Singapore

The housing market is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model has been extensively studied in economics and computer science due to its broad applications, little attention has been paid to settings where preferences are unknown and must be learned through repeated interactions.
In this paper, we propose a statistical learning model within the multi-player multi-armed bandit framework, where players (agents) learn their preferences over arms (goods) from stochastic rewards. We introduce the notion of \emph{core regret} for each player as the market objective. We study both centralized and decentralized approaches, proving $\mathcal{O}(\log T / \Delta^2)$ upper bounds on regret, where $T$ is the time horizon and $\Delta$ is the minimum preference gap among players. For the decentralized setting, we also establish a matching lower bound, demonstrating that our algorithm is order-optimal.

AAAI 2026

Bandit Learning in Housing Markets

online learning & bandits

cooperative game theory

learning theory

game theory

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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 Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose ${\tt KTCF}$, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our ${\tt KTCF}$ method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help prevent large skill gaps. Our work demonstrates that counterfactuals have the potential to advance the responsible use of AI in education. Existing XAI works for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

The rapid advancement and integration of artificial
intelligence (AI) into our everyday lives, work, and
classrooms have added demands for K-12 education to ensure
that students are given opportunities to obtain AI
competencies essential for responsible participation in the
AI-driven future. AI literacy encompasses technical
knowledge, ethical awareness, and critical evaluation of AI
tools, as well as the ability to collaborate with AI
systems in creative and productive ways. Research
highlights the importance of age-appropriate approaches
that address foundational AI knowledge, data literacy,
ethics, problem-solving, and creativity, ensuring students
can both use, analyze, critically evaluate, and design AI
solutions for real-world challenges. This not only requires
schools to prepare learners with AI competencies, but also
colleges and universities to ensure that we prepare our
preservice teachers to be AI-ready, who can integrate
technological, pedagogical, and content knowledge in
classroom practice. This paper highlights the importance of
purposefully integrating AI competencies into teacher
education programs and offers practical examples of how
such integration can be achieved.

Integrating AI Competencies into Teacher Education Programs

Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLSP) in terms of the problem dimension, the advantage is bottlenecked by condition number of the coefficient matrix. In this work, we propose a new quantum algorithm for QLSP inspired by the classical proximal point algorithm (PPA). Our proposed method can be viewed as a meta-algorithm that allows inverting a modified matrix via an existing \texttt{QLSP\_solver}, thereby directly approximating the solution vector instead of approximating the inverse of the coefficient matrix. By carefully choosing the step size $\eta$, the proposed algorithm can effectively precondition the linear system to mitigate the dependence on condition numbers that hindered the applicability of previous approaches. Importantly, this is the first iterative framework for QLSP where a tunable parameter $\eta$ and initialization $x_0$ allows controlling the trade-off between the runtime and approximation error.

A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm

Domain generalization (DG) and domain adaptation (DA) for 3D semantic segmentation enable the model to maintain high performance while avoiding labor-intensive and time-consuming annotation of target-domain data.
However, under adverse weather conditions, the injection of spatial noise will affect the reflectivity of LiDAR point clouds, exacerbate domain distribution discrepancies, and degrade the generalization ability of the model.
Current methods mainly rely on sparse convolution-based architecture. Due to its limited receptive field, the model captures varying local geometric information when dealing with point clouds of different sparsities, thereby limiting its transferability.
To this end, we propose \textbf{BeyondSparse}, a novel cross-domain 3D semantic segmentation method under adverse weather that incorporates a state-space model into a 3D sparse convolution-based architecture, sequentially modeling all features to learn domain-invariant representations.
This method consists of two main components: domain feature decoupling and Mamba-based encoder. The former performs feature disentanglement before sequential modeling, while the latter performs global modeling on voxelized point cloud data. In addition, we introduce a token-style augmentation to capture the intrinsic properties of input data.
Extensive experimental results demonstrate that our method outperforms SOTA competitors in both DG and DA tasks, for instance, achieving +4.6\% and +0.8\% mIoU on SynLiDAR$\rightarrow$SemanticSTF.

BeyondSparse: Facilitating Mamba to Enhance Cross-Domain 3D Semantic Segmentation in Adverse Weather

Scene recognition (SR) is a fundamental task in computer vision (CV). In recent years, transformer-based methods have achieved remarkable success in scene recognition tasks. Most existing approaches primarily rely on visual features and fail to effectively model the structural relationships within scenes, which are crucial for accurate scene recognition. To this end, we propose TANSR, an innovative method that leverages topological relationships from graphs to guide scene recognition. Specifically, GAMGN generates topology-aware masks from graph representations constructed by GGM and integrates them with patch embeddings by TAG, enabling the transformer's attention mechanism to be aware of topological information. Furthermore, we introduce an innovative attention-driven multimodal fusion strategy that integrates graph-derived topological cues with visual patch embeddings, substantially enhancing the transformer’s capability to capture topological information and improving performance in complex scene recognition tasks. We evaluate the model on the benchmarks MIT-67, Scene-15 and SUN397, where it achieves consistent state-of-the-art (SOTA) performance, including 98.58% accuracy on MIT-67.

