| 2025 | CVPR | Dual Diffusion for Unified Image Generation and Understanding. | Zijie Li, Henry Li, Yichun Shi, Amir Barati Farimani, Yuval Kluger, Linjie Yang, Peng Wang |
| 2025 | ICML | Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data. | Erez Peterfreund, Ofir Lindenbaum, Yuval Kluger, Boris Landa |
| 2024 | ICASSP | Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. | Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon |
| 2024 | ICLR | Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps. | Henry Li, Ronen Basri, Yuval Kluger |
| 2024 | UAI | Transductive and Inductive Outlier Detection with Robust Autoencoders. | Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger |
| 2023 | ICLR | GEASS: Neural causal feature selection for high-dimensional biological data. | Mingze Dong, Yuval Kluger |
| 2023 | ICML | Few-Sample Feature Selection via Feature Manifold Learning. | David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon |
| 2023 | ICML | Towards Understanding and Reducing Graph Structural Noise for GNNs. | Mingze Dong, Yuval Kluger |
| 2023 | UAI | Multi-modal differentiable unsupervised feature selection. | Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe |
| 2022 | AISTATS | Crowdsourcing Regression: A Spectral Approach. | Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger |
| 2022 | ICLR | L0-Sparse Canonical Correlation Analysis. | Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger |
| 2022 | ICML | Neural Inverse Transform Sampler. | Henry Li, Yuval Kluger |
| 2022 | ICML | Locally Sparse Neural Networks for Tabular Biomedical Data. | Junchen Yang, Ofir Lindenbaum, Yuval Kluger |
| 2020 | ICML | Feature Selection using Stochastic Gates. | Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger |
| 2018 | ICLR | SpectralNet: Spectral Clustering using Deep Neural Networks. | Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger |
| 2018 | ICML | Learning Binary Latent Variable Models: A Tensor Eigenpair Approach. | Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler |
| 2016 | AISTATS | Unsupervised Ensemble Learning with Dependent Classifiers. | Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger |
| 2016 | ICML | A Deep Learning Approach to Unsupervised Ensemble Learning. | Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger |
| 2015 | AISTATS | Estimating the accuracies of multiple classifiers without labeled data. | Ariel Jaffe, Boaz Nadler, Yuval Kluger |