UBC Computer Science publishes 21 papers at top international machine learning conference
A total of 9 papers were accepted at the International Conference on Learning Representation (ICLR) main conference and 12 papers at the associated workshops
Researchers from UBC Computer Science will be in Rio de Janeiro, Brazil from April 23 - 27, 2026 for the 14th International Conference on Learning Representations (ICLR). Each year, thousands of computer science researchers and industry leaders from around the world gather at ICLR to present the latest research and discuss breakthroughs in the field.
Nearly 20,000 papers were submitted to the conference and 5,355 papers in total were accepted. UBC Computer Science had 9 papers accepted at the main conference and 12 papers accepted to the various workshops.
A paper from Assistant Professor Mi Jung Park’s group on safe image generation was accepted as an oral presentation, representing the top 1% of conference paper submissions.
In addition to papers at the conference and workshops, Assistant Professor Evan Shelhamer is Co-Chair of mentoring and part of the organizing team for the Machine Learning for Remote Sensing Workshop and the Test-Time Updates Workshop.
The 9 papers at the main conference are:
DRBench: A Realistic Benchmark for Enterprise Deep Research
Amirhossein Abaskohi, Tianyi Chen, Miguel Muñoz-Mármol, Curtis Fox, Amrutha Varshini Ramesh, Etienne Marcotte, Xing Han Lù, Nicolas Chapados, Spandana Gella, Peter West, Giuseppe Carenini, Christopher Pal, Alexandre Drouin, Issam H. Laradji
Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen
Safety-Guided Flow (SGF): A Unified Framework for Negative Guidance in Safe Generation
Mingyu Kim, Young-Heon Kim, Mi Jung Park
*Oral presentation
SysMoBench: Evaluating AI on Formally Specifying Complex Real-World Systems
Qian Cheng, Ruize Tang, Emilie Ma, Finn Hackett, Peiyang He, Yiming Su, Ivan Beschastnikh, Yu Huang, Xiaoxing Ma, Tianyin Xu
Thicker and Quicker: The Jumbo Token for Fast Plain Vision Transformers
Anthony Fuller, Yousef Yassin, Daniel Kyrollos, Evan Shelhamer, James Green
To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
Jiayun Luo, Wan-Cyuan Fan, Lyuyang Wang, Xiangteng He, Tanzila Rahman, Purang Abolmaesumi, Leonid Sigal
Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning
Wenlong Deng, Yi Ren, Yushu Li, Boying Gong, Danica Sutherland, Xiaoxiao Li, Christos Thrampoulidis
UBC Computer Science also has 12 accepted papers in various ICLR workshops:
Bridging Generative and Predictive Paradigms via Hidden-Self-Distillation
Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham W. Taylor
Multimodal Intelligence: Next Token Prediction & Beyond Workshop
Caravan: Asynchronous Test-Time Adaptation for Faster Inference
Jiayin Kralik, Ethan Zhao, Anrui Liu, Reto Achermann, Ivan Beschastnikh, Evan Shelhamer
Test-Time Updates Workshop (TTU)
Changing Modalities by Cross-Band Transfer, Addition, and Peeking
Tim G. Zhou, Anthony Fuller, Geoff Pleiss, Evan Shelhamer
Machine Learning for Remote Sensing Workshop (ML4RS)
Discrete Meanflow Training Curriculum
Chia-Hong HSU, Frank Wood
Deep Generative Model in Machine Learning: Theory, Principle and Efficacy Workshop (DeLTa)
Hidden-Layer Self-Distillation Yields Drift-Resilient Visual Representations
Scott C. Lowe, Anthony Fuller, Sageev Oore, Graham W. Taylor, Evan Shelhamer
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop (CAO)
Learning to Continually Learn via Meta-learning Agentic Memory Designs
Yiming Xiong, Shengran Hu, Jeff Clune
AI with Recursive Self-Improvement Workshop (RSI) &
Memory for LLM-Based Agentic Systems Workshop
LookSharp: Attention Entropy Minimization for Test-Time Adaptation
Yash Mali, Evan Shelhamer
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop (CAO) &
Test-Time Updates Workshop (TTU)
ProtoTTA: Prototype-Guided Test-Time Adaptation
Mohammad Mahdi Abootorabi, Parvin Mousavi, Purang Abolmaesumi, Evan Shelhamer
Test-Time Updates Workshop (TTU)
RDUMB++: Drift-Aware Continual Test-time Adaptation
Himanshu Mishra
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop (CAO) &
Test-Time Updates Workshop (TTU)
Self-Soupervision: Cooking and Seasoning Model Soups without Labels for Adaptation
Anthony Fuller, James R Green, Evan Shelhamer
Test-Time Updates Workshop (TTU)
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath, Marius Mosbach, Karolina Stanczak, Vered Shwartz, Siva Reddy
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop (CAO)
When is Model Souping Tasty? Similarity, Transitivity, and Robustness
Pierre Mackenzie, Simon Ghyselincks, Evan Shelhamer
Test-Time Updates Workshop (TTU)