| 2024 | Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. | Raphal Carpintero Perez, Sbastien Da Veiga, Josselin Garnier, Brian Staber |
| 2024 | Equivariant bootstrapping for uncertainty quantification in imaging inverse problems. | Marcelo Pereyra, Julin Tachella |
| 2024 | Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. | Pratik Patil, Yuchen Wu, Ryan J. Tibshirani |
| 2024 | On learning history-based policies for controlling Markov decision processes. | Gandharv Patil, Aditya Mahajan, Doina Precup |
| 2024 | Conformal Contextual Robust Optimization. | Yash P. Patel, Sahana Rayan, Ambuj Tewari |
| 2024 | Sum-max Submodular Bandits. | Stephen U. Pasteris, Alberto Rumi, Fabio Vitale, Nicol Cesa-Bianchi |
| 2024 | Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning. | Amey P. Pasarkar, Adji Bousso Dieng |
| 2024 | Density Uncertainty Layers for Reliable Uncertainty Estimation. | Yookoon Park, David M. Blei |
| 2024 | Safe and Interpretable Estimation of Optimal Treatment Regimes. | Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky |
| 2024 | Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. | Hristo Papazov, Scott Pesme, Nicolas Flammarion |
| 2024 | Deep anytime-valid hypothesis testing. | Teodora Pandeva, Patrick Forr, Aaditya Ramdas, Shubhanshu Shekhar |
| 2024 | Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. | Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava |
| 2024 | An Online Bootstrap for Time Series. | Nicolai Palm, Thomas Nagler |
| 2024 | Thompson Sampling Itself is Differentially Private. | Tingting Ou, Rachel Cummings, Marco Avella Medina |
| 2024 | Low-rank MDPs with Continuous Action Spaces. | Miruna Oprescu, Andrew Bennett, Nathan Kallus |
| 2024 | Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators. | Kenny Falkr Olsen, Rasmus M. Hoeegh Lindrup, Morten Mrup |
| 2024 | On the connection between Noise-Contrastive Estimation and Contrastive Divergence. | Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten |
| 2024 | Faster Recalibration of an Online Predictor via Approachability. | Princewill Okoroafor, Robert D. Kleinberg, Wen Sun |
| 2024 | Fair Machine Unlearning: Data Removal while Mitigating Disparities. | Alex Oesterling, Jiaqi Ma, Flvio P. Calmon, Himabindu Lakkaraju |
| 2024 | Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias. | Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko |
| 2024 | ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data. | Maria C. Novitasari, Johannes Quaas, Miguel Rodrigues |
| 2024 | Why is parameter averaging beneficial in SGD? An objective smoothing perspective. | Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu |
| 2024 | Corruption-Robust Offline Two-Player Zero-Sum Markov Games. | Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic |
| 2024 | Mixture-of-Linear-Experts for Long-term Time Series Forecasting. | Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti |
| 2024 | Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. | Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho |