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

Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian process bandit policies are designed to handle a large (or infinite) number of arms in stationary reward environments under constraints on the reward function. In this manuscript, we propose a novel policy to learn reward environments over a continuous space using Gaussian interpolation. We show that our method efficiently learns continuous Lipschitz reward functions with $\mathcal{O}^*(\sqrt{T})$ cumulative regret. Furthermore, our method naturally extends to non-stationary problems with a simple modification. We finally demonstrate that our method is computationally favorable (100-10000x faster) and experimentally outperforms sliding Gaussian process policies on datasets with non-stationarity and an extremely large number of arms.

Anyone who owned an Apple device over the last decade may be able to claim part of a 95 million class action lawsuit against the tech giant. According to the lawsuit, iPhones, iPads, Apple Watches, and MacBooks dating back to 2014 may have secretly recorded their users' private conversations after the devices unintentionally activated Apple's voice assistant Siri. A notice about the case, Lopez v. Apple, has advised anyone who believes Siri spied on their confidential or private calls between September 17, 2014 and December 31, 2024 to submit a claim for damages. Apple's iMacs, Apple TV streaming boxes, HomePod speakers, and iPod Touches are also included in the lawsuit. Although Apple has denied that their devices spied on users, the 3 trillion company reached a settlement in the case, agreeing to give users up to 20 per Siri device in their claim.

We introduce SWE-Lancer, a benchmark of over 1,400 freelance software engineering tasks from Upwork, valued at \$1 million USD total in real-world payouts. SWE-Lancer encompasses both independent engineering tasks--ranging from \$50 bug fixes to \$32,000 feature implementations--and managerial tasks, where models choose between technical implementation proposals. Independent tasks are graded with end-to-end tests triple-verified by experienced software engineers, while managerial decisions are assessed against the choices of the original hired engineering managers. We evaluate model performance and find that frontier models are still unable to solve the majority of tasks. To facilitate future research, we open-source a unified Docker image and a public evaluation split, SWE-Lancer Diamond (https://github.com/openai/SWELancer-Benchmark). By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development.

OpenAI is paying the digital media company Dotdash Meredith at least 16 million per year to license its content, according to public financial documents reviewed by Adweek. We already knew about this burgeoning partnership, but we didn't have a financial figure. The actual payout could rise above 16 million per year, as it only reflects the "fixed" component of the payment. The "variable" component will be calculated in the future, according to a recent earnings call led by the chief operating and financial officer of Dotdash Meredith's parent company IAC. "If you look at Q3 of 2024, licensing revenue was up about 4.1 million year over year. The lion's share of that would be driven by the OpenAI license," CFO Chris Halpin said.

Musicians have long criticized streaming services for their abysmal revenue sharing programs. In 2021, for example, as much as 97 percent of Spotify's over 6 million listed artists earned less than 1,000. Last year, the company announced a new system offering fractions of a cent per track, all of which is now based on even more stringent rules. But there was apparently a way to earn some real dividends from those songs--provided you have access to thousands of bots, hundreds of thousands of AI-generated songs, and are willing to risk receiving a federal grand jury indictment for wire fraud and money laundering. That's what a man named Michael Smith in North Carolina is currently facing, according to a DOJ announcement on September 4. Unsealed filings from US prosecutors accuse Smith of scamming digital streaming platforms including Spotify, Apple Music, Amazon Music, and YouTube Music of over 10 million in royalty payouts between 2017 and 2024.

Spotify is going through something right now. On Monday morning, the industry-defining audio streaming service announced that it would be hiking its Premium subscription prices for users in the United States, effective next month. The individual plan is rising by 1, the Duo plan by 2, and the family subscription by 3. These shifts arrive almost a year after Spotify raised U.S. subscription rates for the first time ever, upping the individual plan to 10.99 a month to match with competitors' price points. That increase was meant to mollify music-industry executives (who demanded better royalty payouts) and investors (who demanded that Spotify squeeze out regular profits).

Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.

The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine. One of the hallmarks of a bandit setting is the agent's capacity to explore its environment through active intervention, which contrasts with the ability to collect passive data by estimating associational relationships between actions and payouts. The existence of unobserved confounders, namely unmeasured variables affecting both the action and the outcome variables, implies that these two data-collection modes will in general not coincide. In this paper, we show that formalizing this distinction has conceptual and algorithmic implications to the bandit setting. The current generation of bandit algorithms implicitly try to maximize rewards based on estimation of the experimental distribution, which we show is not always the best strategy to pursue. Indeed, to achieve low regret in certain realistic classes of bandit problems (namely, in the face of unobserved confounders), both experimental and observational quantities are required by the rational agent. After this realization, we propose an optimization metric (employing both experimental and observational distributions) that bandit agents should pursue, and illustrate its benefits over traditional algorithms.

Vertical federated learning (VFL) is a promising approach for collaboratively training machine learning models using private data partitioned vertically across different parties. Ideally in a VFL setting, the active party (party possessing features of samples with labels) benefits by improving its machine learning model through collaboration with some passive parties (parties possessing additional features of the same samples without labels) in a privacy preserving manner. However, motivating passive parties to participate in VFL can be challenging. In this paper, we focus on the problem of allocating incentives to the passive parties by the active party based on their contributions to the VFL process. We formulate this problem as a variant of the Nucleolus game theory concept, known as the Bankruptcy Problem, and solve it using the Talmud's division rule. We evaluate our proposed method on synthetic and real-world datasets and show that it ensures fairness and stability in incentive allocation among passive parties who contribute their data to the federated model. Additionally, we compare our method to the existing solution of calculating Shapley values and show that our approach provides a more efficient solution with fewer computations.