G. Wolfer
@gwolfer.bsky.social
Tokyo University of Agriculture and Technology
Department of Computer and Information Sciences
https://geo-wolfer.gitlab.io/
Department of Computer and Information Sciences
https://geo-wolfer.gitlab.io/
Reposted by G. Wolfer
Our paper is now available on #arXiv: arxiv.org/abs/2508.02637
August 5, 2025 at 4:14 AM
Our paper is now available on #arXiv: arxiv.org/abs/2508.02637
Reposted by G. Wolfer
new work on computing distances between stochastic processes ***based on sample paths only***! we can now:
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
May 26, 2025 at 1:27 PM
new work on computing distances between stochastic processes ***based on sample paths only***! we can now:
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
Reposted by G. Wolfer
A new blog post with intuitions behind continuous-time Markov chains, a building block of diffusion language models, like @inceptionlabs.bsky.social's Mercury and Gemini Diffusion. This post touches on different ways of looking at Markov chains, connections to point processes, and more.
Discrete Diffusion: Continuous-Time Markov Chains
A tutorial explaining some key intuitions behind continuous time Markov chains for machine learners interested in discrete diffusion models: alternative representations, connections to point processes...
www.inference.vc
May 22, 2025 at 3:14 PM
A new blog post with intuitions behind continuous-time Markov chains, a building block of diffusion language models, like @inceptionlabs.bsky.social's Mercury and Gemini Diffusion. This post touches on different ways of looking at Markov chains, connections to point processes, and more.
Reposted by G. Wolfer
Published today in JMLR: our joint work with Geoffrey Wolfer (Waseda University) @gwolfer.bsky.social
"Variance-Aware Estimation of Kernel Mean Embedding".
jmlr.org/papers/v26/2...
"Variance-Aware Estimation of Kernel Mean Embedding".
jmlr.org/papers/v26/2...
Variance-Aware Estimation of Kernel Mean Embedding
jmlr.org
April 16, 2025 at 2:09 AM
Published today in JMLR: our joint work with Geoffrey Wolfer (Waseda University) @gwolfer.bsky.social
"Variance-Aware Estimation of Kernel Mean Embedding".
jmlr.org/papers/v26/2...
"Variance-Aware Estimation of Kernel Mean Embedding".
jmlr.org/papers/v26/2...
Reposted by G. Wolfer
Our joint paper with Geoffrey Wolfer @gwolfer.bsky.social "Variance-Aware Estimation of the Kernel Mean Embedding" accepted for publication in the Journal of Machine Learning Research 🥳
arxiv.org/abs/2210.06672
arxiv.org/abs/2210.06672
Variance-Aware Estimation of Kernel Mean Embedding
An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, prope...
arxiv.org
March 12, 2025 at 3:16 AM
Our joint paper with Geoffrey Wolfer @gwolfer.bsky.social "Variance-Aware Estimation of the Kernel Mean Embedding" accepted for publication in the Journal of Machine Learning Research 🥳
arxiv.org/abs/2210.06672
arxiv.org/abs/2210.06672
Reposted by G. Wolfer
Traveling to Copenhagen. Tomorrow, I will give a talk at the DeLTA lab seminar sites.google.com/diku.edu/del...
I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link:
arxiv.org/abs/2402.10506
I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link:
arxiv.org/abs/2402.10506
December 1, 2024 at 12:57 PM
Traveling to Copenhagen. Tomorrow, I will give a talk at the DeLTA lab seminar sites.google.com/diku.edu/del...
I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link:
arxiv.org/abs/2402.10506
I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link:
arxiv.org/abs/2402.10506