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<li><strong>Upload</strong> an existing slide deck to <code>static/</code> and link using <code>url_slides</code> parameter in the front matter of the talk file</li>
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<p>Further event details, including page elements such as image galleries, can be added to the body of this page.</p></description></item><item><title>Beyond quantification: Navigating uncertainty in professional AI systems</title><link>https://fortuinlab.github.io/publication/delacroix-2025-beyond/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/delacroix-2025-beyond/</guid><description/></item><item><title>Can Transformers Learn Full Bayesian Inference in Context?</title><link>https://fortuinlab.github.io/publication/reuter-2025-transformers/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/reuter-2025-transformers/</guid><description/></item><item><title>Data Sharing between Wind Energy Producers: Economic Incentives and Strategic Behavior</title><link>https://fortuinlab.github.io/publication/ruizdevargas-2025-datasharing/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/ruizdevargas-2025-datasharing/</guid><description/></item><item><title>Data-Driven Discovery of Feature Groups in Clinical Time Series</title><link>https://fortuinlab.github.io/publication/sergeev-2025-discovery/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sergeev-2025-discovery/</guid><description/></item><item><title>e-SparX: A Graph-Based Artifact Exchange Platform to Accelerate Machine Learning Research in the Energy Systems Community</title><link>https://fortuinlab.github.io/publication/schneider-2025-esparx/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/schneider-2025-esparx/</guid><description/></item><item><title>On the Challenges and Opportunities in Generative AI</title><link>https://fortuinlab.github.io/publication/manduchi-2025-challenges/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/manduchi-2025-challenges/</guid><description/></item><item><title>OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders</title><link>https://fortuinlab.github.io/publication/floege-2025-oneprot/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/floege-2025-oneprot/</guid><description/></item><item><title>ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods</title><link>https://fortuinlab.github.io/publication/kmicikiewicz-2025-prospero/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/kmicikiewicz-2025-prospero/</guid><description/></item><item><title>Quantifying and hedging economic risk in disability income insurance portfolios</title><link>https://fortuinlab.github.io/publication/schneider-khemka-pitt-zhang-2025/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/schneider-khemka-pitt-zhang-2025/</guid><description/></item><item><title>Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461)</title><link>https://fortuinlab.github.io/publication/fortuin-2025-rethinking/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/fortuin-2025-rethinking/</guid><description/></item><item><title>Sparse Gaussian Neural Processes</title><link>https://fortuinlab.github.io/publication/rochussen-2025-sparse/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/rochussen-2025-sparse/</guid><description/></item><item><title>Weighted-Sum of Gaussian Process Latent Variable Models</title><link>https://fortuinlab.github.io/publication/odgers-2025-weightedsumgaussianprocesslatent/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/odgers-2025-weightedsumgaussianprocesslatent/</guid><description/></item><item><title>What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization</title><link>https://fortuinlab.github.io/publication/tristan-2025-materials/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/tristan-2025-materials/</guid><description/></item><item><title>A revised single-cell transcriptomic atlas of Xenopus embryo reveals new differentiation dynamics</title><link>https://fortuinlab.github.io/publication/petrova-2024-atlas/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/petrova-2024-atlas/</guid><description/></item><item><title>Counterfactual Reasoning with Knowledge Graph Embeddings</title><link>https://fortuinlab.github.io/publication/zellinger-etal-2024-counterfactual/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/zellinger-etal-2024-counterfactual/</guid><description/></item><item><title>FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning</title><link>https://fortuinlab.github.io/publication/cinquin-2024-fsp/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/cinquin-2024-fsp/</guid><description/></item><item><title>Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models</title><link>https://fortuinlab.github.io/publication/onal-2024-gaussian/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/onal-2024-gaussian/</guid><description/></item><item><title>Hodge-Aware Contrastive Learning</title><link>https://fortuinlab.github.io/publication/mollers-2024-hodge/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/mollers-2024-hodge/</guid><description/></item><item><title>How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?