Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We appreciate the reviewers' efforts and suggestions (in blue)! We will answer the shared question and then reply to each reviewer. Tasks may prefer different distance metrics, but most physical systems have their own predefined ones, e.g., for We will add discussion of those works. For fair comparison, we need to modify either HER/CHER or baselines, like [Nair et. How sensitive is the performance to this parameter?

Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for a wide class of submodular problems under various types of constraints while clearly outperforming standard greedy approximation algorithms. This paper introduces a setup for benchmarking algorithms for submodular optimization problems with the aim to provide researchers with a framework to enhance and compare the performance of new algorithms for submodular problems. The focus is on the development of iterative search algorithms such as evolutionary algorithms with the implementation provided and integrated into IOHprofiler which allows for tracking and comparing the progress and performance of iterative search algorithms. We present a range of submodular optimization problems that have been integrated into IOHprofiler and show how the setup can be used for analyzing and comparing iterative search algorithms in various settings.

One might assume that when your boss finally comes to tell you that the robots are here to do your job, he won't also point out with enthusiasm that they're going to do it 10 times better than you did. Alas, this was not the case at BuzzFeed. Yesterday, at a virtual all-hands meeting, BuzzFeed CEO Jonah Peretti had some news to discuss about the automated future of media. The brand, known for massively viral stories aggregated from social media and being the most notable progenitor of what some might call clickbait, would begin publishing content generated by artificial-intelligence programs. In other words: Robots would help make BuzzFeed posts. "When you see this work in action it is pretty amazing," Peretti had promised employees in a memo earlier in the day.

Imagine: social distancing restrictions are over. It's safe(r) to go out again! For once, after a long, grueling era of pandemic stress, you make plans to go out to a special public event. It hasn't happened for the longest time. Clearly, this is a cause for celebration and what else to mark the occasion than to dress yourself up a little?

For online advertising in e-commerce, the traditional problem is to assign the right ad to the right user on fixed ad slots. In this paper, we investigate the problem of advertising with adaptive exposure, in which the number of ad slots and their locations can dynamically change over time based on their relative scores with recommendation products. In order to maintain user retention and long-term revenue, there are two types of constraints that need to be met in exposure: query-level and day-level constraints. We model this problem as constrained markov decision process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning to decouple the original advertising exposure optimization problem into two relatively independent sub-optimization problems. We also propose a constrained hindsight experience replay mechanism to accelerate the policy training process. Experimental results show that our method can improve the advertising revenue while satisfying different levels of constraints under the real-world datasets. Besides, the proposal of constrained hindsight experience replay mechanism can significantly improve the training speed and the stability of policy performance.

In 1995's Clueless, you may recall Cher Horowitz using cutting-edge software to select her plaid ensemble. Cher's machine could identify chic head-to-toe looks, adding a small dose of sci-fi to the romcom classic. Twenty-two years later, the 90s fiction movie is closer than ever to reality: Artificial intelligence in fashion is here, but it's still unclear what its role is meant to be. As a form of personal expression, fashion may seem like a strange target for AI disruption. Regardless, machine learning is taking on a variety of fashion-related roles. These include personal shopper, fashion designer, model, and--just like Cher's program--stylist.

Would you trust a robot to put away your clean laundry, then help you pick out an outfit? He's the brains behind Threadrobe, a D.C.-based startup that built a wardrobe that automatically separates, identifies, and stores your clothes--then dispenses them in a complete outfit at your command. "We call it automated furniture, but it's kind of a hybrid of furniture, appliances, simple robotics, and software," he said. Users put clean clothes in a drawer at the bottom of the unit, and the Threadrobe's robotic arm hangs them inside. When it's time to get dressed, the process works in reverse--and the arm dispenses a complete outfit into a steamer that refreshes and dewrinkles garments.

David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend. This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.