Autonomous Feature Engineering (AFE) is critical for improving predictive performance on tabular data by relieving humans from manual feature crafting. However, traditional AFE lacks the semantic guidance needed to fully exploit domain knowledge. Although large language models (LLMs) can, in principle, emulate experts, existing approaches typically operate in an open code space that directly generates and rewrites entire features; without a compositional structural representation and invariant constraints, edits are coarse and non-local, making it hard to distill interpretable features with high information content and rich hierarchical structure.

Spreadsheets are ubiquitous across the World Wide Web, playing a critical role in enhancing work efficiency across various domains. Large language model (LLM) has been recently attempted for automatic spreadsheet manipulation but has not yet been investigated in complicated and realistic tasks where reasoning challenges exist (e.g., long horizon manipulation with multi-step reasoning and ambiguous requirements). To bridge the gap with the real-world requirements, we introduce SheetRM, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges. To mitigate the above challenges, we further propose SheetAgent, a novel autonomous agent that utilizes the power of LLMs. SheetAgent consists of three collaborative modules: Planner, Informer, and Retriever, achieving both advanced reasoning and accurate manipulation over spreadsheets without human interaction through iterative task reasoning and reflection. Extensive experiments demonstrate that SheetAgent delivers 20--40% pass rate improvements on multiple benchmarks over baselines, achieving enhanced precision in spreadsheet manipulation and demonstrating superior table reasoning abilities. More details and visualizations are available at the https://sheetagent.github.io/. The datasets and source code are available at https://anonymous.4open.science/r/SheetAgent.

In large-scale metropolis, it is critical to efficiently allocate various resources such as electricity, medical care, and transportation to meet the living demands of citizens, according to the spatio-temporal distributions of resources and demands. Previous researchers have done plentiful work on such problems by leveraging Multi-Agent Reinforcement Learning (MARL) methods, where multiple agents cooperatively regulate and allocate the resources to meet the demands. However, facing the great number of agents in large cities, existing MARL methods lack efficient parameter sharing strategies among agents to reduce computational complexity. There remain two primary challenges in efficient parameter sharing: (1) during the RL training process, the behavior of agents changes significantly, limiting the performance of group parameter sharing based on fixed role division decided before training; (2) the behavior of agents forms complicated action trajectories, where their role characteristics are implicit, adding difficulty to dynamically adjusting agent role divisions during the training process. In this paper, we propose Dynamic Parameter Sharing (DyPS) to solve the above challenges. We design self-supervised learning tasks to extract the implicit behavioral characteristics from the action trajectories of agents. Based on the obtained behavioral characteristics, we propose a hierarchical MARL framework capable of dynamically revising the agent role divisions during the training process and thus shares parameters among agents with the same role, reducing computational complexity. In addition, our framework can be combined with various typical MARL algorithms, including IPPO, MAPPO, etc. We conduct 7 experiments in 4 representative resource allocation scenarios, where extensive results demonstrate our method's superior performance, outperforming the state-of-the-art baseline methods by up to 31%. Our source codes are available at https://github.com/tsinghua-fib-lab/DyPS.

Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance.This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options.Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs.In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model.While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders.We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function.Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-theart solvers on both an industrial circuit design benchmark, and

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based recommendation deploy GNN models to learn from both knowledge graph and user-item bipartite interaction graph. However, these works have not well considered the hierarchical structure of knowledge graph, leading to sub-optimal results. Despite the benefit of hierarchical structure, leveraging it is challenging since the structure is always partly-observed. In this work, we first propose to reveal unknown hierarchical structures with a supervised signal detection method and then exploit the hierarchical structure with disentangling representation learning. We conduct experiments on two large-scale datasets, of which the results well verify the superiority and rationality of the proposed method. Further experiments of ablation study with respect to key model designs have demonstrated the effectiveness and rationality of our proposed model. The code is available at https://github.com/tsinghua-fib-lab/HIKE .

