| 2024 | How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability. | Jorge Garca-Carrasco, Alejandro Mat, Juan C. Trujillo |
| 2024 | Fusing Individualized Treatment Rules Using Secondary Outcomes. | Daiqi Gao, Yuanjia Wang, Donglin Zeng |
| 2024 | Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate. | Hongchang Gao |
| 2024 | Contextual Bandits with Budgeted Information Reveal. | Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan A. Murphy |
| 2024 | Probabilistic Integral Circuits. | Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur |
| 2024 | Offline Primal-Dual Reinforcement Learning for Linear MDPs. | Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka Okolo |
| 2024 | Information-theoretic Analysis of Bayesian Test Data Sensitivity. | Futoshi Futami, Tomoharu Iwata |
| 2024 | Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. | Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon |
| 2024 | Training Implicit Generative Models via an Invariant Statistical Loss. | Jos Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vazquez Lopez, Joaqun Mguez |
| 2024 | Scalable Learning of Item Response Theory Models. | Susanne Frick, Amer Krivosija, Alexander Munteanu |
| 2024 | SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. | Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi |
| 2024 | Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data. | Kei Sen Fong, Mehul Motani |
| 2024 | The Risks of Recourse in Binary Classification. | Hidde Fokkema, Damien Garreau, Tim van Erven |
| 2024 | Symmetric Equilibrium Learning of VAEs. | Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov |
| 2024 | Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. | Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut |
| 2024 | Monitoring machine learning-based risk prediction algorithms in the presence of performativity. | Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio |
| 2024 | Is this model reliable for everyone? Testing for strong calibration. | Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner |
| 2024 | Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. | Ilyas Fatkhullin, Niao He |
| 2024 | Fast and Adversarial Robust Kernelized SDU Learning. | Yajing Fan, Wanli Shi, Yi Chang, Bin Gu |
| 2024 | RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model. | Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang |
| 2024 | Self-Compatibility: Evaluating Causal Discovery without Ground Truth. | Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing |
| 2024 | Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. | Mathieu Even, Anastasia Koloskova, Laurent Massouli |
| 2024 | Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex. | Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano, Ryoko Tokuhisa, Takuro Kutsuna |
| 2024 | NoisyMix: Boosting Model Robustness to Common Corruptions. | N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney |
| 2024 | Mixed Models with Multiple Instance Learning. | Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale |