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The advantages of accurate customer segmentation are clear for businesses of all shapes and sizes, but for budget-conscious startups and SMEs, the marketing ROI for technology-driven campaigns can't be overstated.  Statistically speaking, 77% of marketing ROI comes from segmented, targeted, and triggered campaigns, while  77% of marketing ROI comes from segmented, targeted, and triggered campaigns, while non-targeted campaigns show a 50% lower click-through rate (CTR) than segmented campaigns.  However, accurate segmentation can be a challenge in itself for small businesses. On the one hand, creating focused customer segments can help optimize spending on marketing campaigns for the ideal customer profile, but on…

In today's rapidly evolving technological landscape, the intersection of data and artificial intelligence (AI) has become a critical focus for organizations across industries. According to Foundry's recent CIO Tech Poll, IT leaders have overwhelmingly placed AI-enabled solutions at the top of their investment list for 2024, with only 8% expressing no interest in generative AI. This surge in AI prioritization underscores the need for a deeper understanding of how data and AI can work together to drive innovation and business value.To explore this crucial topic, we recently hosted a panel discussion featuring industry experts from Informatica, Immuta, and Dataiku. The…

arXiv:2408.04190v1 Announce Type: new Abstract: In Reinforcement Learning (RL), designing precise reward functions remains to be a challenge, particularly when aligning with human intent. Preference-based RL (PbRL) was introduced to address this problem by learning reward models from human feedback. However, existing PbRL methods have limitations as they often overlook the second-order preference that indicates the relative strength of preference. In this paper, we propose Listwise Reward Estimation (LiRE), a novel approach for offline PbRL that leverages second-order preference information by constructing a Ranked List of Trajectories (RLT), which can be efficiently built by using the same ternary feedback type…

arXiv:2408.04172v1 Announce Type: new Abstract: Deep networks have shown impressive performance in the image restoration tasks, such as image colorization. However, we find that previous approaches rely on the digital representation from single color model with a specific mapping function, a.k.a., color space, during the colorization pipeline. In this paper, we first investigate the modeling of different color spaces, and find each of them exhibiting distinctive characteristics with unique distribution of colors. The complementarity among multiple color spaces leads to benefits for the image colorization task. We present MultiColor, a new learning-based approach to automatically colorize grayscale images that combines…

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