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Improving Alignment and Robustness with Circuit Breakers
AI systems can take harmful actions and are highly vulnerable to adversarial attacks.
Scaling Law for Time Series Forecasting
Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning.
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.
Fairness-Aware Meta-Learning via Nash Bargaining Yi Zeng 1, Xuelin Y ang 2, Li Chen
To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set.
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
