Electronic shelf labels, returns machines, robot bag packers and yet more self-service tills – just some of the many technologies that UK retailers are embracing as they try to solve the problem of rising labour costs. Investment in automation was a constant drumbeat amid the flurry of festive trading updates from big retailers in the past few weeks, as they face higher staffing bills from April after the rise in the national minimum wage and employers' national insurance contributions (NICs). The investments could improve productivity – a key government aim – in an industry long reliant on cheap labour. However, they will also replace entry-level jobs and reduce the number of roles in a sector that is the UK's biggest employer. When the British Retail Consortium asked leading retailers' finance directors how they would be responding to the impending increase in employers' NICs, almost a third said they would be using more automation, although this sat behind raising prices, cutting head office jobs and reducing working hours.

The present work takes a significant step in addressing this. The primary contribution of the paper are variations of Online Newton Step that remove this drawback using a sketching approximation to the scaling matrix and a clever implementation of sparse updates. The primary theoretical contributions are the analysis of the RP and FD versions of the algorithm. For RP they show a regret bound which holds when the matrix G_T (the matrix of observed gradients) is actually low-rank. Given the structure of the loss functions assumed, f_t(w) \ell( w, x_t), gradients will always be in the direction of the examples x_t, and so I think this theorem only holds when the data is actually low-rank.

We introduce the problem of sleeping dueling bandits with stochastic preferences and adversarial availabilities (DB-SPAA). In almost all dueling bandit applications, the decision space often changes over time; eg, retail store management, online shopping, restaurant recommendation, search engine optimization, etc. Surprisingly, this sleeping aspect' of dueling bandits has never been studied in the literature. Like dueling bandits, the goal is to compete with the best arm by sequentially querying the preference feedback of item pairs. The non-triviality however results due to the non-stationary item spaces that allow any arbitrary subsets items to go unavailable every round. The goal is to find an optimal no-regret policy that can identify the best available item at each round, as opposed to the standard fixed best-arm regret objective' of dueling bandits.

Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively.

Time series forecasting refers to a class of techniques for the prediction of events through a sequence of time, typically to inform strategic or tactical decision making. Going beyond strategic forecasting problems (e.g., those commonly-used historically in statistics and econometrics [1]), operational forecasting problems are increasingly-important. For example, at large internet retail companies, this includes demand forecasting for products at an online retailer, work force cohorts of a company in its locations, compute capacity needs per region and server type, etc.; in scientific machine learning, this includes prediction of extreme events in, e.g., climate and weather models; and so on. In particular, MQCNN [2] and MQTransformer [3] are stateof-the-art (SOTA) neural network (NN) based multivariate time series forecasting models that are used to predict future demand at the product level for hundreds of millions of products.

Shop Amazon's winter sale and get serious discounts on all your electronics. Amazon is running its yearly winter sale, which runs now through January 17. If you missed any Black Friday sales, don't worry, you can get up to 40% off on everything from laptops to Amazon devices, headphones and more. So, spend your gift cards and get major discounts on all those electronics you didn't get during the holiday season. Make sure your items are delivered ASAP by signing up for a Prime membership. The benefits include fast, free delivery, access to invite-only deals and the option to Buy With Prime.

But Google Maps has been causing chaos for some shoppers after pranksters set up a'phantom' Aldi in the middle of the Welsh countryside. The small village of Cyffylliog has been inundated with an'endless stream' of confused shoppers looking for somewhere to buy their groceries. Following Google's directions actually brought them to an empty field on a remote farm tens of miles away from the nearest supermarket. While it might have been added as a joke, the fake Aldi has since led to chaos for this small community as deliveries have begun to arrive in search of the non-existent supermarket. The misguided prank has even led to real Aldi deliveries arriving on one farmer's doorstep and becoming stuck on the narrow lanes.

Moving your body is for everyone, regardless of experience level, skill, or location. This year's Best of What's New innovations make getting outside and active easier in many ways. A tightly woven shirt stops itchy mosquito bites sans chemicals. An electric fishing reel cuts the cord and ditches heavy batteries once and for all. An app combines avalanche education with hard-to-find reports for safer snowshoeing and skiing.

One of the promises of the next era of generative AI is that the technology will be agentic, or have the ability to perform tasks autonomously on behalf of us chaotic humans. That means AI agents will theoretically be able to "reason" about the next steps they should take, allowing them to execute multiple actions from a single query. The possibilities are endless, if you believe the hype--think maximum efficiency and productivity, plus a host of other buzz word-latent phrases that one might hear during a tech giant's quarterly earnings call. All I want AI to do for me, however, is to shop. I understand that some people find shopping to be a pleasurable act, but the options overwhelm me, whether I'm in an actual store or stuck in an endless scroll.

Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional hierarchical reconciliation methods ensure forecasting coherence, they often trade off accuracy for coherence especially at lower levels and fail to capture the seasonality unique to each time-series in the hierarchy. Thus, we propose a novel framework "Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment (Multi-Stage HiFoReAd)" to address the challenges of preserving seasonality, ensuring coherence, and improving accuracy. Our system first utilizes diverse models, ensembled through Bayesian Optimization (BO), achieving base forecasts. The generated base forecasts are then passed into the Multi-Stage HiFoReAd framework. The initial stage refines the hierarchy using Top-Down forecasts and "harmonic alignment." The second stage aligns the higher levels' forecasts using MinTrace algorithm, following which the last two levels undergo "harmonic alignment" and "stratified scaling", to eventually achieve accurate and coherent forecasts across the whole hierarchy. Our experiments on Walmart's internal Ads-demand dataset and 3 other public datasets, each with 4 hierarchical levels, demonstrate that the average Absolute Percentage Error from the cross-validation sets improve from 3% to 40% across levels against BO-ensemble of models (LGBM, MSTL+ETS, Prophet) as well as from 1.2% to 92.9% against State-Of-The-Art models. In addition, the forecasts at all hierarchical levels are proved to be coherent. The proposed framework has been deployed and leveraged by Walmart's ads, sales and operations teams to track future demands, make informed decisions and plan resources.