Title: Addressing Mark Imbalance in Integration-free Marked Temporal Point Processes
Venue: NeurIPS 2025
Accepted: yes
Keywords: Marked Temporal Point Process

Data Imbalance
Abstract: Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with some marks being frequent but others rare. The imbalance poses a significant challenge to the performance of the next event prediction, especially for events of rare marks. To address this issue, we propose a thresholding method, which learns thresholds to tune the mark probability normalized by the mark's prior probability to optimize mark prediction, rather than predicting the mark directly based on the mark probability as in existing studies. In conjunction with this method, we predict the mark first and then the time.  In particular, we develop a novel neural Marked Temporal Point Process (MTPP) model to support effective time sampling and estimation of mark probability without computationally expensive numerical improper integration. Extensive experiments on real-world datasets demonstrate the superior performance of our solution against various baselines for the next event mark and time prediction. The code is available at https://github.com/undes1red/IFNMTPP.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe work tackles a practical but under-explored problem—class-imbalance in Marked Temporal Point Processes (MTPPs)—and proposes a neat pipeline that (i) predicts the mark first with per-class F1-optimal thresholds, then (ii) predicts the time using an integration-free neural model that directly approximates a unified tail integral.  Empirical gains on four datasets are convincing, but several methodological assumptions and scalability questions remain unresolved.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nPractical relevance: Rare marks often correspond to critical events (e.g., large quake), yet are poorly handled in existing MTPPs; the paper addresses this gap head-on.\nConceptual simplicity: Thresholding at the probability level leaves the generative time model untouched, avoiding the negative side-effects of resampling or cost-sensitive losses on continuous-time likelihood.\nIntegration-free inference: The unified-integral trick yields a truly quadrature-free model, leading to faster inference and simpler implementation.\nComprehensive evaluation: Four real-world datasets, ablations on thresholding, mark-first vs. joint prediction, and synthetic studies on integral approximation.\n\nWeaknesses \n1.Independence assumption may be too strong. The key factor enabling thresholding is assuming p(m | H_t) is independent of Δt.  Yet many domains exhibit mark–time dependence (e.g., “long silence → rare fault”).  The paper does not measure this dependence nor analyze its impact when violated.\n\n2.Static thresholds under distribution drift. F1-optimal thresholds are learned offline once. In streaming settings where class priors evolve (common in social and medical data), static thresholds can quickly become sub-optimal. No adaptive mechanism is discussed.\n\n3.Scalability to large mark vocabularies. All datasets have ≤ 17 marks.  Threshold search cost is O(|M|·S) and the integral network stores per-class parameters. Feasibility for hundreds or thousands of marks is unclear.\n\n4.Lack of theoretical error bounds. IFNMTPP approximates \\Gamma(m,t) with a monotone network, but no approximation error or convergence analysis is provided. It is therefore hard to judge robustness on long horizons or extreme marks.\n\n5.Baseline fairness. The study compares against resampling but omits cost-sensitive loss baselines that are strong for imbalance problems. Without them, the advantage of thresholding may be overstated.\n\n6.End-to-end performance not fully reported. The paper reports MAE conditioned on the true mark and separately macro-F1 for marks. It would be more informative to provide joint metrics (e.g., time MAE using the predicted mark) to quantify error compounding.\n\nQUESTIONS:\nPlease refer to the \"weaknesses\" section above.\n\nLIMITATIONS:\nPlease refer to the \"weaknesses\" section above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new modeling approach for marked temporal point processes with the intentions of mitigating the impact that imbalance in mark frequencies has on downstream mark prediction. This is done through a combination of two techniques: (i) a proposed MTPP architecture that is integration free through directly parameterizing mark-specific survival functions with monotonic networks, and (ii) by applying a thresholding method when predicting next marks in a sequence to help account for frequency imbalance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. Mark imbalance is a real problem that is common in many datasets, so explicitly targeting this as a problem is well motivated. I also do appreciate that that is the primary focus of the paper as well, compared to say presenting the work as a generic MTPP model (which it admittedly is) and then over index on the next event prediction metrics after the fact.\n2. The model presented takes advantage of a clever reformulation of the typical joint distributions that define MTPPs, allowing for direct targets to parameterize to avoid approximating integrals during training. \n3. Empirically the method does appear, on average, to perform well and achieves the improvement in next mark prediction that it aimed for.