An Editor-in-Chief invited the authors of ten submissions headed for desk rejection to discuss their papers; most could not answer basic or technical questions about their own work.

News, announcements, and editorial notes from the Transactions on Machine Learning Research (TMLR).

By Nihar B. Shah

Three things are true today:

TMLR submissions have grown substantially in recent times (Figure 1). In some cases, we have seen authors submit as many as five papers in…

2025 TMLR Annual Report Gautam Kamath Hugo Larochelle Naila Murray Nihar Shah Paul Vicol Summary The Transactions on Machine Learning Research (TMLR) is now almost 4 years old! At the time of writing...

By Gautam Kamath, on behalf of the 2025 TMLR Outstanding Paper Committee: Pablo Samuel Castro, Pin-Yu Chen, Vincent Dumoulin, Amir-massoud…

Gradient scarcity emerges when learning graphs by minimizing a loss on a subset of nodes under the semi-supervised setting. It consists in edges between unlabeled nodes that are far from the...

Recent efforts have augmented large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning,...

The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science,...

Large multimodal models (LMMs) have shown great results in single-image vision language tasks. However, their abilities to solve multi-image visual language tasks is yet to be improved. The...

Please fill out this form to volunteer to be a reviewer for Transactions on Machine Learning Research. More details on the journal are available on the website; in particular, check out the submissio...

2024 TMLR Annual Report Kyunghyun Cho Gautam Kamath Hugo Larochelle Naila Murray Paul Vicol Summary Editorial Team Submissions and Decisions Problematic Case(s) and Policies Certifications OpenReview...

By the 2024 TMLR Outstanding Paper Committee: Michael Bowling, Brian Kingsbury, Andreas Kirsch, Yingzhen Li, and Eleni Triantafillou

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large...

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could...