Topic in Machine Learning

Probabilistic & causal inference

129 labs at 28 universities work on probabilistic & causal inference (part of Machine Learning). Most at Cambridge, UCL and ETH Zurich. 93 published in 2026.

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I want to research probabilistic & causal inference in anywhere.

129 labs at 28 universities work on probabilistic & causal inference. Most at 11, 10, 9. 93 of them published in 2026, mostly at NeurIPS, ICML and ICLR. Earliest deadline: , Dec 1.

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129 labs

CNZhejiangSchool of Artificial IntelligenceFeb 28

Kun KuangAssociate Professor

Studies causal inference and its integration with machine learning, including causal AI for smart justice and domain-specific large models.

Probabilistic & causal inference
Efficient training & inference
Large language models

2026"I Don’t Know What to Say": A Fact-Filling Questionnaire Method to Help Non-Experts Talk to LegalAI AssistantACL
2026Asynchronous Denoising Diffusion Models for Aligning Text-to-Image GenerationICLR

115 T1 · 143 papers · Group size not listed

CHETH ZurichComputer Science (D-INFK)

Bernhard SchölkopfFull Professor

Researches machine learning and causal inference, including causal representation learning, kernel methods and applications in the sciences.

Probabilistic & causal inference
Learning theory & optimization
Efficient training & inference

2026Hyperbolic Busemann Neural NetworksCVPR
2026Learning Nonlinear Causal Reductions to Explain Reinforcement Learning PoliciesICLR

98 T1 · 141 papers · Group size not listed

USStanfordComputer ScienceDec 8

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Develops probabilistic and generative models such as diffusion models, with applications to sustainability.

Generative & foundation models
Probabilistic & causal inference
Image & video generation

2026Adaptive Spectral Feature Forecasting for Diffusion Sampling AccelerationCVPR
2026Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular DynamicsICLR

97 T1 · 114 papers · Group size not listed

CHETH ZurichComputer Science (D-INFK)

Andreas KrauseFull Professor

Leads the Learning & Adaptive Systems group, working on probabilistic machine learning, Bayesian optimization, safe reinforcement learning and uncertainty…

Probabilistic & causal inference
Reinforcement learning

2026A Theoretical Analysis of Test-Driven Code GenerationNeurIPS
2026A Unified Density Operator View of Flow Control and MergingICML

90 T1 · 102 papers · 14 members · 37 alumni

USCMUDepartment of PhilosophyDec 9

Kun ZhangProfessor

Develops methods for causal discovery and causal representation learning, aiming at explainable and controllable AI from a causal perspective.

Probabilistic & causal inference
Generative & foundation models
Human-AI collaboration & trust

2026Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-MakingICLR
2026Beyond DAGs: A Latent Partial Causal Model for Multimodal LearningICLR

90 T1 · 143 papers · Group size not listed

CNPekingInstitute for Artificial IntelligenceJan 15

Haoxuan Li助理教授

Researches machine learning, causal inference, trustworthy AI and large-model training and inference.

Probabilistic & causal inference
Trustworthy & safe ML
Efficient training & inference

2026A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect EstimatorsICLR
2026Bayesian Causal Experimental Design for CATE Estimation under NoncomplianceNeurIPS

88 T1 · 123 papers · Group size not listed

SGNUSDepartment of Computer ScienceDec 15

Bryan Kian Hsiang LowAssociate Professor

Works on probabilistic and data-efficient machine learning, including Bayesian optimisation, Gaussian processes and federated and collaborative learning.

Probabilistic & causal inference
Trustworthy & safe ML
Efficient training & inference

2026ActiveDPO: Active Direct Preference Optimization for Sample-Efficient AlignmentICLR
2026Adalina: Adaptive Linear Approximation for the Shapley Value and BeyondICML

79 T1 · 89 papers · 28 members · 40 alumni

CHETH ZurichComputer Science (D-INFK)

Ryan CotterellAssistant Professor

Studies natural language processing and computational linguistics, including formal language theory of neural language models, morphology and probabilistic…

Multilingual, translation & linguistics
Large language models
Probabilistic & causal inference

2025A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading BehaviorACL
2025Are Language Models Efficient Reasoners? A Perspective from Logic ProgrammingNeurIPS

64 T1 · 98 papers · 15 members · 8 alumni

USMITElectrical Engineering and Computer ScienceDec 1

Leslie KaelblingPanasonic Professor

Studies robotics, machine learning, and planning under uncertainty.

