We consider time to treatment initialization. This can commonly occur in preventive medicine, such as disease screening and vaccination; it can also occur with non-fatal health conditions such as HIV infection without the onset of AIDS. While traditional causal inference focused on ‘when to treat’ and its effects, including their possible dependence on subject characteristics, we consider the incremental causal effect when the intensity of time to treatment initialization is intervened upon. We provide identification of the incremental causal effect without the commonly required positivity assumption, as well as an estimation framework using inverse probability weighting. We illustrate our approach via simulation, and apply it to a rheumatoid arthritis study to evaluate the incremental effect of time to start methotrexate on joint pain.

Retrievers, which form one of the most important recommendation stages, are responsible for efficiently selecting possible positive samples to the later stages under strict latency limitations. Because of this, large-scale systems always rely on approximate calculations and indexes to roughly shrink candidate scale, with a simple ranking model. Most of the existing methods mainly focus on incorporating complicated ranking models. However, index structure is not improved, which also bottlenecks the whole effectiveness. In this paper, we propose a novel index structure: streaming Vector Quantization model, as a new generation of retrieval paradigm. Streaming VQ attaches items with indexes in real time, granting it immediacy. Moreover, through meticulous verification of possible variants, it achieves additional benefits like index balancing and reparability, enabling it to support complicated ranking models as existing approaches. Streaming VQ has been deployed and replaced all major retrievers in Douyin and Douyin Lite, resulting in remarkable user engagement gain.

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios.

Modern online auction-based advertising systems combine item and user features to promote ad creatives with the most revenue.However, new ad creatives have to display for certain initial users before enough click statistics could collected and utilized in later ads ranking and bidding processes. This leads to a well-known challenging cold start problem.In this paper, we argue that the content of the creatives intrinsically determines their performance (e.g. ctr, cvr), and we add a pre-ranking stage based on the content. The stage prunes inferior creatives and thus makes online impressions more effective. Since the pre-ranking stage can be executed offline, we can use deep features and take their well generalization to navigate the cold start problem.Specifically, we propose Pre Evaluation Ad Creation Model (PEAC), a novel method to evaluate creatives even before they were shown in the online ads system. Our proposed PEAC only utilizes ads information such as verbal and visual content, but requires no user data as features. During the online A/B testing, PEAC shows significant improvement in revenue. The method has been implemented and deployed in the large scale online advertising system at ByteDance. Furthermore, we provide detailed analysis on what the model learns, which also gives suggestions for ad creative design.

In this paper, we propose a novel single image action recognition algorithm based on the idea of semantic part actions. Unlike existing part-based methods, we argue that there exists a mid-level semantic, the semantic part action; and human action is a combination of semantic part actions and context cues. In detail, we divide human body into seven parts: head, torso, arms, hands and lower body. For each of them, we define a few semantic part actions (e.g. head: laughing). Finally, we exploit these part actions to infer the entire body action (e.g. applauding). To make the proposed idea practical, we propose a deep network-based framework which consists of two subnetworks, one for part localization and the other for action prediction. The action prediction network jointly learns part-level and body-level action semantics and combines them for the final decision. Extensive experiments demonstrate our proposal on semantic part actions as elements for entire body action. Our method reaches mAP of 93.9% and 91.2% on PASCAL VOC 2012 and Stanford-40, which outperforms the state-of-the-art by 2.3% and 8.6%.

In still images, multi-scale regions contain rich information of different granularity. However, only semantically meaningful regions provide auxiliary cues for action recognition. Moreover, regions at different scales contribute differently. Motivated by the two observations, we propose an approach that is composed of three components: 1) detecting semantic region candidates at multiple scales, 2) training networks at each scale, 3) extracting features and learning to fuse them. The proposed approach captures multi-scale cues and highlights the optimal scale for each action, Experimental results show that our approach reaches the state-of-the-art performance on two challenging benchmarks: 1) PASCAL VOC 2012 and 2) Stanford-40.

Recent advances in object detection have exploited object proposals to speed up object searching. However, many of existing object proposal generators have strong localization bias or require computationally expensive diversification strategies. In this paper, we present an effective approach to address these issues. We first propose a simple and useful localization bias measure, called superpixel tightness. Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification. Our method is essentially a box refinement process, which is intuitive and beneficial, but seldom exploited before. The greatest benefit of our method is that it can be integrated into any existing model to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC dataset demonstrates that our approach improves numerous existing models significantly with little computational overhead.