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In the multi-objective discrete optimization problem (MODOP), the exact nadir objective vector is crucial for decision-making, yet it remains challenging to find. Existing methods for tackling this problem have limitations in theoretical guarantees or high computational costs. This paper first applies boundary decomposition to the MODOP and proposes an exact algorithm called BDNC. BDNC is designed to address a bilevel optimization problem for each objective with finite-time convergence guarantees. The lower-level optimization problem, termed the boundary subproblem, is a scalarization of the MODOP. It can be solved using any suitable single-objective exact solver. According to the theoretical foundations of boundary decomposition, some specific settings of the boundary subproblem can ensure alignment with the nadir objective vector under mild conditions. The upper-level optimization problem evaluates a potential setting using the optimal solution to the lower-level one. It employs our proposed novel pruning method to efficiently identify the specific settings. Moreover, BDNC can leverage a trade-off provided by the decision-makers, potentially facilitating the decision-making process. Experiments on various MODOPs demonstrate that BDNC exhibits superior and reliable performance in terms of runtime compared to existing exact methods.

AAAI 2025

Boundary Decomposition for Finding Nadir Objective Vector in Multi-Objective Discrete Optimization

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Search and recommendation ecosystems exhibit competition among content creators. This competition has been tackled in a variety of game-theoretic frameworks. Content creators generate documents with the aim of being recommended by a content ranker for various information needs. In order for the ecosystem, modeled as a content ranking game, to be effective and maximize user welfare, it should guarantee stability, where stability is associated with the existence of pure Nash equilibrium in the corresponding game. Moreover, if the contents' ranking algorithm possesses a game in which any best-response learning dynamics of the content creators converge to equilibrium of high welfare, the system is considered highly attractive. However, as classical content ranking algorithms, employed by search and recommendation systems, rank documents by their distance to information needs, it has been shown that they fail to provide such stability properties. As a result, novel content ranking algorithms have been devised. In this work, we offer an alternative approach: corpus enrichment with a small set of fixed dummy documents. It turns out that, with the right design, such enrichment can lead to pure Nash equilibrium and even to the convergence of any best-response dynamics to a high welfare result, where we still employ the classical/current content ranking approach. We show two such corpus enrichment techniques with tight bounds on the number of documents needed to obtain the desired results. Interestingly, our study is a novel extension of Borel's Colonel Blotto game.

On the Power of Strategic Corpus Enrichment in Content Creation Games

Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset.

Mediation Analysis for Probabilities of Causation

Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that were not present during training; the most performant models in the inductive setting have employed path encoding modules in addition to standard subgraph encoding modules. This work similarly focuses on KG completion in the inductive setting, without the explicit use of path encodings, which can be time-consuming and introduces several hyperparameters that require costly hyperparameter optimization. Our approach uses a Transformer-based subgraph encoding module only; we introduce connection-biased attention and entity role embeddings into the subgraph encoding module to eliminate the need for an expensive and time-consuming path encoding module. Evaluations on standard inductive KG completion benchmark datasets demonstrate that our \textbf{C}onnection-\textbf{B}iased \textbf{Li}nk \textbf{P}rediction (CBLiP) model has superior performance to models that do not use path information. Compared to models that utilize path information, CBLiP shows competitive or superior performance while being faster. Additionally, to show that the effectiveness of connection-biased attention and entity role embeddings also holds in the transductive setting, we compare CBLiP's performance on the relation prediction task in the transductive setting.

Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion

This paper addresses theory in evolutionary multiobjective optimisation (EMO) and focuses on the role of crossover operators in many-objective optimisation. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives. We present a many-objective problem class together with a theoretical runtime analysis of the widely used NSGA-III to demonstrate that crossover can yield an exponential speedup on the runtime. In particular, this algorithm can find the Pareto set in expected polynomial time when using crossover while without crossover it requires exponential time to even find a single Pareto-optimal point. To our knowledge, this is the first rigorous runtime analysis in many-objective optimisation demonstrating an exponential performance gap when using crossover for more than two objectives.

