16 papers
Inverting Data Transformations via Diffusion Sampling
Jinwoo Kim, Sékou-Oumar Kaba, Jiyun Park +2
We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to recover an inverse transformation tha…
Multi-Armed Sampling Problem and the End of Exploration
Mohammad Pedramfar, Siamak Ravanbakhsh
This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed bandits. Our primary motivation is…
The Role of Symmetry in Optimizing Overparameterized Networks
Kusha Sareen, Mohammad Pedramfar, Sékou-Oumar Kaba +2
Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We analyze weight-space symmet…
Scaling Laws and Symmetry, Evidence from Neural Force Fields
Khang Ngo, Siamak Ravanbakhsh
We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even more at larger scales; we show a clear power-law scali…
Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism
Tianwei Ni, Esther Derman, Vineet Jain +3
Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons. We question the universality o…
Symmetry-Aware Generative Modeling through Learned Canonicalization
Kusha Sareen, Daniel Levy, Arnab Kumar Mondal +3
Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The existing generative modeling paradigm for…