Learning to Rank

Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content across regions. While recent approaches adopt a two-stage pipeline of retrieving candidates and selecting the best match, they typically rely on simplistic similarity heuristics and point-wise supervision, failing to model spatial relationships among candidates. In this paper, we propose GeoRanker, a distance-aware ranking framework that leverages large vision-language models to jointly encode query-candidate interactions and predict geographic proximity. In addition, we introduce a multi-order distance loss that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships. To support this, we curate GeoRanking, the first dataset explicitly designed for geographic ranking tasks with multimodal candidate information. GeoRanker achieves state-of-the-art results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly outperforming current best methods.

Most statistical models for pairwise comparisons, including the Bradley-Terry (BT) and Thurstone models and many extensions, make a relatively strong assumption of stochastic transitivity. This assumption imposes the existence of an unobserved global ranking among all the players/teams/items and monotone constraints on the comparison probabilities implied by the global ranking. However, the stochastic transitivity assumption does not hold in many real-world scenarios of pairwise comparisons, especially games involving multiple skills or strategies. As a result, models relying on this assumption can have suboptimal predictive performance. In this paper, we propose a general family of statistical models for pairwise comparison data without a stochastic transitivity assumption, substantially extending the BT and Thurstone models. In this model, the pairwise probabilities are determined by a (approximately) low-dimensional skew-symmetric matrix. Likelihood-based estimation methods and computational algorithms are developed, which allow for sparse data with only a small proportion of observed pairs. Theoretical analysis shows that the proposed estimator achieves minimax-rate optimality, which adapts effectively to the sparsity level of the data. The spectral theory for skew-symmetric matrices plays a crucial role in the implementation and theoretical analysis. The proposed method's superiority against the BT model, along with its broad applicability across diverse scenarios, is further supported by simulations and real data analysis.

Scheduled batch jobs are widely used on asynchronous computing platforms to execute enterprise applications such as promotional notifications and candidate pre-computation for recommender systems. Delivering or updating information at the right time is important for user experience and execution impact, yet providing a versatile, personalized execution time optimization solution across diverse product scenarios while maintaining reasonable infrastructure costs remains challenging. In this paper, we present a deployed system that serves billions of users daily, combining learning-to-rank with a "best time policy" for execution time selection. We describe the four-stage evolution of our approach: from heuristic peak-hour rules, to pointwise ML-based activity pattern predictions, to a linear signal assembler with globally fixed weights, and finally to a contextual ensemble learner that produces per-user adaptive fusion weights via a neural policy network trained with listwise learning-to-rank objectives. We further report the discovery of cross-use-case cannibalization effects and introduce a coordination system to mitigate the problem. Our production experiments demonstrate measurable improvements in both execution efficiency and downstream product impact. We share deployment lessons including failure analyses and design decisions accumulated over four years of operating this system at scale. To our knowledge, this represents the first ML-based multi-tenant execution time optimization system deployed across different product domains at industrial scale.