Title: Scalable Permutation-Aware Modeling for Temporal Set Prediction
Venue: NeurIPS 2025
Accepted: no
Keywords: Temporal Sets

Temporal Data

Sequence Learning

Sequential Sets

Deep Learning

Temporal Data Forecasting
Abstract: Temporal set prediction involves forecasting the elements that will appear in the next set, given a sequence of prior sets, each containing a variable number of elements. Existing methods often rely on complex architectures with substantial computational overhead, limiting their scalability. In this work, we introduce a novel and scalable framework that leverages permutation-equivariant and permutation-invariant transformations to efficiently model set dynamics. Our approach significantly reduces training and inference time while maintaining competitive performance. Extensive experiments on multiple public datasets demonstrate that our method matches or surpasses state-of-the-art models across several evaluation metrics. These results highlight the effectiveness of our model in enabling efficient and scalable temporal set prediction.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes PIETSP, a permutation-aware model for temporal set prediction. PIETSP uses a simple but effective architecture design that allows it to capture element- and global-level features (using permutation-equivariant and -invariant transformations and the subsequent fusion of extracted features) for improved prediction accuracy. The paper claims state-of-the-art performance and improved computational efficiency (eliminating quadratic costs) compared to prior methods.\n\nSTRENGTHS AND WEAKNESSES:\n1. Quality: \n- (+) Technically sound. \n- (+/-) The claims (improved accuracy and computational efficiency) are mostly supported. (State-of-the-art performance claim must be further verified, see questions).\n- (-) It would be very important to include error values for model performance across multiple seeds (Table 2).\n\t\n2. Clarity: \n- (+) Extremely well-written and easy to follow.\n- (-) Notation can get confusing at times, but the issue is rather minor. Any improvements here are welcome. \n\t    \n3. Significance:\n- (+) The paper targets an important bottleneck (quadratic attention in temporal-set size) and shows significant speed-ups without accuracy loss.\n- (-) More performance analysis (understanding why the method seems to perform better) would increase the significance of this paper (see questions).\n\t    \n4. Originality:\n- The building blocks themselves are well-known; the novelty is largely in the combination of these blocks and the empirical proof that this arrangement suffices.\n\nQUESTIONS:\n1. It would be interesting to see some analysis of how $\\alpha$ and $\\beta$ change over the course of training. Would the authors be able to analyse the contribution of each score (element-level and global-level)? I think this would be an important (and frankly interesting) analysis to convince the reviewers that the fusion works as hypothesised. This would strengthen my confidence and originality score. \n2. While the efficiency gains are nicely explained, it would be good if the authors could explain why they think the accuracy gains are substantial and consistent? A short discussion on this would benefit my understanding of the paper's contribution. \n3. Could the authors please report errors in Table 2? This is an important part of substantiating the claim that the method achieves state-of-the-art performance.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles temporal set prediction, which predicts the next set of elements from a sequence of previous sets. The proposed PIETSP model efficiently captures set dynamics using permutation-aware layers and combines both element-wise and global information. With linear complexity in sequence length and set size, PIETSP shows strong accuracy and speed on four public benchmarks, outperforming or matching prior state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The proposed PIETSP achieves linear time complexity with respect to sequence length and the number of elements, making it suitable for large-scale, real-time applications.\n2. The paper provides clear ablations to assess the contribution of each module (EE, GE), and detailed measurements of latency, throughput, and resource usage.\n\nWeaknesses: \n\n1. Limited novelty. The core of PIETSP consists of permutation-equivariant (PE) and permutation-invariant (PI) blocks. This is fundamentally aligned with the core idea of Deep Sets [1]: both employ a framework that combines independent feature mappings (equivariant layers) and global aggregation (invariant layers), achieving permutation invariance in modeling set inputs and ensuring that the model output depends only on the set of elements, not their order.\n2. The specific form of the Global Evaluator (GE) needs further clarification.\n3. The use of a fixed temporal window (K=19) may truncate long-range dependencies. The sensitivity of the method to the choice of K is not reported.\n4. Many recent sub-quadratic attention mechanisms (such as Performer, FlashAttention-2, Reformer, etc.) can already capture inter-element interactions in O(ND) time. The authors do not discuss or compare their method against these directly competing approaches.\n5. There is a lack of comparison with the latest TSP methods, such as [2].\n\n[1] Deep Sets. NeurIPS 2017.\n[2] Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention. ECAI 2024.\n\nQUESTIONS:\n1. Please articulate precisely which theoretical or architectural elements of PIETSP go beyond Deep Sets and why they are essential for TSP.\n2. Could you provide more details on the specific implementation of GE?\n3. How sensitive is your model to the choice of the temporal window K?\n4. Comparison with recent sub-quadratic attention models.\n4. Include a quantitative comparison with Label Attention Network (ECAI 2024).\n\nLIMITATIONS:\nYes, the authors have adequately discussed the limitations of proposed method in the supplementary material.