</title><link>https://fortuinlab.github.io/publication/kristiadi-2024-useful/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/kristiadi-2024-useful/</guid><description/></item><item><title>Improving Neural Additive Models with Bayesian Principles</title><link>https://fortuinlab.github.io/publication/bouchiat-2024-improving/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/bouchiat-2024-improving/</guid><description/></item><item><title>Incorporating Unlabelled Data into Bayesian Neural Networks</title><link>https://fortuinlab.github.io/publication/sharma-2024-incorporating/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sharma-2024-incorporating/</guid><description/></item><item><title>On the Challenges and Opportunities in Generative AI</title><link>https://fortuinlab.github.io/publication/manduchi-2024-challenges/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/manduchi-2024-challenges/</guid><description/></item><item><title>Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation</title><link>https://fortuinlab.github.io/publication/paul-2024-parameter/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/paul-2024-parameter/</guid><description/></item><item><title>Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI</title><link>https://fortuinlab.github.io/publication/papamarkou-2024-position/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/papamarkou-2024-position/</guid><description/></item><item><title>Rethinking Exploration in Asynchronous Bayesian Optimization: Standard Acquisition is All You Need</title><link>https://fortuinlab.github.io/publication/riegler-2025-rethinking/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/riegler-2025-rethinking/</guid><description/></item><item><title>Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting</title><link>https://fortuinlab.github.io/publication/sanchez-lopez-2024/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sanchez-lopez-2024/</guid><description/></item><item><title>Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood</title><link>https://fortuinlab.github.io/publication/dhahri-2024-shaving/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/dhahri-2024-shaving/</guid><description/></item><item><title>Stein Variational Newton Neural Network Ensembles</title><link>https://fortuinlab.github.io/publication/floege-2024-stein/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/floege-2024-stein/</guid><description/></item><item><title>Structured Partial Stochasticity in Bayesian Neural Networks</title><link>https://fortuinlab.github.io/publication/rochussen-2024-structured/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/rochussen-2024-structured/</guid><description/></item><item><title>Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information</title><link>https://fortuinlab.github.io/publication/sergeev-2024-towards/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sergeev-2024-towards/</guid><description/></item><item><title>Understanding pathologies of deep heteroskedastic regression</title><link>https://fortuinlab.github.io/publication/wong-2024-understanding/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/wong-2024-understanding/</guid><description/></item><item><title>A primer on Bayesian neural networks: review and debates</title><link>https://fortuinlab.github.io/publication/arbel-2023-primer/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/arbel-2023-primer/</guid><description/></item><item><title>Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning</title><link>https://fortuinlab.github.io/publication/ashman-2023-amortised/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/ashman-2023-amortised/</guid><description/></item><item><title>Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072)</title><link>https://fortuinlab.github.io/publication/fortuin-2023-challenges/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/fortuin-2023-challenges/</guid><description/></item><item><title>Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo</title><link>https://fortuinlab.github.io/publication/ujvary-2023-estimating/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/ujvary-2023-estimating/</guid><description/></item><item><title>Learning with noisy labels by adaptive gradient-based outlier removal</title><link>https://fortuinlab.github.io/publication/sedova-2023-learning/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sedova-2023-learning/</guid><description/></item><item><title>Probabilistic predictions for partial least squares using bootstrap</title><link>https://fortuinlab.github.io/publication/odgers-2023-probabilistic/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/odgers-2023-probabilistic/</guid><description/></item><item><title>Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization</title><link>https://fortuinlab.github.io/publication/kristiadi-2023-promises/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/kristiadi-2023-promises/</guid><description/></item><item><title>Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice</title><link>https://fortuinlab.github.io/publication/rothfuss-2023-scalable/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/rothfuss-2023-scalable/</guid><description/></item><item><title>Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks</title><link>https://fortuinlab.github.io/publication/moellers-2023-uncertainty/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/moellers-2023-uncertainty/</guid><description/></item><item><title>Contact</title><link>https://fortuinlab.github.io/contact/</link><pubDate>Mon, 24 Oct 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/contact/</guid><description/></item><item><title>Tour</title><link>https://fortuinlab.github.io/tour/</link><pubDate>Mon, 24 Oct 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/tour/</guid><description/></item><item><title>From Hyperbolic Geometry Back to Word Embeddings</title><link>https://fortuinlab.github.io/publication/assylbekov-2022-hyperbolic/</link><pubDate>Sun, 01 May 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/assylbekov-2022-hyperbolic/</guid><description/></item><item><title>Bayesian neural network priors revisited</title><link>https://fortuinlab.github.io/publication/fortuin-2022-bayesian/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/fortuin-2022-bayesian/</guid><description/></item><item><title>Data augmentation in Bayesian neural networks and the cold posterior effect</title><link>https://fortuinlab.github.io/publication/nabarro-2022-data/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/nabarro-2022-data/</guid><description/></item><item><title>Deep classifiers with label noise modeling and distance awareness</title><link>https://fortuinlab.github.io/publication/fortuin-2022-deep/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/fortuin-2022-deep/</guid><description/></item><item><title>From Judgement's Premises Towards Key Points</title><link>https://fortuinlab.github.io/publication/sultan-2022-judgements/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sultan-2022-judgements/</guid><description/></item><item><title>Invariance learning in deep neural networks with differentiable Laplace approximations</title><link>https://fortuinlab.github.io/publication/immer-2022-invariance/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/immer-2022-invariance/</guid><description/></item><item><title>Meta-learning richer priors for VAEs</title><link>https://fortuinlab.github.io/publication/negri-2022-meta/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/negri-2022-meta/</guid><description/></item><item><title>Neural Variational Gradient Descent</title><link>https://fortuinlab.github.io/publication/langosco-2022-neural/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/langosco-2022-neural/</guid><description/></item><item><title>On Disentanglement in Gaussian Process Variational Autoencoders</title><link>https://fortuinlab.github.io/publication/bing-2022-disentanglement/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/bing-2022-disentanglement/</guid><description/></item><item><title>On Interpretable Reranking-Based Dependency Parsing Systems</title><link>https://fortuinlab.github.io/publication/schottmann-2022-interpretable/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/schottmann-2022-interpretable/</guid><description/></item><item><title>Pathologies in Priors and Inference for Bayesian Transformers</title><link>https://fortuinlab.github.io/publication/cinquin-2022-pathologies/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/cinquin-2022-pathologies/</guid><description/></item><item><title>Priors in Bayesian deep learning: A review</title><link>https://fortuinlab.github.io/publication/fortuin-2022-priors/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/fortuin-2022-priors/</guid><description/></item><item><title>Probing as quantifying inductive bias</title><link>https://fortuinlab.github.io/publication/immer-2022-probing/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/immer-2022-probing/</guid><description/></item><item><title>Quantum Bayesian Neural Networks</title><link>https://fortuinlab.github.io/publication/berner-2022-quantum/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/berner-2022-quantum/</guid><description/></item><item><title>Sparse MoEs meet Efficient Ensembles</title><link>https://fortuinlab.github.io/publication/allingham-2022-sparse/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/allingham-2022-sparse/</guid><description/></item><item><title>A Bayesian Approach to Invariant Deep Neural Networks</title><link>https://fortuinlab.github.io/publication/mourdoukoutas-2021-bayesian/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/mourdoukoutas-2021-bayesian/</guid><description/></item><item><title>Annealed Stein 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Gradients</title><link>https://fortuinlab.github.io/publication/garriga-2021-exact/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/garriga-2021-exact/</guid><description/></item><item><title>Factorized Gaussian Process Variational Autoencoders</title><link>https://fortuinlab.github.io/publication/jazbec-2021-factorized/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/jazbec-2021-factorized/</guid><description/></item><item><title>Fast simulation of the LHCb electromagnetic calorimeter response using VAEs and GANs</title><link>https://fortuinlab.github.io/publication/sergeev-2021-fast/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://fortuinlab.github.io/publication/sergeev-2021-fast/</guid><description/></item><item><title>MGP-AttTCN: An interpretable machine learning model for the prediction of 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