Neural Information Processing Systems http://nips.cc/

User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User events can be broadly categorized into personal events, which involve individual actions, and relational events, which involve interactions between two users. These two types of events are typically modeled separately, using sequence-based methods for personal events and graph-based methods for relational events. Despite the need to capture both event types in real-world systems, prior work has rarely considered them together. This is often due to the convenient simplification that user behavior can be adequately represented by a single formalization, either as a sequence or a graph. To address this gap, there is a need for public datasets and prediction tasks that explicitly incorporate both personal and relational events. In this work, we introduce a collection of such datasets, propose a unified formalization, and empirically show that models benefit from incorporating both event types. Our results also indicate that current methods leave a notable room for improvements. We release these resources to support further research in unified user event modeling and encourage progress in this direction.

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function.

Neural Information Processing Systems http://nips.cc/

A \emph{bidding} game is played on a graph as follows. A token is placed on an initial vertex and both players are allocated budgets. In each turn, the players simultaneously submit bids that do not exceed their available budgets, the higher bidder moves the token, and pays the bid to the lower bidder. We focus on \emph{discrete}-bidding, which are motivated by practical applications and restrict the granularity of the players' bids, e.g, bids must be given in cents. We study, for the first time, discrete-bidding games with {\em mean-payoff} and {\em energy} objectives. In contrast, mean-payoff {\em continuous}-bidding games (i.e., no granularity restrictions) are understood and exhibit a rich mathematical structure. The {\em threshold} budget is a necessary and sufficient initial budget for winning an energy game or guaranteeing a target payoff in a mean-payoff game. We first establish existence of threshold budgets; a non-trivial property due to the concurrent moves of the players. Moreover, we identify the structure of the thresholds, which is key in obtaining compact strategies, and in turn, showing that finding threshold is in \NP~and \coNP even in succinctly-represented games.

Unlike English, which uses distinct forms (e.g., had, has, will have) to mark the perfect aspect across tenses, Chinese and Japanese lack separate grammatical forms for tense within the perfect aspect, which complicates Natural Language Inference (NLI). Focusing on the perfect aspect in these languages, we construct a linguistically motivated, template-based NLI dataset (1,350 pairs per language). Experiments reveal that even advanced LLMs struggle with temporal inference, particularly in detecting subtle tense and reference-time shifts. These findings highlight model limitations and underscore the need for cross-linguistic evaluation in temporal semantics. Our dataset is available at https://github.com/Lujie2001/CrossNLI.

This paper tackles the problem of minimizing the prediction loss of the optimal solution (PLS) of the MILP with given data, which is one of the inverse optimization problems. While existing methods can approximately solve this problem, their implementation in the high-dimensional case to minimize the PLS is computationally expensive because they are inefficient in reducing the prediction loss of weights (PLW). We propose a fast algorithm for minimizing the PLS of MILP. To demonstrate this property, we attribute the problem of minimizing the PLS to that of minimizing the suboptimality loss (SL), which is convex. If the PLS does not vanish, we can adapt the SL to have the estimated loss (SPO loss) with a positive lower bound, which enables us to evaluate the PLW. Consequently, we prove that the proposed algorithm can effectively reduce the PLW and achieve the minimum value of PLS. Our numerical experiments demonstrated that our algorithm successfully achieved the minimum PLS. Compared to existing methods, our algorithm exhibited a smaller dimensionality effect and minimized the PLS in less than 1/7 the number of iterations. Especially in high dimensions, our algorithm significantly improved the PLS by more than two orders of magnitude compared to existing algorithms.

Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, training MDGNNs faces the challenge of handling entangled temporal and structural dependencies, requiring sequential and chronological processing of data sequences to capture accurate temporal patterns. During the batch training, the temporal data points within the same batch will be processed in parallel, while their temporal dependencies are neglected. This issue is referred to as temporal discontinuity and restricts the effective temporal batch size, limiting data parallelism and reducing MDGNNs' flexibility in industrial applications. This paper studies the efficient training of MDGNNs at scale, focusing on the temporal discontinuity in training MDGNNs with large temporal batch sizes. We first conduct a theoretical study on the impact of temporal batch size on the convergence of MDGNN training. Based on the analysis, we propose PRES, an iterative prediction-correction scheme combined with a memory coherence learning objective to mitigate the effect of temporal discontinuity, enabling MDGNNs to be trained with significantly larger temporal batches without sacrificing generalization performance. Experimental results demonstrate that our approach enables up to a 4x larger temporal batch (3.4x speed-up) during MDGNN training.

According to a Forbes Survey from last year, 70 percent of buying decisions are influenced by how clients feel they are being treated; therefore, a superior personalized customer experience can go a long way towards increasing sales. Personalization requires the right product or service to be offered to the customer at the right moment and at the right price. With a plethora of product and service suites available, customers often defer to the expertise of the business to recommend the right solution for them. Businesses that can predict customer needs and supply effective solutions can ensure that their client base is always satisfied. The adage "the early bird gets the worm" has never been so true.