Yingzhen Li
Nanyang Associate Professor (NRF)
Builds reliable probabilistic machine learning that generalises to unseen environments, including approximate inference and generative models.
Yingzhen Li's lab at NTU works on probabilistic & causal inference, generative & foundation models and efficient training & inference. 17 papers since 2023, 16 at top venues, mostly NeurIPS and ICLR. 13 PhD students. 9 alumni, most often to Alibaba and Apple.
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 94%
- PhD students
- 13
- MS students
- 0
- alumni listed
- 9
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 8
- 4 papers2024 · T1 4
- 4 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, IPMI, TMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference4 papers
- Generative & foundation models4 papers
- Efficient training & inference1 paper
- approximate inference0 papers
- approximate0 papers
Paper list
From the lab homepage.
20259 papers
- 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
- ICLR (T1)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
- IPMIUnsupervised 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 went9 listed, 75% to industry
Industry 6Academia 2
Most often to
- Alibaba
- Apple
- Isomorphic Labs
Also to
- University of Copenhagen
- University of Glasgow
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USNYU
Jennifer Hill
- Probabilistic & causal inference
- SGNUS
Bryan Kian Hsiang Low
- Probabilistic & causal inference
- CHETH Zurich
Mark Fuge
- Learning theory & optimization
- GBUCL
Arthur Gretton
- Learning theory & optimization
- GBUCL
Andrew Saxe
- Computational neuroscience & cognition
- USCMU
Nicholas Boffi
- Generative & foundation models
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.