Machine learning systems embed preferences either in training losses or through post-processing of calibrated predictions. Applying information design methods from Strack and Yang (2024), this paper ...

Primal-dual methods in online optimization give several of the state-of-the art results in both of the most common models: adversarial and stochastic/random order. Here we try to provide a more ...

Abstract: Policy Gradient is a policy-based reinforcement learning algorithm that approximates the optimal policy through a parametric function. The algorithm classifies the observations by softmax ...

A2Z/ ├── Problems/ # Solved problems organized by difficulty │ ├── Easy/ # Easy level problems │ ├── Medium/ # Medium level problems │ └── Hard/ # Hard level problems │ ├── DataStructures/ # Core data ...

1 School of Management, University of Shanghai for Science and Technology, Shanghai, China 2 Institute of Mathematical Sciences ICMAT-CSIC, Madrid, Spain In the open capacitated location-routing ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...

AlphaEvolve uses large language models to find new algorithms that outperform the best human-made solutions for data center management, chip design, and more. Google DeepMind has once again used large ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do it for some critical optimization tasks. For ...