Yingzhen Li
Associate Professor in Machine Learning
Builds reliable machine learning combining Bayesian statistics and deep learning, focusing on approximate inference, uncertainty and deep generative models.
Yingzhen Li's lab at Imperial works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 18 papers since 2023, 15 at top venues, mostly NeurIPS and ICML. 13 PhD students. 10 alumni, most often to Alibaba and Apple. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 83%
- PhD students
- 13
- MS students
- 0
- alumni listed
- 10
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 10 papers2025 · T1 7
- 4 papers2024 · T1 4
- 4 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, TMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference5 papers
- Learning theory & optimization3 papers
- Efficient training & inference1 paper
- bayesian statistics0 papers
- approximate inference0 papers
Paper list
From the lab homepage.
202510 papers
- Handbook of Markov Chain Monte Carlo, EditionBayesian Computation in Deep Learning
- ICML (T1)Causal Discovery from Conditionally Stationary Time Series
- NeurIPS (T1)Compact Memory for Continual Logistic Regression
- NeurIPS (T1)Discrete Neural Flow Samplers with Locally Equivariant Transformer
- International Conference on Learning Representations (ICLR), .Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
- NeurIPS (T1)Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
- TMLR (T1)On the Challenges and Opportunities in Generative AI
- NeurIPS (T1)Recurrent Memory for Online Interdomain Gaussian Processes
- Information Processing in Medical Imaging (IPMI), .Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching
- NeurIPS (T1)Variational Uncertainty Decomposition for In-Context Learning
20244 papers
- ICLR (T1)C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
- NeurIPS (T1)Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
- ICML (T1)On the Identifiability of Switching Dynamical Systems
- ICML (T1)Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
20234 papers
- ICLR (T1)Calibrating Transformers via Sparse Gaussian Processes
- NeurIPS (T1)Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
- ICLR (T1)ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
- ICML (T1)Markovian Gaussian Process Variational Autoencoders
Where alumni went10 listed, 67% to industry
Industry 6Academia 3
Most often to
- Alibaba
- Apple
- Isomorphic Labs
Also to
- University of Copenhagen
- University of Glasgow
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
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