Optimization
Revisiting Implicit Differentiation for Learning Problems in Optimal Control
In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system.
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
One such example is two-player zero-sum Markov games, in which efficient ways to compute a Nash equilibrium are known. Inspired by zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of zero-sum
Gradient-free Decoder Inversion in Latent Diffusion Models
For example, recent video LDMs can generate more than 16 frames, but GPUs with 24 GB memory can only perform gradient-based decoder inversion for 4 frames.
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning Yiqin Lv Qi Wang
Accordingly, extensive evaluations demonstrate the significance of our proposal and its scalability to multimodal large models in boosting robustness.
Policy Gradient for Rectangular Robust Markov Decision Processes Anonymous Author(s) Affiliation Address email
We provide a closed-form expression for the worst occupation measure.
Policy Gradient for Rectangular Robust Markov Decision Processes
However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive.
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data.
