Rui-Yang Zhang
@ryzhang.bsky.social
PhD student in Computational Statistics and Machine Learning at STOR-i CDT, Lancaster University, UK.
Research Interests: Bayesian Experimental Designs, Gaussian Processes, Sampling Algorithms.
https://shusheng3927.github.io/
Research Interests: Bayesian Experimental Designs, Gaussian Processes, Sampling Algorithms.
https://shusheng3927.github.io/
Reposted by Rui-Yang Zhang
The UCL IMSS Annual Lecture will take place on the 27th April with a keynote from @lestermackey.bsky.social.
The theme is 'Computational Statistics and Machine Learning' and we'll have talks from Alessandro Barp, Paula Cordero Encinar & Po-Ling Loh.
imss2026.github.io
@statisticsucl.bsky.social
The theme is 'Computational Statistics and Machine Learning' and we'll have talks from Alessandro Barp, Paula Cordero Encinar & Po-Ling Loh.
imss2026.github.io
@statisticsucl.bsky.social
IMSS Lecture 2026
imss2026.github.io
February 3, 2026 at 3:07 PM
The UCL IMSS Annual Lecture will take place on the 27th April with a keynote from @lestermackey.bsky.social.
The theme is 'Computational Statistics and Machine Learning' and we'll have talks from Alessandro Barp, Paula Cordero Encinar & Po-Ling Loh.
imss2026.github.io
@statisticsucl.bsky.social
The theme is 'Computational Statistics and Machine Learning' and we'll have talks from Alessandro Barp, Paula Cordero Encinar & Po-Ling Loh.
imss2026.github.io
@statisticsucl.bsky.social
Reposted by Rui-Yang Zhang
Very excited to announce the ProbAI Theory of Scaling Laws Workshop (warwick.ac.uk/fac/sci/stat...) at @warwickstats.bsky.social, 22-24 June! (1/4)
January 26, 2026 at 1:35 PM
Very excited to announce the ProbAI Theory of Scaling Laws Workshop (warwick.ac.uk/fac/sci/stat...) at @warwickstats.bsky.social, 22-24 June! (1/4)
Reposted by Rui-Yang Zhang
Accurate and thorough representation of prior and related work is one of the cornerstones of good research.
It is shocking to me that so many published NeurIPS papers, even from top institutions, have fabricated references.
I recommend reading the original report: gptzero.me/news/neurips/
It is shocking to me that so many published NeurIPS papers, even from top institutions, have fabricated references.
I recommend reading the original report: gptzero.me/news/neurips/
January 21, 2026 at 7:51 PM
Accurate and thorough representation of prior and related work is one of the cornerstones of good research.
It is shocking to me that so many published NeurIPS papers, even from top institutions, have fabricated references.
I recommend reading the original report: gptzero.me/news/neurips/
It is shocking to me that so many published NeurIPS papers, even from top institutions, have fabricated references.
I recommend reading the original report: gptzero.me/news/neurips/
Reposted by Rui-Yang Zhang
Mathematical Colloquium (at King's College London): A duality in the foundations of probability and statistics through history by Vladimir Vovk
www.kcl.ac.uk/events/mathe...
www.kcl.ac.uk/events/mathe...
January 8, 2026 at 9:55 AM
Mathematical Colloquium (at King's College London): A duality in the foundations of probability and statistics through history by Vladimir Vovk
www.kcl.ac.uk/events/mathe...
www.kcl.ac.uk/events/mathe...
Reposted by Rui-Yang Zhang
How do large language models interpret words relating to probability like “unlikely,” “probably,” or “almost certain"?
The below shows what happens when we compare judgements from different models to a benchmark dataset of human judgments (data from: github.com/zonination/p...).
The below shows what happens when we compare judgements from different models to a benchmark dataset of human judgments (data from: github.com/zonination/p...).
December 8, 2025 at 9:42 AM
How do large language models interpret words relating to probability like “unlikely,” “probably,” or “almost certain"?
The below shows what happens when we compare judgements from different models to a benchmark dataset of human judgments (data from: github.com/zonination/p...).
The below shows what happens when we compare judgements from different models to a benchmark dataset of human judgments (data from: github.com/zonination/p...).
