚨 New Preprint! 🚨
We explore Amortized In-Context Bayesian Posterior Estimation with Niels, @glajoie.bsky.social, Priyank Jaini & @marcusabrubaker.bsky.social ! 🔥
Amortized Conditional Modeling = key to success in large-scale models! We use it to estimate posteriors 🔑
📄 arxiv.org/abs/2502.06601
We explore Amortized In-Context Bayesian Posterior Estimation with Niels, @glajoie.bsky.social, Priyank Jaini & @marcusabrubaker.bsky.social ! 🔥
Amortized Conditional Modeling = key to success in large-scale models! We use it to estimate posteriors 🔑
📄 arxiv.org/abs/2502.06601
Amortized In-Context Bayesian Posterior Estimation
Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Current solutions rely on iterative routines like Markov Chain ...
arxiv.org
February 28, 2025 at 3:21 PM
🚨 New Preprint! 🚨
We explore Amortized In-Context Bayesian Posterior Estimation with Niels, @glajoie.bsky.social, Priyank Jaini & @marcusabrubaker.bsky.social ! 🔥
Amortized Conditional Modeling = key to success in large-scale models! We use it to estimate posteriors 🔑
📄 arxiv.org/abs/2502.06601
We explore Amortized In-Context Bayesian Posterior Estimation with Niels, @glajoie.bsky.social, Priyank Jaini & @marcusabrubaker.bsky.social ! 🔥
Amortized Conditional Modeling = key to success in large-scale models! We use it to estimate posteriors 🔑
📄 arxiv.org/abs/2502.06601