Planning, control & multi-robot systems
Probabilistic & causal inference

2026Which Reconstruction Model Should a Robot Use? Routing Image-to-3D Models for Cost-Aware Robotic ManipulationIROS
2026From Pixels to Predicates: Learning Symbolic World Models Via Pretrained VLMsRA-L

54 T1 · 72 papers · 12 members · 122 alumni

USNYUComputer Science (Courant)Dec 12

Andrew Gordon WilsonProfessor of Computer Science and Data Science

Works on Bayesian deep learning, Gaussian processes and generalization in neural networks, with numerical linear algebra for scalable inference.

Probabilistic & causal inference
Learning theory & optimization
Efficient training & inference

2026A Unification of Discrete, Gaussian, and Simplicial DiffusionICLR
2026Diverse Dictionary LearningICLR

53 T1 · 58 papers · 7 members · 11 alumni

USUCLAComputer ScienceDec 15

Guy Van den BroeckProfessor

Directs the StarAI lab on probabilistic circuits, tractable probabilistic reasoning and neurosymbolic learning.

Probabilistic & causal inference
Reasoning & neuro-symbolic AI
Reasoning & agents

2026Breaking the Factorization Barrier in Diffusion Language ModelsICML
2026CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language ModelsNeurIPS

49 T1 · 70 papers · 7 members · 23 alumni

GBCambridgeEngineering - Information Engineering DivisionDec 8

José Miguel Hernández-LobatoProfessor of Machine Learning

Works on probabilistic machine learning, including Bayesian optimisation, approximate inference and machine learning for molecular design.

Probabilistic & causal inference
Efficient training & inference

2026A Diffusive Classification Loss for Learning Energy-based Generative ModelsICML
2026A Gaussian Process View on Observation Noise and Initialization in Wide Neural NetworksAISTATS

43 T1 · 60 papers · 6 members · 28 alumni

GBUCLGatsby Computational Neuroscience Unit

Arthur GrettonProfessor of Machine Learning

Develops kernel methods for machine learning, including two-sample tests, dependence measures and causal inference.

Learning theory & optimization
Probabilistic & causal inference
Neuroscience & brain-computer interfaces

2026Learn to Guide Your Diffusion ModelICLR
2025(De)-regularized Maximum Mean Discrepancy Gradient FlowJMLR

34 T1 · 36 papers · 4 members · 22 alumni

DETUMDepartment of Computer Science

Stephan GünnemannProfessor

Makes machine learning reliable and robust, with a focus on graph neural networks, adversarial robustness, uncertainty estimation and machine learning for…

Trustworthy & safe ML
Probabilistic & causal inference

2026A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial RobustnessICML
2026Amplified Patch-Level Differential Privacy for Free via Random CroppingTMLR

34 T1 · 81 papers · 26 members · 16 alumni

GBEdinburghSchool of Informatics

Amos StorkeyProfessor

Leads a group working on machine learning, reinforcement learning and language models, with interests in efficient deep learning and probabilistic methods.

Reinforcement learning
Probabilistic & causal inference
Efficient training & inference

2026A Sobering Look at Tabular Data Generation with Probabilistic CircuitsUAI
2026Kalman Linear Attention: Parallel Bayesian Filtering for Efficient Language Modeling and State TrackingICML

33 T1 · 61 papers · 18 members

USStanfordDepartment of Aeronautics and AstronauticsDec 8

Mykel J. KochenderferAssociate Professor of Aeronautics and Astronautics, Senior Fellow at the Stanford Institute for Human-Centered AI

Develops decision-making under uncertainty for safety-critical autonomous systems such as aircraft collision avoidance.

Autonomous vehicles & drones
Reinforcement learning
Probabilistic & causal inference

2026A new strategy for verifying reach-avoid specifications in neural feedback systemsAAAI
2026A semi-decentralized approach to multiagent controlAAMAS

33 T1 · 132 papers · 23 members · 173 alumni

USUT AustinComputer ScienceDec 15

Qiang LiuAssociate Professor, Faculty Fellowship #7 in Computer Science

Leads the Statistical Learning & AI Group, working on probabilistic inference, Stein variational methods, generative models such as rectified flow and deep…

Learning theory & optimization
Probabilistic & causal inference
Efficient training & inference

2026∇-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent SpaceICLR
2026Cautious Optimizers: Improving Training with One Line of CodeICLR

33 T1 · 34 papers · Group size not listed

USUCSDHalıcıoğlu Data Science InstituteDec 15

Biwei HuangAssistant Professor

Develops causal discovery methods with theoretical guarantees and causality-inspired machine learning.

Probabilistic & causal inference
Learning theory & optimization
Data management & big data

2025A Skewness-based Criterion for Addressing Heterogeneous Noise in Causal DiscoveryICLR
2025Activation Control for Efficient Eliciting Long Chain-of-Thought Ability of Language ModelsNeurIPS

32 T1 · 35 papers · 13 members

GBCambridgeEngineering - Information Engineering DivisionDec 8

Richard E. TurnerProfessor of Machine Learning

Works on probabilistic machine learning and its use for weather and earth-system prediction, including the Aardvark Weather and Aurora foundation models.

Probabilistic & causal inference
Generative & foundation models
Large language models

2026Fresh in memory: Training-order recency is linearly encoded in language model activationsICLR
2026Graph Random Features for Scalable Gaussian ProcessesICLR

29 T1 · 41 papers · 10 members · 19 alumni

CATorontoComputer ScienceDec 1

Rahul G. KrishnanAssistant Professor

Develops deep generative models and causal inference methods for machine learning in healthcare.

Probabilistic & causal inference
Generative & foundation models
Efficient training & inference

2026Can we generate portable representations for clinical time series data using LLMs?ICLR
2026Causal methods for LLM development and evaluationKDD

25 T1 · 32 papers · 11 members · 30 alumni

Where they areClick a university to narrow the list

Where they areClick a university to narrow the list

Other topics in Machine Learning

I want to research probabilistic & causal inference in anywhere.

Kun KuangAssociate Professor

Bernhard SchölkopfFull Professor

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Andreas KrauseFull Professor

Kun ZhangProfessor

Haoxuan Li助理教授

Bryan Kian Hsiang LowAssociate Professor

Ryan CotterellAssistant Professor

Leslie KaelblingPanasonic Professor

Andrew Gordon WilsonProfessor of Computer Science and Data Science

Guy Van den BroeckProfessor

José Miguel Hernández-LobatoProfessor of Machine Learning

Arthur GrettonProfessor of Machine Learning

Stephan GünnemannProfessor

Amos StorkeyProfessor

Mykel J. KochenderferAssociate Professor of Aeronautics and Astronautics, Senior Fellow at the Stanford Institute for Human-Centered AI

Qiang LiuAssociate Professor, Faculty Fellowship #7 in Computer Science

Biwei HuangAssistant Professor

Richard E. TurnerProfessor of Machine Learning

Rahul G. KrishnanAssistant Professor

Where they areClick a university to narrow the list

Other topics in Machine Learning

I want to research probabilistic & causal inference in anywhere.

Kun KuangAssociate Professor

Bernhard SchölkopfFull Professor

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Andreas KrauseFull Professor

Kun ZhangProfessor

Haoxuan Li助理教授

Bryan Kian Hsiang LowAssociate Professor

Ryan CotterellAssistant Professor

Leslie KaelblingPanasonic Professor

Andrew Gordon WilsonProfessor of Computer Science and Data Science

Guy Van den BroeckProfessor

José Miguel Hernández-LobatoProfessor of Machine Learning

Arthur GrettonProfessor of Machine Learning

Stephan GünnemannProfessor

Amos StorkeyProfessor

Mykel J. KochenderferAssociate Professor of Aeronautics and Astronautics, Senior Fellow at the Stanford Institute for Human-Centered AI

Qiang LiuAssociate Professor, Faculty Fellowship #7 in Computer Science

Biwei HuangAssistant Professor

Richard E. TurnerProfessor of Machine Learning

Rahul G. KrishnanAssistant Professor