Kun Zhang
Professor
Develops methods for causal discovery and causal representation learning, aiming at explainable and controllable AI from a causal perspective.
Kun Zhang's lab at CMU works on probabilistic & causal inference, generative & foundation models and human-AI collaboration & trust. 143 papers since 2023, 90 at top venues, mostly ICLR and ICML. Applications close .
At a glance
- papers, 2023–2026
- 37/ year
- at T1 venues
- 63%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 32 papers2026 · T1 29 · collecting
- 45 papers2025 · T1 43
- 37 papers2024 · T1 18
- 29 papers2023 · T1 0
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, AAAI, CVPR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference55 papers
- causal53 papers
- Generative & foundation models26 papers
- Human-AI collaboration & trust1 paper
- develops methods0 papers
Paper list
From the lab homepage.
202632 papers
- ICLR (T1)Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
- the Annual Meeting of the Association for Computational Linguistics (ACL)Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards
- ICLR (T1)Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
- ECCV (T1)Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality
- ICML (T1)Causal Modeling of Selection in Evolution
- ICML (T1)CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
- ICLR (T1)Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
- ICML (T1)Concept Concentration for Faithful Representation Intervention
- ICLR (T1)Conditional Independent Component Analysis For Estimating Causal Structure with Latent Variables
- ICLR (T1)Controllable Video Generation with Provable Disentanglement
- ICLR (T1)Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
- ICLR (T1)Diverse Dictionary Learning
202545 papers
- ICLR (T1)A Conditional Independence Test in the Presence of Discretization
- ICML (T1)A General Representation-Based Approach to Multi-Source Domain Adaptation
- ICLR (T1)A Robust Method to Discover Causal or Anticausal Relation
- ICLR (T1)A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
- ICLR (T1)Analytic DAG Constraints for Differentiable DAG Learning
- ICLR (T1)Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference
- ICLR (T1)Causal Representation Learning from Multimodal Biological Observations
- the Conference on Neural Information Processing Systems (NeurIPS ) Benchamark TrackCausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
- AAAI (T1)Continual Unsupervised Generative Modelling via Online Optimal Transport
- NeurIPS (T1)Detecting Generated Images by Fitting Natural Image Distributions
- ICLR (T1)Differentiable Causal Discovery for Latent Hierarchical Causal Models
- AAAI (T1)Dynamic Expansion Diffusion Learning for Lifelong Generative Modelling
202437 papers
- NeurIPS (T1)A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
- PLOS ONEA Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions
- International Conference on Learning Representations (ICLR)A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
- AAAI (T1)ACAMDA: Improving Data Efficiency in Reinforcement Learning Through Guided Counterfactual Data Augmentation
- International Conference on Machine Learning (ICML)CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
- International Conference on Machine Learning (ICML)Causal Representation Learning from Multiple Distributions: A General Setting
- International Conference on Learning Representations (ICLR)Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
- NeurIPS (T1)Causal Temporal Representation Learning with Nonstationary Sparse Transition
- Persuasive TechnologyCounterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
- International Conference on Machine Learning (ICML)Detecting and Identifying Selection Structure in Sequential Data
- NeurIPS (T1)Discovery of the Hidden World with Large Language Models
- International Conference on Machine Learning (ICML)Empowering Graph Invariance Learning with Deep Spurious Infomax
202329 papers
- International Conference on Learning Representations (ICLR)“Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems,” (opens in a new tab)
- International Conference on Learning Representations (ICLR)“Causal Balancing for Domain Generalization,” (opens in a new tab)
- International Conference on Machine Learning (ICML)“Causal Discovery with Latent Confounders Based on Higher-Order Cumulants,” (opens in a new tab)
- the Conference on Causal Learning and Reasoning (CLeaR)“Causal Discovery with Score Matching on Additive Models with Arbitrary Noise,” (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)“Counterfactual Generation with Identifiability Guarantee,” (opens in a new tab)
- IEEE International Symposium on Biomedical Imaging (ISBI)"Deep DAG Learning of Effective Brain Connectivity for fMRI Analysis," (opens in a new tab)
- International Conference on Machine Learning (ICML)“Evolving Semantic Prototype Improves Generative Zero-Shot Learning,” (opens in a new tab)
- International Conference on Machine Learning (ICML)“Feature Expansion for Graph Neural Networks,” (opens in a new tab)
- International Conference on Learning Representations (ICLR)“GAIN: On the Generalization of Instructional Action Understanding,” (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)“Generalizing Nonlinear ICA Beyond Structural Sparsity,” (opens in a new tab)
- International Conference on Machine Learning (ICML)“Identifiability of Label Noise Transition Matrix,” (opens in a new tab)
- Conference on Neural Information Processing Systems (NeurIPS)“Identification of Nonlinear Latent Hierarchical Models,” (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
Similar labsnearest by research summary
- CNTsinghua
Mingsheng Long
- Knowledge, QA & retrieval
- USMIT
Martin Wainwright
- Learning theory & optimization
- CNZhejiang
Lei Cheng
- Learning theory & optimization
- USUSC
Yue Zhao
- Time series, anomaly & spatio-temporal data
- USMichigan
Richard Lewis
- Reinforcement learning
- USCMU
Albert Gu
- Generative & foundation models
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
CMUcloses