| 2024 | Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation. | Qing En, Yuhong Guo |
| 2024 | Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. | Konstantinos Emmanouilidis, Ren Vidal, Nicolas Loizou |
| 2024 | On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. | Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schnlieb |
| 2024 | General Tail Bounds for Non-Smooth Stochastic Mirror Descent. | Khaled Eldowa, Andrea Paudice |
| 2024 | Approximate Control for Continuous-Time POMDPs. | Yannick Eich, Bastian Alt, Heinz Koeppl |
| 2024 | Analysis of Kernel Mirror Prox for Measure Optimization. | Pavel E. Dvurechensky, Jia-Jie Zhu |
| 2024 | CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference. | Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi |
| 2024 | The Solution Path of SLOPE. | Xavier Dupuis, Patrick Tardivel |
| 2024 | Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. | Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos |
| 2024 | Free-form Flows: Make Any Architecture a Normalizing Flow. | Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Kthe |
| 2024 | Certified private data release for sparse Lipschitz functions. | Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hnig, Fanny Yang |
| 2024 | Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. | Jing Dong, Baoxiang Wang, Yaoliang Yu |
| 2024 | Bayesian Semi-structured Subspace Inference. | Daniel Dold, David Rgamer, Beate Sick, Oliver Drr |
| 2024 | MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization. | Nguyen Hoang Khoi Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai |
| 2024 | Resilient Constrained Reinforcement Learning. | Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro |
| 2024 | GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models. | Tolga Dimlioglu, Anna Choromanska |
| 2024 | Mixed variational flows for discrete variables. | Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell |
| 2024 | Conformalized Deep Splines for Optimal and Efficient Prediction Sets. | Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia |
| 2024 | On the Expected Size of Conformal Prediction Sets. | Guneet S. Dhillon, George Deligiannidis, Tom Rainforth |
| 2024 | Probabilistic Calibration by Design for Neural Network Regression. | Victor Dheur, Souhaib Ben Taieb |
| 2024 | Online Calibrated and Conformal Prediction Improves Bayesian Optimization. | Shachi Deshpande, Charles Marx, Volodymyr Kuleshov |
| 2024 | Learning-Based Algorithms for Graph Searching Problems. | Adela Frances DePavia, Erasmo Tani, Ali Vakilian |
| 2024 | On the Generalization Ability of Unsupervised Pretraining. | Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi |
| 2024 | Sample Complexity Characterization for Linear Contextual MDPs. | Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang |
| 2024 | Benchmarking Observational Studies with Experimental Data under Right-Censoring. | Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag |