Solomon, Justin

33 publications

TMLR 2025 Closed-Form Diffusion Models Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon

ICML 2025 Compress Then Serve: Serving Thousands of LoRA Adapters with Little Overhead Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

TMLR 2025 Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning Rickard Brüel Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon

NeurIPS 2025 Locality in Image Diffusion Models Emerges from Data Statistics Artem Lukoianov, Chenyang Yuan, Justin Solomon, Vincent Sitzmann

ICML 2024 Asymmetry in Low-Rank Adapters of Foundation Models Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez De Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon

ICLRW 2024 Asymmetry in Low-Rank Adapters of Foundation Models Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon

ICMLW 2024 Compress Then Serve: Serving Thousands of LoRA Adapters with Little Overhead Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

NeurIPS 2024 Nuclear Norm Regularization for Deep Learning Christopher Scarvelis, Justin Solomon

NeurIPS 2024 Score Distillation via Reparametrized DDIM Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald, Vitor Campagnolo Guizilini, Timur Bagautdinov, Vincent Sitzmann, Justin Solomon

ICML 2024 Slicing Mutual Information Generalization Bounds for Neural Networks Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson, Justin Solomon

TMLR 2023 $k$-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien

NeurIPSW 2023 LLM Routing with Benchmark Datasets Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin

ICLR 2023 Learning Proximal Operators to Discover Multiple Optima Lingxiao Li, Noam Aigerman, Vladimir Kim, Jiajin Li, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

NeurIPSW 2023 Outlier-Robust Group Inference via Gradient Space Clustering Yuchen Zeng, Kristjan Greenewald, Luann Jung, Kangwook Lee, Justin Solomon, Mikhail Yurochkin

TMLR 2023 Rewiring with Positional Encodings for Graph Neural Networks Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon

ICLR 2023 Riemannian Metric Learning via Optimal Transport Christopher Scarvelis, Justin Solomon

ICLR 2023 Sampling with Mollified Interaction Energy Descent Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon

ICMLW 2023 Slicing Mutual Information Generalization Bounds for Neural Networks Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson, Justin Solomon

CVPR 2022 DeepCurrents: Learning Implicit Representations of Shapes with Boundaries David Palmer, Dmitriy Smirnov, Stephanie Wang, Albert Chern, Justin Solomon

CoRL 2022 Representation Learning for Object Detection from Unlabeled Point Cloud Sequences Xiangru Huang, Yue Wang, Vitor Campagnolo Guizilini, Rares Andrei Ambrus, Adrien Gaidon, Justin Solomon

ICLR 2021 Continuous Wasserstein-2 Barycenter Estimation Without Minimax Optimization Alexander Korotin, Lingxiao Li, Justin Solomon, Evgeny Burnaev

CoRL 2021 DETR3D: 3D Object Detection from Multi-View Images via 3D-to-2D Queries Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, Justin Solomon

UAI 2021 Improving Approximate Optimal Transport Distances Using Quantization Gaspard Beugnot, Aude Genevay, Kristjan Greenewald, Justin Solomon

JMLR 2021 Incorporating Unlabeled Data into Distributionally Robust Learning Charlie Frogner, Sebastian Claici, Edward Chien, Justin Solomon

ICLR 2021 Learning Manifold Patch-Based Representations of Man-Made Shapes Dmitriy Smirnov, Mikhail Bessmeltsev, Justin Solomon

CVPR 2021 Polygonal Building Extraction by Frame Field Learning Nicolas Girard, Dmitriy Smirnov, Justin Solomon, Yuliya Tarabalka

ICML 2020 Model Fusion with Kullback-Leibler Divergence Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon

ECCV 2020 Pillar-Based Object Detection for Autonomous Driving Yue Wang, Alireza Fathi, Abhijit Kundu, David A. Ross, Caroline Pantofaru, Tom Funkhouser, Justin Solomon

ICLR 2019 Learning Embeddings into Entropic Wasserstein Spaces Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon

ICML 2018 Stochastic Wasserstein Barycenters Sebastian Claici, Edward Chien, Justin Solomon

ICML 2016 Gromov-Wasserstein Averaging of Kernel and Distance Matrices Gabriel Peyré, Marco Cuturi, Justin Solomon

ICML 2015 Exponential Integration for Hamiltonian Monte Carlo Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha

ICML 2014 Wasserstein Propagation for Semi-Supervised Learning Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher