| 2023 | Efficient fair PCA for fair representation learning. | Matthus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar |
| 2023 | The Lie-Group Bayesian Learning Rule. | Eren Mehmet Kiral, Thomas Mllenhoff, Mohammad Emtiyaz Khan |
| 2023 | Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles. | Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes |
| 2023 | Characterizing Internal Evasion Attacks in Federated Learning. | Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong |
| 2023 | Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. | Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh |
| 2023 | Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE. | Young-Geun Kim, Ying Liu, Xuexin Wei |
| 2023 | SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval. | Minyoung Kim |
| 2023 | Convolutional Persistence as a Remedy to Neural Model Analysis. | Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh |
| 2023 | Adversarial robustness of VAEs through the lens of local geometry. | Asif Khan, Amos Storkey |
| 2023 | Barlow Graph Auto-Encoder for Unsupervised Network Embedding. | Rayyan Ahmad Khan, Martin Kleinsteuber |
| 2023 | Diffusion Generative Models in Infinite Dimensions. | Gavin Kerrigan, Justin Ley, Padhraic Smyth |
| 2023 | Rank-Based Causal Discovery for Post-Nonlinear Models. | Grigor Keropyan, David Strieder, Mathias Drton |
| 2023 | Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics. | Masahiro Kato, Masaaki Imaizumi, Kentaro Minami |
| 2023 | Neural Discovery of Permutation Subgroups. | Pavan Karjol, Rohan Kashyap, Prathosh AP |
| 2023 | Robust and Agnostic Learning of Conditional Distributional Treatment Effects. | Nathan Kallus, Miruna Oprescu |
| 2023 | Average Adjusted Association: Efficient Estimation with High Dimensional Confounders. | Sung Jae Jun, Sokbae Lee |
| 2023 | Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior. | Yohan Jung, Jinkyoo Park |
| 2023 | Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion. | Haotian Ju, Dongyue Li, Aneesh Sharma, Hongyang R. Zhang |
| 2023 | Federated Learning under Distributed Concept Drift. | Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons |
| 2023 | Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. | Felix Jimenez, Matthias Katzfuss |
| 2023 | A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. | Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani |
| 2023 | Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. | Neil Jethani, Adriel Saporta, Rajesh Ranganath |
| 2023 | Factorial SDE for Multi-Output Gaussian Process Regression. | Daniel P. Jeong, Seyoung Kim |
| 2023 | Nearly Optimal Latent State Decoding in Block MDPs. | Yassir Jedra, Junghyun Lee, Alexandre Proutire, Se-Young Yun |
| 2023 | Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees. | Joseph Janssen, Vincent Guan, Elina Robeva |