ICML 2025 Past Other

ICML 2025 Workshop on Scaling Up Intervention Models

ICML 2025 Workshop SIM

Submission deadline
May 26, 2025, 11:59 UTC
imported from OpenReview — check the website for extensions
Submission portal
OpenReview
Notes
Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).

Accepted papers (41)

Fetched from OpenReview (v2) on 2026-06-10.

  1. A Meta-Learning Approach to Causal Inference

    Dragos Cristian Manta, Philippe Brouillard, Dhanya Sridhar · PDF
  2. Amortized Active Generation of Pareto Sets

    Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira, Piotr Koniusz, Cheng Soon Ong, Edwin V. Bonilla · PDF
  3. An Object-Attribute Decoupled Approach for Learning Disentangled Representation for Image and Video Analysis

    Sanket Gandhi, Atul, Samanyu Mahajan, Rushil Gupta, Vishal Sharma, Arnab Kumar Mondal, Rohan Paul, Parag Singla · PDF
  4. Bidding for Influence: Auction-Driven Diffusion Image Generation

    Lillian Sun, Henry Huang, Fucheng Warren Zhu, Giannis Daras, Constantinos Costis Daskalakis · PDF
  5. Can Large Language Models Help Experimental Design for Causal Discovery?

    Junyi Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, Hui Xiong · PDF
  6. CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

    Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas, Bingru Li, Junwei Ma, Jesse C. Cresswell, Rahul Krishnan · PDF
  7. Competing Event Models: Next Event Prediction Under Interventions

    Yoav Wald, Xiang Gao, Rajesh Ranganath · PDF
  8. Deep RL Inventory Management with Supply and Capacity Risk Awareness

    Defeng Liu, Ying Liu, Carson Eisenach · PDF
  9. Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

    Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade, Hanlin Zhang · PDF
  10. Do-PFN: In-Context Learning for Causal Effect Estimation

    Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf · PDF
  11. Enhancing Math Reasoning in Small-sized LLMs via Preview Difficulty-Aware Intervention

    XINHAN DI, JoyJiaoW · PDF
  12. Equipping Graphical Models with Interventions and Interactions Simultaneously

    James Enouen, Angela Zhou, Yan Liu · PDF
  13. Estimating Causal Effects in Gaussian Linear SCMs with Finite Data

    Aurghya Maiti, Prateek Jain · PDF
  14. Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning

    Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk · PDF
  15. Estimating treatment effects in networks using domain adversarial learning

    Daan Caljon, Jente Van Belle, Wouter Verbeke · PDF
  16. Failure Modes of LLMs for Causal Reasoning on Narratives

    Khurram Yamin, Shantanu Gupta, Gaurav Rohit Ghosal, Zachary Chase Lipton, Bryan Wilder · PDF
  17. Faithfulness and Intervention-Only Causal Discovery

    Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler · PDF
  18. Keep the Alignment, Skip the Overhead: Lightweight Instruction Alignment for Continually Trained LLMs

    Ishan Jindal, Badrinath Chandana, Pranjal Bharti, Lakkidi Vinay, SACHIN DEV SHARMA · PDF
  19. Learning to Adapt: Self-Supervised Representations for Robust Contextual Bandits

    Janos Horvath · PDF
  20. Learning Treatment Representations for Downstream Instrumental Variable Regression

    Shiangyi Lin, Hui Lan, Vasilis Syrgkanis · PDF
  21. LLMs Struggle to Perform Counterfactual Reasoning with Parametric Knowledge

    Khurram Yamin, Gaurav Rohit Ghosal, Bryan Wilder · PDF
  22. Markov-Boundary Consistent Feature Attribution

    Mateusz Gajewski, Mateusz Olko, Mikołaj Morzy, Piotr Sankowski · PDF
  23. MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging

    Berker Demirel, Marco Fumero, Theofanis Karaletsos, Francesco Locatello · PDF
  24. Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design

    Tong Chen, Yinuo Zhang, Sophia Tang, Pranam Chatterjee · PDF
  25. Multi-Objective-Guided Generative Design of mRNA with Therapeutic Properties

    Sawan Patel, Sophia Tang, Yinuo Zhang, Pranam Chatterjee, Sherwood Yao · PDF
  26. Network System Forecasting Despite Topology Perturbation

    Ramzi Dakhmouche, Ivan Lunati, Hossein Gorji · PDF
  27. Noise Tolerance of Distributionally Robust Learning

    Ramzi Dakhmouche, Ivan Lunati, Hossein Gorji · PDF
  28. Off-Policy Learning for Diversity-aware Candidate Retrieval in Two-stage Decisions

    Haruka Kiyohara, Rayhan Khanna, Thorsten Joachims · PDF
  29. OOD Detection with Relative Angles

    Berker Demirel, Marco Fumero, Francesco Locatello · PDF
  30. Prompt Optimization with Logged Bandit Data

    Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims · PDF
  31. Quantized Disentanglement: A Practical Approach

    Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent · PDF
  32. Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

    Rickard K.A. Karlsson, Piersilvio De Bartolomeis, Issa Dahabreh, Jesse H. Krijthe · PDF
  33. scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

    Davide D'Ascenzo, Sebastiano Cultrera di Montesano · PDF
  34. SOAPIA: Siamese-Guided Generation of Off Target-Avoiding Protein Interactions with High Target Affinity

    Sophia Vincoff, Oscar Davis, Ismail Ilkan Ceylan, Alexander Tong, Joey Bose, Pranam Chatterjee · PDF
  35. Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers

    Harsh NILESH PATHAK, Randy C. Paffenroth · PDF
  36. Steering LLM Reasoning Through Bias-Only Adaptation

    Viacheslav Sinii, Alexey Gorbatovski, Artem Cherepanov, Boris Shaposhnikov, Nikita Balagansky, Daniil Gavrilov · PDF
  37. The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations

    Dingling Yao, Shimeng Huang, Riccardo Cadei, Kun Zhang, Francesco Locatello · PDF
  38. Thompson Sampling in Function Spaces via Neural Operators

    Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai, Edwin V. Bonilla · PDF
  39. Towards Causal Representation Learning with Observable Sources as Auxiliaries

    Kwonho Kim, Heejeong Nam, Inwoo Hwang, Sanghack Lee · PDF
  40. Towards Robust Causal Effect Identification beyond Markov Equivalence

    Kai Z. Teh, Kayvan Sadeghi, Terry Soo · PDF
  41. TrialCalibre: A Fully Automated Causal Engine for RCT Benchmarking and Observational Trial Calibration

    Amir Habibdoust Lafmajani, Xing Song · PDF