Moritz Schauer
@MoritzSchauer
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Statistician, Associate professor, Chalmers University of Technology and University of Gothenburg
Joined January 2018
Apparently this is my claim to fame
obsessed with whoever saw gum on a sidewalk and was like "let me add this to the poisson distribution wikipedia article"
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Applications are open for the CO-OP Master’s 26/27, which is a unique work-integrated programme, combining academic training with industry experience! Hold a Bachelor’s in math, science or engineering & want to help shape a prosperous Africa? Apply via: https://t.co/QACa43C1jo
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Sam Power, Giorgos Vasdekis. [statCO]. Some aspects of robustness in modern Markov Chain Monte Carlo.
arxiv.org
Markov Chain Monte Carlo (MCMC) is a flexible approach to approximate sampling from intractable probability distributions, with a rich theoretical foundation and comprising a wealth of exemplar...
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A propos…
As child I climbed on top of our old red painted wardrobe to contemplate - I don’t think I have the right substitute for that now
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Neural Guided Diffusion Bridges https://t.co/c7Btf0PmiC w/ @gefanyang @MeulenFrank We introduce a new bridge simulation method that combines the guided proposals of @MeulenFrank and @MoritzSchauer with an additional correction drift term parametrized by a learnable neural
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Sebastiano Grazzi, Giacomo Zanella. [ https://t.co/HSQj8NHDOn]. Parallel computations for Metropolis Markov chains with Picard maps.
stat.io
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Right, you don't need error bars on error bars. Probabilistic uncertainty about uncertainty collapses. This is the “monadic join” in probability. Instead of a coin with random bias p ∼ π, you can flip a coin with the deterministic bias μ. Just take μ = E[p].
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We are looking for someone to join the group as a postdoc to help us with scaling implicit transfer operators. If you are interested in this, please reach out to me through email. Include CV, with publications and brief motivational statement. RTs appreciated!
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1/4) I am very happy to share our latest work on the information theory of generative diffusion: "Entropic Time Schedulers for Generative Diffusion Models" We find that the conditional entropy offers a natural data-dependent notion of time.
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Registration for this years CHAIR Structured Learning Workshop is open. Speakers include: Klaus Robert Müller, Jens Sjölund, @AlexanderTong7, @JanStuehmer @ArnaudDoucet1, Marco Cuturi, @MartaBetcke, Elena Agliari, Beatriz Seoane, Alessandro Ingrosso,
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A friend of mine says that Twitter has a dehumanizing effect on a person - it sharpens your wit but hollows out your soul. For some reason, it has the opposite effect on me: my soul feels strangely uplifted, but my sense of self…
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A paper that started with a tweet, now its submitted: Compositionality in algorithms for smoothing / Moritz Schauer, Frank van der Meulen, Andi Q. Wang https://t.co/1YISX48IHX
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Check out cool new work from our group in collaboration with Pfizer and AstraZeneca, lead by Julian Cremer and Ross Irwin on FLOWR, a flow-based ligand generation approach, and highly sanitized benchmark dataset, SPINDR, for the SBDD community!
FLOWR – Flow Matching for Structure-Aware de novo and Conditional Ligand Generation 1. FLOWR introduces a new generative framework for structure-based ligand design using flow matching instead of diffusion, achieving up to 70x faster inference while improving ligand validity,
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Erik Jansson, Moritz Schauer, Ruben Seyer, Akash Sharma. [ https://t.co/kqdORNEPTP]. Creating non-reversible rejection-free samplers by rebalancing skew-balanced Markov jump processes.
arxiv.org
Markov chain Monte Carlo methods are central in computational statistics, and typically rely on detailed balance to ensure invariance with respect to a target distribution. Although...
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Vincent Molin, Axel Ringh, Moritz Schauer, Akash Sharma: Controlled stochastic processes for simulated annealing https://t.co/2rwnJeDfuY
https://t.co/lfT6CHRD4f
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Eklund, Lang, Schauer: Guided smoothing and control for diffusion processes https://t.co/uyxdYIe6LQ
https://t.co/lDrNQP8N0u
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But, have you heard of the convex order? X ≤c Y if E[f(X)]≤E[f(Y)] for all convex f. 𝐂𝐨𝐮𝐩𝐥𝐢𝐧𝐠: this is the same as saying that X,Y cam be represented on the same probability space such that X=E[Y|X] (Strassen, 1965)
Compare distributions of real random variables X,Y. You might know the stochastic order. X≤ₛₜY if P(X>x)≤P(Y>x) for all x. Equiv., E[f(X)]≤E[f(Y)] for all increasing f. 𝐂𝐨𝐮𝐩𝐥𝐢𝐧𝐠: same as saying that X,Y can be represented on the same probability space such that X≤Y
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Shape evolution…. We knew it @stefanhsommer
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Excited to share our NeurIPS 2024 Oral, Convolutional Differentiable Logic Gate Networks, leading to a range of inference efficiency records, including inference in only 4 nanoseconds 🏎️. We reduce model sizes by factors of 29x-61x over the SOTA. Paper: https://t.co/Aptk35mKir
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