Aryan Dokania
Research Subjects
Aryan Dokania has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Bo Zhao
University of California San Diego Flatiron Institute Northeastern University University of California San Diego
1 shared publication - Guy Zamir
Department of Computer Sciences University of Wisconsin-Madison
1 shared publication - Rose Yu
Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
1 shared publication
Latest Publications
- Open Access
Improving Learning to Optimize Using Parameter Symmetries
We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry transformations and local updates improves meta-optimizer performance. Supporting this, our theoretical analysis demonstrates that even without identifying the optimal group element, the method locally resembles Newton's method. We further provide an example where the algorithm provably learns the correct symmetry transformation during training. To empirically evaluate L2O with teleportation, we introduce a benchmark, analyze its success and failure cases, and show that enhancements like momentum further improve performance. Our results highlight the potential of leveraging neural network parameter space symmetry to advance meta-optimization.
Open AccessImproving Learning to Optimize Using Parameter Symmetries
We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry transformations and local updates improves meta-optimizer performance. Supporting this, our theoretical analysis demonstrates that even without identifying the optimal group element, the method locally resembles Newton's method. We further provide an example where the algorithm provably learns the correct symmetry transformation during training. To empirically evaluate L2O with teleportation, we introduce a benchmark, analyze its success and failure cases, and show that enhancements like momentum further improve performance. Our results highlight the potential of leveraging neural network parameter space symmetry to advance meta-optimization.