{"id":953,"date":"2017-11-08T20:17:54","date_gmt":"2017-11-08T20:17:54","guid":{"rendered":"http:\/\/yingzhenli.net\/home\/en\/?page_id=953"},"modified":"2026-05-12T19:39:50","modified_gmt":"2026-05-12T19:39:50","slug":"talks","status":"publish","type":"page","link":"https:\/\/yingzhenli.net\/home\/en\/?page_id=953","title":{"rendered":"Talks"},"content":{"rendered":"
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Neural Flow Shortcut Samplers<\/a>. @ BIRS Efficient Approximate Bayesian Inference workshop<\/a>, Mar 2025<\/p>\n

On Identification & Learning of Structured Latent Representations<\/a>. @ ICML 2024 SPIGM workshop<\/a>, July 2024<\/p>\n

On Advancing Approximate Inference in the Era of Big AI<\/a>. @ UAI 2024 TPM workshop<\/a>, July 2024<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Calibrating Transformers via Sparse Gaussian Processes<\/a>. @ 2024 ISBA world meeting Bayesian deep learning session<\/a>, July 2024<\/p>\n

Sequential Generative Models: Some Basics & Advances<\/a>. @ Generative modelling summer school<\/a>, June 2023<\/p>\n

Make Stein's Method \"Great Again\" for Generative modelling?<\/a> @ Distance-based methods for machine learning workshop<\/a>, June 2023 [video<\/a>]<\/p>\n

Introduction to Bayesian Neural Networks<\/a>. @ ProbAI 2023 summer school<\/a>, June 2023<\/p>\n

Towards Causal Deep Generative Models for Sequential Data<\/a>. @ Quarter on Causality \"When Causal Inference Meets Statistical Analysis\"<\/a>, Apr 2023<\/p>\n

Variational Bayes for Deep Learning: Towards Function-Space Uncertainty Quantification<\/a>. @ BayesComp<\/a>, Mar 2023<\/p>\n

Understanding Masked Pre-Training: Fundamentally Different?<\/a> @ GenU 2022 workshop<\/a>, Sept 2022<\/p>\n

Introduction to Bayesian Neural Networks<\/a>. @ ProbAI 2022 summer school, June 2022 [video<\/a>][notes<\/a>][demo1<\/a>][demo2<\/a>]<\/p>\n

Inference with scores: slices, diffusions and flows<\/a>. @ ICML 2021 INNF+ workshop, July 2021 [video<\/a>]<\/p>\n

EBM inference & learning: A personal story<\/a>. @ ICLR 2021 EBM workshop, May 2021 [video<\/a>] [panel<\/a>]<\/p>\n

From parametric models to Gaussian processes<\/a>. @ UnderstandingDL talk series, Data @ University de Sao Paolo, Apr 2021 [video<\/a>]<\/p>\n

Understanding approximate inference in Bayesian neural networks: A joint talk<\/a>. Mar 2021 (as moderator).<\/p>\n

Advances in Approximate Inference<\/a>. Tutorial @ NeurIPS 2020<\/p>\n

On estimating epistemic uncertainty<\/a>. @ NeurIPS 2019 Bayesian deep learning workshop, Dec 2019 [video<\/a>]<\/p>\n

How accurate is the uncertainty estimate from your Bayesian neural networks?<\/a> @ MICCAI 2019 UNSURE workshop, Oct 2019<\/p>\n

Bayesian neural networks: a function space view tour<\/a>. @ Gaussian process summer school, Sept 2019 [video<\/a>]<\/p>\n

Gradient estimators for implicit models with Stein's method<\/a>. @ ICML 2019 Stein's method workshop, June 2019 [video<\/a>]<\/p>\n

Variational implicit processes<\/a>. @ ICML 2019, June 2019<\/p>\n

On KL divergence and beyond<\/a>. @ U Cambridge Faculty of English, Nov 2018<\/p>\n

Meta-learning for stochastic gradient MCMC<\/a>. @ UAI 2018 uncertainty in DL workshop, Aug 2018<\/p>\n

Efficient computation for Bayesian deep learning<\/a>. @ MSR Cambridge & Oxford statistics, Mar 2018<\/p>\n

Gradient estimators for implicit models<\/a>. @ NIPS 2017 approximate inference workshop, Dec 2017<\/p>\n

Wild approximate inference: why and how<\/a>. @ UCL CSML seminar series, Dec 2017<\/p>\n

Adversarial attacks and defences<\/a>. @ AI safety reading group, CUED, Nov 2017<\/p>\n

Approximate inference with amortised MCMC<\/a>. @ ICML 2017 implicit generative model workshop, Aug 2017<\/p>\n

Dropout-alpha BNNs<\/a>. @ ICML 2017, Aug 2017<\/p>\n

Approximate inference with amortised MCMC<\/a>. CamAIML workshop, Mar 2017<\/p>\n

Objective functions for variational auto-encoders. @ Twitter Cortex Vx (previously Magic Pony), Sept 2016<\/p>\n

Variational inference with R\u00e9nyi divergence. MSR-MLG joint workshop, Mar 2016<\/p>\n

Alpha divergence back to conversation.<\/a> Research Talk @ CBL, Feb 2016<\/p>\n

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Towards a unified variational framework for approximate Bayesian inference<\/a>. Invited talk @ Tsinghua University, Dec 2015<\/p>\n

Stochastic expectation propagation<\/a>. Spotlight @ NIPS 2015, Dec 2015<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Stochastic optimisation and adaptive learning rates.<\/a> Reading Group Talk @ CBL (with Mark Rowland<\/a>), Nov 2015 [code<\/a>]<\/p>\n

Concave-convex procedure.<\/a> Tea Talk @ CBL, Oct 2015<\/p>\n

Expectation propagation as a way of life.<\/a><\/em> Tea Talk @ CBL, Feb 2015. [reference<\/a>]<\/p>\n

Introduction to transfer learning.<\/a><\/em> Reading Group Talk @ CBL (with\u00a0Pei-Hao Su<\/a>), Jan 2015.<\/p>\n

Bayesian learning for restricted Boltzmann machines.<\/a><\/em> Research Talk @ CBL, Nov 2014.<\/p>\n

On Restricted Boltzmann Machine Learning.<\/a><\/em> Guest Talk @ Dept of Math, SYSU, Jun 2014.<\/p>\n

Loopy Belief Propagation.<\/a><\/em> RCC Talk @ CBL (with Alex Matthews<\/a>), Apr 2014.<\/p>\n

The Importance of Encoding.<\/a><\/em> Tea Talk @ CBL, Feb 2014.\u00a0[reference<\/a>]<\/p>\n

IT & ML: Channels, Quantizers, and\u00a0Divergences.<\/em><\/a> RCC Talk @ CBL (with\u00a0Antonio Art\u00e9s-Rodr\u00edguez<\/a>),\u00a0Jan 2014.<\/p>\n","protected":false},"excerpt":{"rendered":"

Neural Flow Shortcut Samplers. @ BIRS Efficient Approximate Bayesian Inference workshop, Mar 2025 On Identification & Learning of Structured Latent Representations. @ ICML 2024 SPIGM workshop, July 2024 On Advancing Approximate Inference in the Era of Big AI. @ UAI 2024 TPM workshop, July 2024 Calibrating Transformers via Sparse Gaussian Processes. @ 2024 ISBA world ... [Read more...]<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["entry","page","publish","author-admin","post-953"],"_links":{"self":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/953","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=953"}],"version-history":[{"count":32,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/953\/revisions"}],"predecessor-version":[{"id":1762,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/953\/revisions\/1762"}],"wp:attachment":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=953"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}