Justin Solomon
24 papers · 2014–2025 · 8 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (52) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🧭
Keyword Pioneer
🗃️
Keyword Collector
(121)
📈
Trend Setter
💎
Century Club · Bronze
🔥
Unstoppable · Bronze
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Prolific Year · Productive
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(13)
Deep Learning
(10)
Mathematics & Optimization
(9)
Artificial Intelligence
(8)
Core Methods
(8)
Mathematics
(7)
Computer Vision
(6)
Analysis
(5)
Core AI
(5)
Optimization
(5)
Architectures
(4)
Models
(4)
Application Areas
(3)
Bayesian & Probabilistic
(3)
Keywords
optimal transport
(7)
representation learning
(3)
probability measure
(3)
probability distribution
(2)
loras
(2)
metric learning
(2)
shape representation
(2)
3d object detection
(2)
wasserstein barycenter
(2)
semi-supervised learning
(2)
wasserstein distance
(2)
generalization bound
(2)
neural network
(2)
regularization
(1)
variational inference
(1)
semantic segmentation
(1)
non-convex optimization
(1)
image generation
(1)
3d reconstruction
(1)
active learning
(1)
Papers
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
ICML 2025
Asymmetry in Low-Rank Adapters of Foundation Models
ICML 2024
Nuclear Norm Regularization for Deep Learning
NeurIPS 2024
Score Distillation via Reparametrized DDIM
NeurIPS 2024
Slicing Mutual Information Generalization Bounds for Neural Networks
ICML 2024
Learning Proximal Operators to Discover Multiple Optima
ICLR 2023
Riemannian Metric Learning via Optimal Transport
ICLR 2023
Sampling with Mollified Interaction Energy Descent
ICLR 2023
DeepCurrents: Learning Implicit Representations of Shapes With Boundaries
CVPR 2022
Representation Learning for Object Detection from Unlabeled Point Cloud Sequences
CoRL 2022
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
ICLR 2021
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
CoRL 2021
Improving approximate optimal transport distances using quantization
UAI 2021
Incorporating Unlabeled Data into Distributionally Robust Learning
JMLR 2021
Learning Manifold Patch-Based Representations of Man-Made Shapes
ICLR 2021
Polygonal Building Extraction by Frame Field Learning
CVPR 2021
Deep Parametric Shape Predictions Using Distance Fields
CVPR 2020
Model Fusion with Kullback-Leibler Divergence
ICML 2020
Pillar-based Object Detection for Autonomous Driving
ECCV 2020
Learning Embeddings into Entropic Wasserstein Spaces
ICLR 2019
Stochastic Wasserstein Barycenters
ICML 2018
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
ICML 2016
Exponential Integration for Hamiltonian Monte Carlo
ICML 2015
Wasserstein Propagation for Semi-Supervised Learning
ICML 2014