| 2024 | Integrating Uncertainty Awareness into Conformalized Quantile Regression. | Raphael Rossellini, Rina Foygel Barber, Rebecca Willett |
| 2024 | An Impossibility Theorem for Node Embedding. | T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra |
| 2024 | Intrinsic Gaussian Vector Fields on Manifolds. | Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy |
| 2024 | Simulating weighted automata over sequences and trees with transformers. | Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau |
| 2024 | Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication. | Hugo Richard, Etienne Boursier, Vianney Perchet |
| 2024 | Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow. | Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal |
| 2024 | Learning Adaptive Kernels for Statistical Independence Tests. | Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou |
| 2024 | Understanding the Generalization Benefits of Late Learning Rate Decay. | Yinuo Ren, Chao Ma, Lexing Ying |
| 2024 | EM for Mixture of Linear Regression with Clustered Data. | Amirhossein Reisizadeh, Khashayar Gatmiry, Asuman E. Ozdaglar |
| 2024 | Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. | Tim Reichelt, Luke Ong, Tom Rainforth |
| 2024 | Categorical Generative Model Evaluation via Synthetic Distribution Coarsening. | Florence Regol, Mark Coates |
| 2024 | Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization. | Bahador Rashidi, Kerrick Johnstonbaugh, Chao Gao |
| 2024 | Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. | Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart |
| 2024 | BlockBoost: Scalable and Efficient Blocking through Boosting. | Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I. Oliveira, Paulo Orenstein |
| 2024 | Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. | Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtrik |
| 2024 | Distributionally Robust Model-based Reinforcement Learning with Large State Spaces. | Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic |
| 2024 | A General Algorithm for Solving Rank-one Matrix Sensing. | Lianke Qin, Zhao Song, Ruizhe Zhang |
| 2024 | Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems. | Chendi Qian, Didier Chtelat, Christopher Morris |
| 2024 | Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective. | Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow |
| 2024 | Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. | Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov, Alexander V. Gasnikov |
| 2024 | Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. | Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Bttner, Matthew B. Blaschko |
| 2024 | Efficient Conformal Prediction under Data Heterogeneity. | Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvth, Martin Takc, Eric Moulines, Maxim Panov |
| 2024 | On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem. | Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg |
| 2024 | Importance Matching Lemma for Lossy Compression with Side Information. | Buu Phan, Ashish Khisti, Christos Louizos |
| 2024 | Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach. | Ivan Peshekhonov, Aleksey Arzhantsev, Maxim V. Rakhuba |