Kenji Kawaguchi
NUS Presidential Young Professor
Leads the Deep Learning Lab on deep learning theory and practice and large language models.
Kenji Kawaguchi's lab at NUS works on learning theory & optimization and large language models. 39 papers since 2023, 36 at top venues, mostly ICML and ICLR. 15 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 92%
- PhD students
- 15
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 22 papers2024 · T1 20
- 17 papers2023 · T1 16
T1 venuesOtherVenues: ICML, ICLR, NeurIPS, ACL, ECCV
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- Large language models9 papers
- deep3 papers
- deep learning2 papers
- learning theory0 papers
Paper list
From the lab homepage.
202422 papers
- NeurIPS (T1)Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling
- ICML (T1)Deep Regression Representation Learning with Topology
- ICML (T1)Drug Discovery with Dynamic Goal-aware Fragments
- ECCV (T1)Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models
- ICML (T1)Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
- NeurIPS (T1)How do Large Language Models Handle Multilingualism?
- NeurIPS (T1)Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
- ICML (T1)PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
- ACL (T1)Prompt Optimization via Adversarial In-Context Learning
- ACL (T1)ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
- Findings of ACLReaction-ship
- ICML (T1)Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
202317 papers
- NeurIPS (T1)An Information Theory Perspective on Variance-Invariance-Covariance Regularization
- Findings of EMNLPAutomatic Model Selection with Large Language Models for Reasoning
- ICML (T1)Auxiliary Learning as an Asymmetric Bargaining Game
- ICLR (T1)D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory
- ICML (T1)Discrete Key-Value Bottleneck
- ICML (T1)GFlowOut: Dropout with Generative Flow Networks
- ICML (T1)How Does Information Bottleneck Help Deep Learning?
- NeurIPS (T1)Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
- UAI (T1)MixupE: Understanding and improving mixup from directional derivative perspective
- EMNLP (T1)MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
- NeurIPS (T1)PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
- NeurIPS (T1)Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NUSFall 2027
School of Computing PhD in Computer Science
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USUW
Vasileios Charisopoulos
- Learning theory & optimization
- CNTsinghua
Zhixuan Fang
- Reasoning & neuro-symbolic AI
- CAToronto
Ashish J. Khisti
- Learning theory & optimization
- USUMD
Han Shao
- Trustworthy & safe ML
- HKCUHK
Hanrui Zhang
- Reasoning & neuro-symbolic AI
- USUT Austin
Yan Leng
- Trustworthy & safe ML
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
NUScloses