In this paper, we focus on the decision-making challenge in autonomous driving, a central and intricate problem influencing the safety and practicality of autonomous vehicles. We propose an innovative hierarchical imitation learning framework that effectively alleviates the complexity of learning in autonomous driving decision-making problems by decoupling decision-making tasks into sub-problems. Specifically, the decision-making process is divided into two levels of sub-problems: the upper level directs the vehicle's lane selection and qualitative speed management, while the lower level implements precise control of the driving speed and direction. We harness Transformer-based models for solving each sub-problem, enabling overall hierarchical framework to comprehend and navigate diverse and various road conditions, ultimately resulting in improved decision-making. Through an evaluation in several typical driving scenarios within the SMARTS autonomous driving simulation environment, our proposed hierarchical decision-making framework significantly outperforms end-to-end reinforcement learning algorithms and behavior cloning algorithm, achieving an average pass rate of over 90%. Our framework's effectiveness is substantiated by its commendable achievements at the NeurIPS 2022 Driving SMARTS competition, where it secures dual track championships.

The recommender system is one of the most fundamental information services. A significant effort has been devoted to improving prediction accuracy, inevitably leading to the potential degradation of recommendation diversity. Moreover, individuals have different needs for diversity. To address these problems, diversity-enhanced approaches are proposed to modify the recommender models. However, these methods fail to break free from the relevance-oriented paradigm and are mostly haunted by sharply-declined accuracy and high computational costs. To tackle these challenges, we propose the Dual-Process Graph Neural Network (DPGNN), an efficient diversity-enhanced recommender system, resonating with the dual-process model of human cognition and the arousal theory of human interest. The first stage reduces the risk of suboptimal output during the training procedure, which helps to find a solution outside the relevance-oriented paradigm. Moreover, the second stage utilizes user-specific rating adjustments, boosting the recommendation diversity and accommodating users' distinctive needs with minimum computational costs. Extensive experiments on real-world datasets verify the effectiveness of our method in improving diversity, while maintaining accuracy with low computational costs.

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.

In the information-overloaded era of the Web, recommender systems that provide personalized content filtering are now the mainstream portal for users to access Web information. Recommender systems deploy machine learning models to learn users’ preferences from collected historical data, leading to more centralized recommendation results due to the feedback loop. As a result, it will harm the ranking of content outside the narrowed scope and limit the options seen by users. In this work, we first conduct data analysis from a graph view to observe that the users’ feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. To address the filter bubble issue under the feedback loop, we then propose a general and easy-to-use reinforcement learning-based method, which can adaptively select few but effective connections between nodes from different communities as the exposure list. We conduct extensive experiments in the simulation framework based on large-scale real-world datasets. The results demonstrate that our proposed reinforcement learning-based control method can serve as an effective solution to alleviate the filter bubble and the separated communities induced by it. We believe the proposed framework of controllable recommendation in this work can inspire not only the researchers of recommender systems, but also a broader community concerned with artificial intelligence algorithms’ impact on humanity, especially for those vulnerable populations on the Web.

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue. To alleviate these problems, in this article, we propose a novel debiased recommendation framework based on user feature balancing. The general idea is to introduce a projection function to adjust user feature distributions, such that the ideal unbiased learning objective can be upper bounded by a solvable objective purely based on the offline dataset. In the upper bound, the projected user distributions are expected to be equal given different items. From the causal inference perspective, this requirement aims to remove the causal relation from the user to the item, which enables us to achieve unbiased recommendation, bypassing the computation of IPS. To efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling, and adversarial learning to improve the training process. For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems. To the best of our knowledge, this article is the first work on debiased recommendation based on confounder balancing. In the experiments, we compare our framework with many state-of-the-art methods based on synthetic, semi-synthetic, and real-world datasets. Extensive experiments demonstrate that our model is effective in promoting the recommendation performance.