A Many-Objective Problem Where Crossover Is Provably Indispensable

Reentrancy vulnerabilities detection is a hotspot issue in smart contract security, as attackers have used them to steal enormous amounts of money from Ethereum platforms. 
A new attack pattern is emerging in which attackers continuously release new reentrancy patterns to exploit fresh vulnerabilities and obfuscate existing ones. However, existing detection methods neglect the time-series evolution of vulnerabilities across different smart contract versions, leading to a gradual decline in their effectiveness over time. We investigate the time-series correlations among vulnerabilities in various updated versions and refer to these as Evolutionary Reentrancy Vulnerabilities (ERVs). We summarize that ERVs detection faces two key challenges:  (i) capturing the evolving patterns of ERVs along a complete evolutionary chain and (ii) detecting fresh reentrancy vulnerabilities in new versions. To address these challenges,  we propose CLEP, a novel Contrastive Learning with Evolving Pairs detection method. It can effectively capture the  evolving patterns by discerning similarities and differences across versions.  Specifically, we first modified the sample distribution by incorporating version declarations as time-series evolution information. Then, leveraging the hierarchical similarity, we design an evolving pairs scheme to form negative and positive contract pairs across versions. Finally, we build a complete evolutionary chain by proposing a version-aware contrastive sampler.  Our experimental results show that CLEP not only outperforms state-of-the-art baselines in version-specific scenarios but also shows  promising performance in cross-version evolution scenarios.

CLEP: A Novel Contrastive Learning Method for Evolutionary Reentrancy Vulnerability Detection

We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models. 
OmniMark can encode user-specific fingerprints across multiple dimensions of the diffusion model's weights, including kernels, filters, channels, and spatial dimensions, which ends up with the presence of invisible fingerprints in any generated images that can be subsequently extracted by a decoder.
This approach thus achieves efficient and scalable ad-hoc generation (<100 ms) of numerous models with unique fingerprints that enable user responsibility tracking and attribution of the model.
Our experiments demonstrate that OmniMark applies to various image generation and editing tasks, and achieves Highly accurate fingerprint detection without compromising image quality. 
Furthermore, we show that our approach exhibits good robustness, against both white-box model attacks and image attacks, including fine-tuning and JPEG compression.

OmniMark: Efficient and Scalable Latent Diffusion Model Fingerprinting

The application of graph neural networks (GNNs) to learn heuristic functions in classical planning is gaining traction. Despite the variety of methods proposed in the literature to encode classical planning tasks for GNNs, a comparative study evaluating their relative performances has been lacking. Moreover, some encodings have been assessed solely for their expressiveness rather than practical effectiveness in planning. This paper provides an extensive comparative analysis of existing encodings. Our results indicate that the smallest encoding based on Gaifman graphs, not yet applied in planning, outperforms the rest due to its fast evaluation times and the informativeness of the resulting heuristic. The overall coverage measured on the IPC almost reaches that of the state-of-the-art planner LAMA, while exhibiting rather complementary strengths across different domains.

State Encodings for GNN-Based Lifted Planners

Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: 1) Existing models do not adequately capture the "face" information in molecular graphs for the reaction center identification. 2) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules' inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms contemporary state-of-the-art semi-template models across various evaluative metrics (for example, GDiffRetro achieves a performance improvement of up to 12.0% on top-1 accuracy).

GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation

Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency.

Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.

Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice

In a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pattern recognition and feature learning rather than symbol manipulation and feature engineering. In this paper, we provide a proof of concept of an AI course that follows that proposed approach. Students study how these systems become so incredibly powerful through machine learning of features and through pattern matching. Students learn how those systems represent knowledge and they study their currently limited reasoning abilities. Students spend time discussing the accomplishments of current systems, positive as well as negative and they study the projected impact of anticipated systems. In this paper, we give a brief argument of why one would want to offer such a course. We present a detailed outline of the contents of such a course, together with learning materials and their proposed use. We summarize relevant anonymous student feedback and offer a subjective evaluation of the pilot course.

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