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the task of *Temporal Set Prediction* (TSP), where the goal is to predict the next set of elements given a sequence of previous sets. The authors propose a model named **PIETSP**, which integrates permutation-equivariant (PE) and permutation-invariant (PI) layers to model set dynamics efficiently. The model processes input sets using a multi-hot sequence feature encoding and combines it with learned element embeddings. Two separate scoring branches—Element Evaluator (EE) and Global Evaluator (GE)—are used to assess element relevance, and the final prediction is obtained via a weighted score fusion. The authors claim linear time complexity with respect to both sequence length and set size, and demonstrate improved performance and inference efficiency over prior methods across four public benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n#### **Strengths:**\n\n- The paper targets a meaningful and general prediction problem in dynamic set modeling, relevant to applications such as next-basket recommendation and temporal event prediction.\n- The architecture is computationally efficient, with theoretical complexity carefully analyzed and validated by empirical speed comparisons.\n- Experiments cover four diverse real-world datasets and show competitive or superior performance across several metrics.\n- The paper provides clear illustrations and implementation details, contributing to clarity and potential reproducibility.\n\n#### **Weaknesses:**\n\n- The core modeling components—particularly the PE layer and the EE/GE scoring mechanisms—are relatively simple or borrowed from prior work (e.g., SFCNTSP), limiting the novelty of the architectural contribution.\n- The **Sequence Feature Integration (SFI)** module uses a multi-hot encoding concatenated with element embeddings, which is a straightforward yet potentially underpowered approach to model temporal dynamics.\n- Both the **Element Evaluator** (MLP) and the **Global Evaluator** (dot-product similarity) lack expressiveness, raising concerns about the model's ability to capture complex dependencies among elements.\n- While the model demonstrates strong computational efficiency, the practical implications for real-world deployment (e.g., generalization, interpretability, cold-start robustness) are not explored.\n\nQUESTIONS:\n- **SFI Representation:** Given the limitations of multi-hot encoding, why was this simple formulation chosen over more expressive temporal representations (e.g., frequency-weighted features, positional encodings, or decay-based embeddings)?\n- **Scoring Module Simplicity:** Have the authors explored more expressive mechanisms for the Element and Global Evaluators? For instance, attention-based fusion or pairwise scoring functions could improve interaction modeling.\n- **Score Fusion Generalization:** The score fusion module relies on element-specific learnable weights. Is there any analysis on how these weights behave during training, and whether they introduce overfitting or instability?\n- **Missing Baseline Comparisons:** Can the authors clarify why more powerful set-based architectures such as Set Transformers were omitted from the baselines? Their inclusion could contextualize the performance gains.\n- **Deployment Evaluation:** Can the authors provide a use case or case study demonstrating the model's value in real-world recommendation or monitoring systems?\n\nLIMITATIONS:\nThe authors have addressed several limitations, including the expressiveness of the architecture and the fixed-length input window. However, the paper could benefit from a more critical discussion on the expressiveness of the scoring modules and the limitations introduced by the use of multi-hot encoding as a representation of temporal structure. Additional experiments or ablations exploring these choices would strengthen the paper.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles temporal set prediction (TSP), which is a task of forecasting which elements will appear in the next set of a sequence. To overcome the quadratic time and memory costs that plague attention- and graph-based baselines, the authors propose PIETSP, a permutation-aware architecture that fuses element embeddings with sequence features and passes them through a permutation-equivariant layer, a permutation-invariant aggregator, and dual evaluators (element-level EE and global GE) whose scores are adaptively fused. The time complexity analysis and empirical experiments are also provided to assess the proposed PIETSP framework.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Clear, mathematically grounded problem formulation. The paper recasts temporal-set prediction as a joint representation task that unifies element features and their time-varying membership.\n- A time complexity analysis is provided to theoretically provide the backup for the efficiency claims, though the time complexity of baselines is not provided for reference or comparison.\n\nWeaknesses:\n- Figure 2 is difficult to interpret. For example, the “X” inside a circle is never defined. Important details are missing, so the figure is not self-contained and does not convey the workflow unambiguously.\n- Lack of comparative complexity analysis. The paper presents only the time-complexity derivation for the proposed method and immediately claims efficiency. Without contrasting these results with state-of-the-art baselines, readers cannot assess how large—if at all—the computational advantage really is.\n- Unclear source of the efficiency gains in Table 3. The baselines appear to be fully converged, yet the authors give no evidence that their own model was trained to convergence. Consequently, it is impossible to know whether the reported speed-ups come from fewer training epochs or from an intrinsically faster algorithm/architecture.\n- Missing inference-time evaluation. A thorough comparison of inference latency and throughput against competing methods is strongly recommended to substantiate the paper’s claims of practical scalability.\n- Clarity and organization need considerable refinement. The expression in the paper is often dense and poorly sign-posted, so grasping the main idea requires several close readings. Reordering sections for a more logical flow, defining symbols immediately where they appear, and adding concise transition sentences would greatly improve readability.\n\nQUESTIONS:\nPlease refer to the weakness.\n\nLIMITATIONS:\nPlease refer to the weakness.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-04"}