Graph neural networks (GNNs) have achieved significant success in numerous fields under settings where training and testing graphs are identically distributed. However, this setting is rarely satisfied in real life. Due to the lack of out-of-distribution (OOD) generalization abilities, existing GNNs methods perform disappointingly when there exist distribution shifts between testing and training graphs. Though several attempts have been made to deal with the issue, they mainly focus on structural properties while overlooking rich graph feature information. To this end, we propose an Invariant Factor GNN (IFGNN), which utilizes causal factor graphs to achieve invariant performances across different environments. Specifically, we dissect the graph generalization problem in a causal view, and argue that the key of graph generalization lies in discovering causal factors. Thus we extract the latent factors in the graph through disentanglement, and the causal ones are discovered with the invariant learning mechanism. We conduct extensive experiments on both synthetic and real-world datasets with distribution shifts to validate the OOD generalization abilities. The results demonstrate that our proposed IFGNN significantly outperforms the state-of-the-art baselines.

In the recent years, the deep reinforcement learning community has achieved impressive success to tackle real-world challenges. In this work, we propose a novel deep reinforcement learning agent to perform floorplanning, one of the early stages of VLSI physical design. Traditional methods to solve floorplanning problem are intractable for large circuit netlists and impossible to learn from past experience. We adopt the domain knowledge of floorplanning representation and propose a learning-based method that directly predicts block id and location through an RL framework. The resulting solutions are platform-independent and can be converted into layout within $O(n)$ time. We encode the hypernet information in the circuit netlist in a one-to-one mapping through hypergraph neural networks. Furthermore, We deploy transformer-like action selection to allow for transferability and generalization across netlist circuits with different sizes and handle the large discrete action space. This allows the parameter space of our model to remain the same regardless of the number of blocks. Our RL agent is able to transfer previously learnt knowledge to quickly optimize a new design with different size and purpose. To our knowledge, this is the first work to select both id and block position with an entirely end-to-end learning-based framework that can generalize. Results on publicly available benchmarks of GSRC and MCNC demonstrate that our method can outperform the baselines while being able to generalize.

Aiming to alleviate data sparsity and cold-start problems of tradi- tional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topologies to learn the low-dimensional representations of users and items with rich semantics. These real-world tripartite graphs are usually scale-free, however, the intrinsic hierarchical graph structures of which are underemphasized in existing works, consequently, leading to suboptimal recommendation performance. To address this issue and provide more accurate recommendation, we propose a knowledge-aware recommendation method with Lorentz model of the hyperbolic geometry, namely Lorentzian Knowledge-enhanced Graph convolutional networks for Recommendation (LKGR). LKGR facilitates better modeling of scale-free tripartite graphs after the data unification. Specifically, we employ different information propagation strategies in the hyperbolic space to explicitly encode heterogeneous information from historical interactions and KGs. Additionally, our proposed knowledge-aware attention mechanism enables the model to automatically measure the information contribution, producing the coherent information aggregation in the hyperbolic space. Extensive experiments on three real-world benchmarks demonstrate that LKGR outperforms state-of-the-art methods by 3.6-15.3% of [email protected] on Top-K recommendation.

Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples. Though with significant performance improvement, it commonly suffers from two critical issues: the non-compatibility with mainstream industrial deployment and the heavy computational burdens, both due to the inner-loop gradient operation. These two issues make them hard to be applied in practical recommender systems. To enjoy the benefits of meta learning framework and mitigate these problems, we propose a recommendation framework called Contextual Modulation Meta Learning (CMML). CMML is composed of fully feed-forward operations so it is computationally efficient and completely compatible with the mainstream industrial deployment. CMML consists of three components, including a context encoder that can generate context embedding to represent a specific task, a hybrid context generator that aggregates specific user-item features with task-level context, and a contextual modulation network, which can modulate the recommendation model to adapt effectively. We validate our approach on both scenario-specific and user-specific cold-start setting on various real-world datasets, showing CMML can achieve comparable or even better performance with gradient based methods yet with higher computational efficiency and better interpretability.

