5 citations · 21 across the 23 of their papers we have counts for
6 papers · 1 filter
CAT-Flow: Curvature-Adaptive sTeps for Flow Matching
Qinchan Li, Pedro Cisneros-Velarde, Keru Fu +3
Flow Matching has emerged as a leading framework for generative modeling, powering state-of-the-art systems such as FLUX and Stable Diffusion 3.5. However, the iterative nature of…
Optimization for Neural Operators can Benefit from Width
Pedro Cisneros-Velarde, Bhavesh Shrimali, Arindam Banerjee
Neural Operators that directly learn mappings between function spaces, such as Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs), have received considerable attenti…
Optimization and Generalization Guarantees for Weight Normalization
Pedro Cisneros-Velarde, Zhijie Chen, Sanmi Koyejo +1
Weight normalization (WeightNorm) is widely used in practice for the training of deep neural networks and modern deep learning libraries have built-in implementations of it. In thi…
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation
Pedro Cisneros-Velarde, Sanmi Koyejo
Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium o…
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu +1
We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initializatio…
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo +1
Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically. We study the…