\n\n**Weaknesses:**\n1. I felt that the technical details surrounding the proposed model (sections 3.3, 3.4) were a bit lacking in detail and explanation. These sections could have benefited a lot from more text concerning what equations represented and how they pieced together. \n2. The empirical results shown established that within the proposed model, thresholding was superior for next mark prediction compared to no thresholding, time then mark prediction (w/ and w/o thresholding), over-sampling, and under-sampling. It also shows that the proposed model with thresholding is in general superior in performance compared to other modeling baselines. Importantly, what it does not show is whether the improvement over baselines is primarily due to the proposed model or thresholding. To have a proper comparison, an additional ablation should be done to apply thresholding (when applicable) to the baseline methods -- at least for some of the experiments as this can understandably be a large undertaking.\n3. A minor note, but the empirical results presented in the paper largely consisted of tables of numbers which can be hard to parse at times. Some visualizations of them could go a long way towards improving the readability of the experiments section.\n\nQUESTIONS:\n1. The model proposed probably most resembles the Fully-NN model, which the paper does compare to. I do think the paper does warrant a more in depth comparison in the methodology between these two given their similarities. It is worth noting though that the Fully-NN model was not designed to model marks as it was just a pure TPP model. How then was this model used to compare with in the experiments?\n2. If my understanding is correct, the thresholding is applied after the model is initially trained using the log-likelihood objective. From the experiments, it is clear that the thresholding seems to have some positive impact in correcting for mark imbalance. Do you have any potential thoughts on how these lessons could be incorporated into the training of the model directly, to potentially allow for better predictions out of the box without having the transform the distribution post-hoc?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work studies the class imbalance problem in MTPP.\nThe authors propose a novel two-part solution: (1) a thresholding method that adjusts predicted mark probabilities by each mark’s prior frequency to better handle class imbalance, and (2) an Integration-Free Neural MTPP (IFNMTPP) model that predicts the next event’s mark first and then its time without requiring expensive numeric integration.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- It is the first work on class imbalance in MTPPs.\n- The proposed thresholding method is an original adaptation of imbalance handling to sequential event prediction.\n- the IFNMTPP model introduces a new “integration-free” approach to neural point process modeling.\n- The method yields large improvements in rare event prediction (several-fold increases in rare-mark F1 over baselines) while maintaining or improving overall performance.\n\nWeakness\n- The IFNMTPP architecture introduces additional complexity (an LSTM history encoder plus a specialized IEM network for each mark) compared to simpler models. Training this model with the monotonic constraints could be challenging too.\n- The thresholds for each mark are learned to maximize F1 on the training set. This could potentially overfit those thresholds to training characteristics.\n- The study focuses on thresholding vs basic resampling. Other imbalance-handling strategies (e.g., cost-sensitive learning where rare events incur higher loss, or more advanced oversampling like SMOTE variants) are not explored or compared with.\n\nQUESTIONS:\n- Why not learn thresholds on a validation set instead of the training set to prevent overfitting?\n- The Integral Estimation Module (IEM) enforces monotonicity via non-negative weights and activation functions. Does this constraint reduce the flexibility of IFNMTPP in modeling highly non-linear or non-monotonic dynamics?\n- The loss includes a log survival term. How sensitive is model performance to this term?\n\nLIMITATIONS:\n1. Thresholding is Post-hoc and Static\n2. A mark-specific monotonic neural network (Integral Estimation Module) must be trained for each mark.\n3. Applicability Limited to Discrete Marks", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper claims to achieve high performance in discrete imbalanced temporal processes.

STRENGTHS AND WEAKNESSES:
First, my understanding is that  the key idea of the paper is to predict separately for the different marks. If I understand right, this by itself does not help much, but makes it possible to generate samples for the rare marks, and then do thresholding.?

The way the authors approach the thresholding is to spend 3.3 to motivate a  complex NN  that includes  a History Encoder, plus a linear stages.  I didn´t quite get it  how you use the LSTM. Would it make sense to  try a regression based approach?

Results:
There are maybe too many results. Namely, always presenting the three configurations makes it difficult to focus on the rare marks, Also, you have different notions of "rare" comparing USearthquake  an say TaoBao. It would be helpful to know basic dataset info such as size and marks frequency.

Macro F1-Micro F1: why these two.  Why not Precision and REcall?

QUESTIONS:
..

LIMITATIONS:
How do these algorithms scale?
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-07