TMLR

Identifiable Deep Generative Models via Sparse Decoding

Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei

Comments Transactions on Machine Learning Research (2022)

Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review

Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi

Comments Accepted by TMLR. Source code: https://github.com/king/ctdg-info-flow

ResiDual Transformer Alignment with Spectral Decomposition

Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, Francesco Locatello

Comments Published in Transactions on Machine Learning Research (TMLR)

An Embedding is Worth a Thousand Noisy Labels

Francesco Di Salvo, Sebastian Doerrich, Ines Rieger, Christian Ledig

Comments Accepted to Transactions on Machine Learning Research (TMLR)

Why is constrained neural language generation particularly challenging?

Cristina Garbacea, Qiaozhu Mei

Comments This survey is specifically focused on constrained neural language generation. For a more general survey of NLG literature, please see "Neural language generation: Formulation, methods, and evaluation" at arXiv:2007.15780

Journal ref Published in Transactions on Machine Learning Research (02/2025)

Rethinking Patch Dependence for Masked Autoencoders

Letian Fu, Long Lian, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala, Trevor Darrell, Alexei A. Efros, Ken Goldberg

Comments Transactions on Machine Learning Research (TMLR) 2025

No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning

Manu Gaur, Darshan Singh, Makarand Tapaswi

Comments Published at Transactions on Machine Learning Research (TMLR) https://openreview.net/forum?id=gqh0yzPYdo

Memory-Modular Classification: Learning to Generalize with Memory Replacement

Dahyun Kang, Ahmet Iscen, Eunchan Jo, Sua Choi, Minsu Cho, Cordelia Schmid

Comments Accepted to TMLR. Code available: https://github.com/dahyun-kang/mml

Scaling Laws for Predicting Downstream Performance in LLMs

Yangyi Chen, Binxuan Huang, Yifan Gao, Zhengyang Wang, Jingfeng Yang, Heng Ji

Comments Accepted to TMLR

Archetypal Analysis++: Rethinking the Initialization Strategy

Sebastian Mair, Jens Sjölund

Comments 27 pages, 17 figures, accepted at the Transactions on Machine Learning Research

Journal ref Transactions on Machine Learning Research (04/2024)

On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals

Rudi Coppola, Manuel Mazo

Journal ref Transactions on Machine Learning Research (2025)

FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting

Liyao Jiang, Negar Hassanpour, Mohammad Salameh, Mohan Sai Singamsetti, Fengyu Sun, Wei Lu, Di Niu

Comments TMLR 2025

Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting

Inkyu Shin, Qihang Yu, Xiaohui Shen, In So Kweon, Kuk-Jin Yoon, Liang-Chieh Chen

Comments Accepted to TMLR 2025. Project page at https://video-3dgs-project.github.io/

Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction

Makoto Takamoto, Daniel Oñoro-Rubio, Wiem Ben Rim, Takashi Maruyama, Bhushan Kotnis

Comments 11 pages, 6 figures, 15 Tables, accepted and to be published in TMLR

Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning

Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo

Comments Published at TMLR: https://openreview.net/forum?id=Lt2H8Bd8jF

What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias

Aida Mohammadshahi, Yani Ioannou

Comments Published in Transactions on Machine Learning Research (TMLR), March 2024. https://openreview.net/forum?id=xBbj46Y2fN

Journal ref Transactions on Machine Learning Research, 2835-8856, March 2025. https://openreview.net/forum?id=xBbj46Y2fN

DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting

Yuxuan Shu, Vasileios Lampos

Comments Published in Transactions on Machine Learning Research (04/2025). The code is available at https://github.com/ClaudiaShu/DeformTime

Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models

Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour, Ali Etemad

Comments Accepted in Transactions on Machine Learning Research (TMLR)

RedMotion: Motion Prediction via Redundancy Reduction

Royden Wagner, Omer Sahin Tas, Marvin Klemp, Carlos Fernandez, Christoph Stiller

Comments TMLR published version

Journal ref Transactions on Machine Learning Research (05/2024)

Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott

Comments 27 pages, 22 figures

Journal ref Transactions on Machine Learning Research (TMLR), March 2025, https://openreview.net/forum?id=dNWaTuKV9M

Emergent representations in networks trained with the Forward-Forward algorithm

Niccolò Tosato, Lorenzo Basile, Emanuele Ballarin, Giuseppe de Alteriis, Alberto Cazzaniga, Alessio Ansuini

Comments Published in Transactions on Machine Learning Research (TMLR)

Probabilistic neural operators for functional uncertainty quantification

Christopher Bülte, Philipp Scholl, Gitta Kutyniok

Journal ref Transactions on Machine Learning Research, 2025. ISSN 2835-8856

Implicit Bias and Fast Convergence Rates for Self-attention

Bhavya Vasudeva, Puneesh Deora, Christos Thrampoulidis

Comments Accepted in TMLR, 43 pages, 10 figures

Sample, estimate, aggregate: A recipe for causal discovery foundation models

Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola

Comments Our code is available at https://github.com/rmwu/sea

Journal ref Transactions on Machine Learning Research (03/2025)

Adaptive Multi-step Refinement Network for Robust Point Cloud Registration

Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann

Comments Accepted at TMLR'25

Personalized Privacy Amplification via Importance Sampling

Dominik Fay, Sebastian Mair, Jens Sjölund

Comments 28 pages, 7 figures

Journal ref Transactions on Machine Learning Research (12/2024)

An Efficient Training Algorithm for Models with Block-wise Sparsity

Ding Zhu, Zhiqun Zuo, Mohammad Mahdi Khalili

Comments 24 pages, submitted on Transactions on Machine Learning Research

Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach

Mahrokh Ghoddousi Boroujeni, Andreas Krause, Giancarlo Ferrari Trecate

Journal ref Boroujeni, M. G., Krause, A., & Ferrari-Trecate, G. (2025). Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach. Transactions on Machine Learning Research

Decomposing The Dark Matter of Sparse Autoencoders

Joshua Engels, Logan Riggs, Max Tegmark

Comments Published in TMLR. Code at https://github.com/JoshEngels/SAE-Dark-Matter

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps

Mikko A. Heikkilä

Comments 22 pages. Published in TMLR 03/2025: https://openreview.net/forum?id=uxyWlXPuIg