Kevin Lam @khflam
Stats/ML PhD student @OxfordStats khflam.github.io Oxford, England Joined October 2022-
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Steering Diffusion Models to Rare Events with Sequential Monte Carlo Aavash Subedi, Tim Reichelt, Christopher Williams, Philip Stier, Yee Whye Teh, Saifuddin Syed arxiv.org/abs/2610.08652 [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶]
@NeurIPSConf 2026, here we come! 🚀 The @OxfordYss is thrilled to celebrate 12 main-track papers accepted from our members and their supervisors! 🎉 Many of us will be in Sydney 🇦🇺, Paris 🇫🇷 and Atlanta 🇺🇸 — come say hi!
I'm at @ISBA_events in Nagoya this week. Come say hi and check out our work! 🔶Metropolis Adjusted Diffusion Models Wed (poster w/ @new2_yj @khflam) 🔶Rapid mixing of stereographic MCMC for heavy-tailed sampling Fri & Sun (talk by Federica Milinanni w/ @new2_yj)
This week, the @OxfordYss is attending the 2026 World Meeting of the @ISBA_events. An impressive number of our members are in Japan presenting their work. If you are in Japan this week and would like to discuss their research, please reach out and connect! 🤝
How to incorporate Metropolis-Hastings into score-based Diffusion models to improve generation? On the coming Tuesday, June 16th 4pm-5pm UK time, we will have @khflam to talk about "Metropolis-Adjusted Diffusion Models" (arxiv.org/pdf/2605.09654) 🚀 Join us with Zoom!! Links 👇
We've lost an absolute giant today. RIP Dimitri Bertsekas. His probability and optimization books got me through my masters. Massive loss for the MIT community and the field.
Check out the preprint: arxiv.org/abs/2605.09654 Huge thanks to my brilliant co-first authors @kevinhflam & @Chr1sW1lliams, as well as the fantastic @new2_yj, @yeewhye & @ArnaudDoucet1 [3/3]
We propose two approaches: 🔵Exact Barker corrector via a two-coin algorithm: the first exactly adjusted corrector for score-based models 🟢Simpson's rule approximation: efficient with order 5/2 accuracy Consistent FID gains on FFHQ, AFHQv2 & ImageNet-64 without retraining [2/3]
[📄preprint] Diffusion models 🤝 MCMC ! Diffusion model samplers are biased due to discretisation 💡The fix: Metropolis-type adjustment on corrector steps ❗️Challenge: no access to the density ratio, only the score 🔑Insight: the score (and some maths) is all you need... [1/3]
That’s a wrap on the #NeurIPS2025 Main Track! 🌴🏁 We presented 8 papers & loved the energy in San Diego. Huge thanks for the discussions! ☀️ But we aren't done. 🚀 Catch 4 more papers at the Workshops this weekend. #NeurIPS @OxfordStats
At #NeurIPS2025 presenting 2 papers: Rao-Blackwellised Reparameterisation Gradients with a rising star @kevinhflam, George Deligiannidis, and @yeewhye Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited with the inimitable Michalis Titsias
This explains why local reparam gradients have lower variance, but the local reparam trick is only applicable to independent pre-activations. To lower gradient variance in more general cases, we use conjugate gradients to invert covariance matrices in backprop: yielding R2-G2!
Excited to share our #NeurIPS2025 paper: Rao-Blackwellised Reparameterisation Gradients! We propose R2-G2 as a general-purpose gradient estimator for latent Gaussians and as the Rao-Blackwellisation of reparam gradients. Joint work with @thdbui @GeorgeDeligian9 @yeewhye
Wrote up some notes providing an introduction to discrete diffusion models, going into the theory of time-inhomogeneous CTMCs via generators/time-evolution systems. What motivated me was the sheer difficulty of finding a useful reference which laid out the theory (e.g. Kolmogorov equations etc.) in a single place. Hope these can be helpful to others.
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Alexander Soen @AlexanderSoen
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G. @ The Neuron @TheNeuronScribe
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Mathieu @miniapeur
42K Followers 2K Following Gradient surfer by day, Möbius stripper by night. PhD @ai_ucl. Looking for opportunities. DMs are clopen.
Luca Ambrogioni @LucaAmb
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Aavash Subedi @asubedi01
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NeurIPS Europe @EurIPSConf
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213 Followers 395 Following PhD candidate at University College London
Rianne van den Berg @vdbergrianne
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Chris Glaze @chris_m_glaze
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152 Followers 1K Following PhD Student @TU_Muenchen Previous @TheDPTechnology | @chalmersuniv | @VolvoGroup | BSc. NUDT 18'
MolSS Reading Group @MolSS_Group
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Omar Rivasplata @OmarRivasplata
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Oscar Clivio @oscar_clivio
163 Followers 130 Following Scientist II, @Uber. Previously, PhD in ML and stats (causal inference) @OxfordStats @StatMLIO.
Paula Cordero @pcorderoencinar
55 Followers 157 Following PhD student at Imperial College and Oxford university
Michael Choi @michaelchchoi
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Jun Yang @new2_yj
377 Followers 516 Following Interested in computational statistics and machine learning.
Quentin Berthet @qberthet
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Sergio Calvo Ordoñez @s_calvoordonez
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Harit Vishwakarma @harit_v
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