Reposted by Rui-Yang Zhang
Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which
i) mass is reasonably well-concentrated in the centre of the state space, and
ii) the log-density is smooth and of moderate growth.
Outside of this setting, things can go poorly.
i) mass is reasonably well-concentrated in the centre of the state space, and
ii) the log-density is smooth and of moderate growth.
Outside of this setting, things can go poorly.
November 27, 2025 at 10:35 AM
Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which
i) mass is reasonably well-concentrated in the centre of the state space, and
ii) the log-density is smooth and of moderate growth.
Outside of this setting, things can go poorly.
i) mass is reasonably well-concentrated in the centre of the state space, and
ii) the log-density is smooth and of moderate growth.
Outside of this setting, things can go poorly.
Reposted by Rui-Yang Zhang
The recording of my talk on 'Multilevel neural simulation-based inference' at the 'One World Approximate Bayesian Inference' seminar series is now available on YouTube.
Link: www.youtube.com/watch?v=hBWd...
Link: www.youtube.com/watch?v=hBWd...
November 17, 2025 at 9:40 AM
The recording of my talk on 'Multilevel neural simulation-based inference' at the 'One World Approximate Bayesian Inference' seminar series is now available on YouTube.
Link: www.youtube.com/watch?v=hBWd...
Link: www.youtube.com/watch?v=hBWd...
Preferential Sampling refers to scenarios where observation locations are confounded by the field of interest which the same observations are used to infer. This recent arxiv (arxiv.org/abs/2511.03158) looked at how harmful ignoring preferential sampling would be - not much, according to the paper.
November 7, 2025 at 12:52 PM
Preferential Sampling refers to scenarios where observation locations are confounded by the field of interest which the same observations are used to infer. This recent arxiv (arxiv.org/abs/2511.03158) looked at how harmful ignoring preferential sampling would be - not much, according to the paper.
Reposted by Rui-Yang Zhang
I’ll be giving a talk on a recently accepted NeurIPS paper at the next OWABI seminar on Thursday. The talk will cover simulation-based inference and how you can enhance accuracy when you have cheap approximate simulators at hand.
The 1st OWABI talk of the Season will be given by François-Xavier Briol (University College London). who will talk about "Multilevel neural simulation-based inference".
Multilevel neural simulation-based inference
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences...
arxiv.org
October 28, 2025 at 8:06 AM
I’ll be giving a talk on a recently accepted NeurIPS paper at the next OWABI seminar on Thursday. The talk will cover simulation-based inference and how you can enhance accuracy when you have cheap approximate simulators at hand.
Reposted by Rui-Yang Zhang
"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
The Principles of Diffusion Models
This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas. Diffu...
arxiv.org
October 28, 2025 at 8:35 AM
"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
Reposted by Rui-Yang Zhang
Let me advertise a bit our Online Monte Carlo seminar:
This coming Tuesday, we have Giorgos Vasdekis speaking on some very interesting recent work.
Moreover, we have confirmed our speaker line-up through until December - very exciting!
See sites.google.com/view/monte-c... for further details.
This coming Tuesday, we have Giorgos Vasdekis speaking on some very interesting recent work.
Moreover, we have confirmed our speaker line-up through until December - very exciting!
See sites.google.com/view/monte-c... for further details.
October 26, 2025 at 4:35 PM
Let me advertise a bit our Online Monte Carlo seminar:
This coming Tuesday, we have Giorgos Vasdekis speaking on some very interesting recent work.
Moreover, we have confirmed our speaker line-up through until December - very exciting!
See sites.google.com/view/monte-c... for further details.
This coming Tuesday, we have Giorgos Vasdekis speaking on some very interesting recent work.
Moreover, we have confirmed our speaker line-up through until December - very exciting!
See sites.google.com/view/monte-c... for further details.
Reposted by Rui-Yang Zhang
The first talk of the season will be this coming Tuesday (23 September), given by Alexandre Bouchard-Côté from UBC. Alex is a great speaker, so do join if you have the chance!
See sites.google.com/view/monte-c... for details, links, and so on.
See sites.google.com/view/monte-c... for details, links, and so on.
September 19, 2025 at 3:23 PM
The first talk of the season will be this coming Tuesday (23 September), given by Alexandre Bouchard-Côté from UBC. Alex is a great speaker, so do join if you have the chance!