With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC), which refers to assigning location-based tasks to moving workers, has drawn increasing attention in recent years. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. In this paper, we propose a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., house removals, furniture installation) may require more than one workers (forming a coalition) to cooperate in order to maximize the overall rewards of workers. To tackle the CTA problem, we design both greedy method and equilibrium-based method. In particular, the greedy method aims to form a set of worker coalitions greedily to perform the tasks, in which we introduce an acceptance possibility to find the high-value task assignments. In the equilibrium-based algorithm, workers form coalitions in sequence and update their strategy (i.e., selecting a best-response task) at their turn, in order to maximize their own utility (i.e., reward of the coalition they stay in) until Nash equilibrium is reached. Since the equilibrium point obtained by the best-response approach is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. The extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on both real and synthetic datasets.

Hybrid Flow Shop Scheduling Problem (HFSP) is an essential problem in the automated warehouse scheduling, aiming at optimizing the sequence of jobs and the assignment of machines to utilize the makespan or other objectives. Existing algorithms adopt fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large scale of the practical HFSP, these methods fail to guarantee the quality of the obtained solution under the real-time requirement, especially facing extremely different data distribution. To address this challenge, we propose a novel Multi-Graph Attributed Reinforcement Learning based Optimization (MGRO) algorithm to better tackle the practical large-scale HFSP and improve the existing algorithm. Owing to incorporating the reinforcement learning-based policy search approach with classic search operators and the powerful multi-graph based representation, MGRO is capable of adjusting the search paradigm according to specific instances and enhancing the search efficiency. Specifically, we formulate the Gantt chart of the instance into the multi-graph-structured data. Then Graph Neural Network (GNN) and attention-based adaptive weighted pooling are employed to represent the state and make MGRO size-agnostic across arbitrary sizes of instances. In addition, a useful reward shaping approach is designed to facilitate model convergence. Extensive numerical experiments on both the publicly available dataset and real industrial dataset from Huawei Supply Chain Business Unit demonstrate the superiority of MGRO over existing baselines.

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models are based on the probabilistic graphical model (PGM) framework, which requires manually designed dependencies and may oversimplify user behaviors. Recently, methods based on neural networks are proposed to improve the prediction accuracy of user behaviors by enhancing the expressive ability and allowing flexible dependencies. However, they still suffer from the data sparsity and cold-start problems. In this paper, we propose a novel graph-enhanced click model (GraphCM) for web search. Firstly, we regard each query or document as a vertex, and propose novel homogeneous graph construction methods for queries and documents respectively, to fully exploit both intra-session and inter-session information for the sparsity and cold-start problems. Secondly, following the examination hypothesis, we separately model the attractiveness estimator and examination predictor to output the attractiveness scores and examination probabilities, where graph neural networks and neighbor interaction techniques are applied to extract the auxiliary information encoded in the pre-constructed homogeneous graphs. Finally, we apply combination functions to integrate examination probabilities and attractiveness scores into click predictions. Extensive experiments conducted on three real-world session datasets show that GraphCM not only outperforms the state-of-art models, but also achieves superior performance in addressing the data sparsity and cold-start problems.

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how users interact with a ranked list of items, provide a useful understanding of user feedback for learning ranking models. Constructing ”right” dependencies is the key of any successful click model. However, probabilistic graphical models (PGMs) have to rely on manually assigned dependencies, and oversimplify user behaviors. Existing neural network based methods promote PGMs by enhancing the expressive ability and allowing flexible dependencies, but still suffer from exposure bias and inferior estimation. In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. Firstly, we explicitly learn the reward function that recovers users’ intrinsic utility and underlying intentions. Secondly, we model user interactions with a ranked list as a dynamic system instead of one-step click prediction, alleviating the exposure bias problem. Finally, we minimize the JS divergence through adversarial training and learn a stable distribution of click sequences, which makes AICM generalize well across different distributions of ranked lists. A theoretical analysis has indicated that AICM reduces the exposure bias from O(T2) to O(T). Our studies on a public web search dataset show that AICM not only outperforms state-of-the-art models in traditional click metrics but also achieves superior performance in addressing the exposure bias and recovering the underlying patterns of click sequences.

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.