Topology-Aware Vision Transformers for Enhanced Scene Recognition

Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world graphs. Pre-computation-based HGNNs address this by performing message passing only once during preprocessing, collecting neighbor information into regular-shaped tensors, which enables efficient mini-batch training. Label-based pre-computation methods collect neighbors' label information but suffer from training label leakage, where a node's own label information propagates back to itself during multi-hop message passing---the echo effect. Existing mitigation strategies are memory-inefficient on large graphs or suffer from compatibility issues with advanced message passing methods. We propose Echoless Label-based Pre-computation (Echoless-LP), which eliminates training label leakage with Partition-Focused Echoless Propagation (PFEP). PFEP partitions target nodes and performs echoless propagation, where nodes in each partition collect label information only from neighbors in other partitions, avoiding echo while remaining memory-efficient and compatible with any message passing method. We also introduce an Asymmetric Partitioning Scheme (APS) and a PostAdjust mechanism to address information loss from partitioning and distributional shifts across partitions. Experiments on public datasets demonstrate that Echoless-LP achieves superior performance and maintains memory efficiency compared to baselines. Our code is provided.

Echoless Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning

Interactive segmentation aims to delineate a user-specified target in an image by leveraging positive and negative clicks. While effective on natural images, existing methods often fail in remote sensing scenarios, where satellite imagery is characterized by ultra-high resolution, sparse object distribution, and significant scale variation. These factors hinder accurate segmentation of fine-grained targets like roads, buildings, and aircraft. To overcome these problems, we propose CrossCut, a novel interactive segmentation framework tailored for remote sensing imagery. Unlike previous approaches that either process the entire image or treat each patch independently, CrossCut enables simultaneous segmentation across multiple patches by propagating user click information to all patches. This design allows the model to fully utilize click guidance regardless of object location, effectively resolving the challenge of inter-patch information isolation. Furthermore, CrossCut supports flexible inference by allowing segmentation results from different patch configurations to be fused, enhancing both accuracy and robustness. Extensive evaluations across multiple remote sensing datasets demonstrate that CrossCut achieves state-of-the-art performance. Quantitative results and visualizations show that CrossCut significantly advances the field of interactive segmentation for remote sensing imagery.

CrossCut: Cross-Patch Aware Interactive Segmentation for Remote Sensing Images

Molecular representation plays a central role in computational drug discovery. Pharmacophores, functional groups responsible for molecular bioactivity, have been widely studied in cheminformatics. However, their incorporation into molecular representation learning, particularly in a context reasoning or generalization, remains relatively limited. To address this gap, we propose PharmaQA, a pharmacophore oriented question answering framework that formulates tailored prompts to extract context-aware molecular semantics. Rather than encoding pharmacophore features, PharmaQA learns to answer pharmacophore related queries. This design enables flexible reasoning across diverse tasks, including molecular property prediction, compound-target interaction prediction, and binding affinity estimation. Experimental results on benchmark datasets demonstrate that PharmaQA achieves competitive performance. In a ligand discovery case study using FDA-approved compounds, the framework identified potential inhibitors for three therapeutic targets, with strong docking performance. As a generalizable and modular solution, PharmaQA incorporates pharmacophoric knowledge into molecular embeddings, enhancing both predictive accuracy and interpretability in drug discovery applications.

PharmaQA: Prompt-Based Molecular Representation Learning via Pharmacophore-Oriented Question Answering

With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge.
This problem is especially challenging when multiple, potentially conflicting human values must be considered and balanced.
Although several variants of existing alignment methods (such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO)) have been proposed to address multi-value alignment, they suffer from notable limitations: 1) they are often unstable and inefficient in multi-value optimization; and 2) they fail to effectively handle value conflicts. 
As a result, these approaches typically struggle to achieve optimal trade-offs when aligning multiple values.

To address this challenge, we propose a novel framework called Multi-Value Alignment (MVA).
It mitigates alignment degradation caused by parameter interference among diverse human values by minimizing their mutual information.
Furthermore, we propose a value extrapolation strategy to efficiently explore the Pareto frontier, thereby constructing a set of LLMs with diverse value preferences.
Extensive experiments demonstrate that MVA consistently outperforms existing baselines in aligning LLMs with multiple human values.

Multi-Value Alignment for LLMs via Value Decorrelation and Extrapolation

The performance of optimization-based robot motion planning
algorithms is highly dependent on the initial solutions,
commonly obtained by running a sampling-based planner to
obtain a collision-free path. However, these methods can be
slow in high-dimensional and complex scenes and produce
nonsmooth solutions. Given previously solved path-planning
problems, it is highly desirable to learn their
distribution and use it as a prior for new similar
problems. Several works propose utilizing this prior to
bootstrap the motion planning problem, either by sampling
initial solutions from it, or using its distribution in a
maximum-a-posterior formulation for trajectory
optimization. In this work, we introduce motion planning
diffusion (MPD), an algorithm that learns trajectory
distribution priors with diffusion models. These generative
models have shown increasing success in encoding multimodal
data and have desirable properties for gradient-based
motion planning, such as cost guidance. Given a motion
planning problem, we construct a cost function and sample
from the posterior distribution using the learned prior
combined with the cost function gradients during the
denoising process. Instead of learning the prior on all
trajectory waypoints, we propose learning a lower
dimensional representation of a trajectory using linear
motion primitives, particularly B-spline curves. This
parametrization guarantees that the generated trajectory is
smooth, can be interpolated at higher frequencies, and
needs fewer parameters than a dense waypoint
representation. We demonstrate the results of our method
ranging from simple 2-D to more complex tasks using a 7-DOF
robot arm manipulator. In addition to learning from
simulated data, we also use human demonstrations on a
real-world pick-and-place task. The experiment results show
that diffusion models are strong priors for encoding
multimodal trajectory distributions for optimization-based
motion planning.


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KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

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