We study the problem of how to repeatedly sell to a buyer running a no-regret,mean-based algorithm. Previous work [Braverman et al., 2018] shows that it ispossible to design effective mechanisms in such a setting that extract almost allof the economic surplus, but these mechanisms require the buyer's values each

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

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

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

In search advertising, the search engine needs to select the most profitable advertisements to display, which can be formulated as an instance of online learning with partial feedback, also known as the stochastic multi-armed bandit (MAB) problem. In this paper, we show that the naive application of MAB algorithms to search advertising for advertisement selection will produce sample selection bias that harms the search engine by decreasing expected revenue and "estimation of the largest mean" (ELM) bias that harms the advertisers by increasing game-theoretic player-regret. We then propose simple bias-correction methods with benefits to both the search engine and the advertisers.

The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.

Promotions and discounts are essential components of modern e-commerce platforms, where they are often used to incentivize customers towards purchase completion. Promotions also affect revenue and may incur a monetary loss that is often limited by a dedicated promotional budget. We study the Online Constrained Multiple-Choice Promotions Personalization Problem, where the optimization goal is to select for each customer which promotion to present in order to maximize purchase completions, while also complying with global budget limitations. Our work formalizes the problem as an Online Multiple Choice Knapsack Problem and extends the existent literature by addressing cases with negative weights and values. We provide a real-time adaptive method that guarantees budget constraints compliance and achieves above 99.7% of the optimal promotional impact on various datasets. Our method is evaluated on a large-scale experimental study at one of the leading online travel platforms in the world.

AI is not only a powerful tool, but it is also can be a highly risky technology if used incorrectly. No matter how much you take care of, there might be some lurking risk, which is either unknown or uncontrollable, and that means you need a different way of risk-management. If you perform a thorough pre-mortem analysis, ensure that training and testing are complete, and stress test all the systems with red teams' help, you can be almost confident about the system's performance. However, there might be some unidentified or identified risk but can't be anticipated or controlled. Such residual risk can be dealt with by way of transference. Transference of risk transfer is a risk management and control strategy that involves the contractual shifting of a pure risk from one party to another. One example is the purchase of an insurance policy, by which a policyholder passes the specified risk of loss to the insurer.

This work is motivated by a practical concern from our retail partner. While they respect the advantages of dynamic pricing, they must limit the number of price changes to be within some constant. We study the classical price-based network revenue management problem, where a retailer has finite initial inventory of multiple resources to sell over a finite time horizon. We consider both known and unknown distribution settings, and derive policies that have the best-possible asymptotic performance in both settings. Our results suggest an intrinsic difference between the expected revenue associated with how many switches are allowed, which further depends on the number of resources. Our results are also the first to show a separation between the regret bounds associated with different number of resources.

UK companies that are too slow to adopt artificial intelligence technology are at risk of losing 20% of their cash flow, according to McKinsey Global Institute. New figures released by consultancy firm's research arm claimed that the UK's economy could be boosted 22% by AI alone on the next decade, with fast-moving businesses potentially growing 120% if they invest in AI tools. It claimed the UK is "potentially more AI-ready compared with the global average", but could miss out on the opportunity if investment does not occur. "The United Kingdom has impressive pockets of innovation but is failing to scale to business more broadly," the report stated. Google's DeepMind AI division was cited at an example of these innovation pockets, with McKinsey suggested that companies can replicate by achieving growth through AI by offering it at scale, investing in talent and forging links between cutting-edge research and commercial success.