See sites.google.com/view/monte-c... for details, links, and so on.
See sites.google.com/view/monte-c... for details, links, and so on.
Reposted by Rui-Yang Zhang
September 18, 2025 at 6:59 PM
Reposted by Rui-Yang Zhang
Join us online for a discussion on
“Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning.
📅 Wed 08 Oct | 🕓 4–6pm UK
🔗 Register + download the paper: rss.org.uk/training-eve...
“Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning.
📅 Wed 08 Oct | 🕓 4–6pm UK
🔗 Register + download the paper: rss.org.uk/training-eve...
September 9, 2025 at 9:07 AM
Join us online for a discussion on
“Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning.
📅 Wed 08 Oct | 🕓 4–6pm UK
🔗 Register + download the paper: rss.org.uk/training-eve...
“Statistical exploration of the Manifold Hypothesis” and an opportunity to explore the intersection of geometry, statistics and machine learning.
📅 Wed 08 Oct | 🕓 4–6pm UK
🔗 Register + download the paper: rss.org.uk/training-eve...
Reposted by Rui-Yang Zhang
“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material.
In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵
In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵
September 6, 2025 at 9:20 PM
“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material.
In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵
In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵
Reposted by Rui-Yang Zhang
Gearing up for this workshop next week, with the finalised schedule attached!
For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.
For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.
September 3, 2025 at 7:30 PM
Gearing up for this workshop next week, with the finalised schedule attached!
For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.
For those who are unable to attend in person, but are interested in watching the talks, they will be streamed live on MS Teams. Please do get in touch with me if you'd like to stay informed about the stream.
Reposted by Rui-Yang Zhang
An announcement, which might be of some interest:
In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.
In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.
September 2, 2025 at 12:13 PM
An announcement, which might be of some interest:
In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.
In the period 2022-2024, myself and a number of other postdocs on the "CoSInES" and "Bayes4Health" EPSRC grants were involved in organising a number of internal tutorial workshops, on topics relevant to researchers in computational statistics.
Very cool!
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
August 29, 2025 at 3:41 PM
Very cool!
Reposted by Rui-Yang Zhang
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
August 29, 2025 at 3:07 PM
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Reposted by Rui-Yang Zhang
Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.
The slides are now available here: fxbriol.github.io/pdfs/slides-....
The slides are now available here: fxbriol.github.io/pdfs/slides-....
August 28, 2025 at 11:46 AM
Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.
The slides are now available here: fxbriol.github.io/pdfs/slides-....
The slides are now available here: fxbriol.github.io/pdfs/slides-....
Reposted by Rui-Yang Zhang
📣 Please share: We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (#AISTATS 2026) and welcome paper submissions at the intersection of AI, machine learning, statistics, and related areas. [1/3]
August 12, 2025 at 11:46 AM
📣 Please share: We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (#AISTATS 2026) and welcome paper submissions at the intersection of AI, machine learning, statistics, and related areas. [1/3]
Reposted by Rui-Yang Zhang
Liwen Xue, Axel Finke, Adam M. Johansen: Online Rolling Controlled Sequential Monte Carlo https://arxiv.org/abs/2508.00696 https://arxiv.org/pdf/2508.00696 https://arxiv.org/html/2508.00696
August 4, 2025 at 6:53 AM
Liwen Xue, Axel Finke, Adam M. Johansen: Online Rolling Controlled Sequential Monte Carlo https://arxiv.org/abs/2508.00696 https://arxiv.org/pdf/2508.00696 https://arxiv.org/html/2508.00696
Really enjoyed listening to this interview with Mike Giles. Only knew him from his multilevel Monte Carlo work, and it was quite a nice surprise to learn about his contributions to CFD and experiences with industrial collaborations!
July 28, 2025 at 9:58 AM
Really enjoyed listening to this interview with Mike Giles. Only knew him from his multilevel Monte Carlo work, and it was quite a nice surprise to learn about his contributions to CFD and experiences with industrial collaborations!
Reposted by Rui-Yang Zhang
we're out here simulating, visualising, thriving
July 15, 2025 at 12:46 PM
we're out here simulating, visualising, thriving