Everlane, Revolve and Under Armour pilot Happy Returns' AI system to combat return fraud as retailers prepare for $850 billion in returned goods in 2025.

Save big on robot vacuums, air fryers, air purifiers, kitchen appliances, and tons of other devices to improve your home life. We may earn revenue from the products available on this page and participate in affiliate programs. You survived the holidays, and now you're holding the most powerful post-season artifact: an Amazon gift card. Instead of spending it on a random pile of impulse buys, put it toward upgrades that make your home cleaner, cozier, and easier to live in. If you didn't get what you wanted under the tree, now is the time to get it for yourself.

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. We first derive an instance-specific lower bound for DB-SPAA $\Omega( \sum_{i =1}^{K-1}\sum_{j=i+1}^K \frac{\log T}{\Delta(i,j)})$, where $K$ is the number of items and $\Delta(i,j)$ is the gap between items $i$ and $j$. This indicates that the sleeping problem with preference feedback is inherently more difficult than that for classical multi-armed bandits (MAB). We then propose two algorithms, with near optimal regret guarantees. Our results are corroborated empirically.

Human-designed visual manuals are crucial components in shape assembly activities. They provide step-by-step guidance on how we should move and connect different parts in a convenient and physically-realizable way. While there has been an ongoing effort in building agents that perform assembly tasks, the information in human-design manuals has been largely overlooked. We identify that this is due to 1) a lack of realistic 3D assembly objects that have paired manuals and 2) the difficulty of extracting structured information from purely image-based manuals. Motivated by this observation, we present IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly manuals. We provide fine-grained annotations on the IKEA objects and assembly manuals, including decomposed assembly parts, assembly plans, manual segmentation, and 2D-3D correspondence between 3D parts and visual manuals. We illustrate the broad application of our dataset on four tasks related to shape assembly: assembly plan generation, part segmentation, pose estimationand 3D part assembly.

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. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\tilde{O}(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

We study online convex optimization in the random order model, recently proposed by Garber et al. (2020), where the loss functions may be chosen by an adversary, but are then presented to the online algorithm in a uniformly random order. Focusing on the scenario where the cumulative loss function is (strongly) convex, yet individual loss functions are smooth but might be non-convex, we give algorithms that achieve the optimal bounds and significantly outperform the results of Garber et al. (2020), completely removing the dimension dependence and improve their scaling with respect to the strong convexity parameter. Our analysis relies on novel connections between algorithmic stability and generalization for sampling without-replacement analogous to those studied in the with-replacement i.i.d.

It's too late to get a gift shipped and shopping in-store is a nightmare. We may earn revenue from the products available on this page and participate in affiliate programs. OK, so you waited too long to order a present online . You don't want to brave the crowds. And you do't want to disappoint everyone during the holidays.

The Mac Mini is a fantastic little computer, but you'll need one of these great monitors to complete the setup. The Mac Mini is unbeatable in value . But unlike an iMac or MacBook, you'll need to pair it with a monitor. Apple has a couple of options: the Apple Studio Display and Pro Display XDR, but both are incredibly expensive (not to mention a few years old at this point). Fortunately, there are tons of great monitors out there that fit well into the Apple ecosystem. Be sure to check our Best Apple Desktops, Best MacBooks, Best Monitors, and Best Gaming Monitors guides for more.

From dead crabs to shredded bed sheets, fraudsters are using fake photos and videos to get their money back from ecommerce sites. I don't want to admit it, but I did spend a lot of money online this holiday shopping season. And unsurprisingly, some of those purchases didn't meet my expectations. A photobook I bought was damaged in transit, so I snapped a few pictures, emailed them to the merchant, and got a refund. Online shopping platforms have long depended on photos submitted by customers to confirm that refund requests are legitimate.

Can AI really help us discover new materials? Judging from headlines and social media posts in recent years, one might reasonably assume that AI is going to fix the power grid, cure the world's diseases, and finish my holiday shopping for me. This week, we published a new package called Hype Correction . The collection of stories takes a look at how the world is starting to reckon with the reality of what AI can do, and what's just fluff. One of my favorite stories in that package comes from my colleague David Rotman, who took a hard look at AI for materials research . AI could transform the process of discovering new materials--innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more.