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In this study, we consider an optimization problem with uncertainty dependent on decision variables, which has recently attracted attention due to its importance in applications. In this problem, the gradient of the objective function cannot be obtained explicitly because the decision-dependent distribution is unknown. Therefore, several zeroth-order methods have been proposed, which obtain noisy objective values by sampling and update the iterates.  Although these existing methods have theoretical convergence for optimization problems with decision-dependent uncertainty, they require strong assumptions about the function and distribution or exhibit large variances in their gradient estimators. To overcome these issues, we propose two zeroth-order methods under mild assumptions. First, we develop a zeroth-order method with a new one-point gradient estimator including a variance reduction parameter. The proposed method updates the decision variables while adjusting the variance reduction parameter. Second, we develop a zeroth-order method with a two-point gradient estimator. There are situations where only one-point estimators can be used, but if both one-point and two-point estimators are available, it is more practical to use the two-point estimator. As theoretical results, we show the convergence of our methods to stationary points and provide the worst-case iteration and sample complexity analysis. Our simulation experiments with real data on a retail service application show that our methods output solutions with lower objective values than the conventional zeroth-order methods.

AAAI 2025

Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions

optimization

poster

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.



Whole-body multimodal motion generation, controlled by text, speech, or music, has numerous applications including video generation and character animation. However, employing a unified model to achieve various generation tasks with different condition modalities presents two main challenges: motion distribution drifts across different tasks (e.g., co-speech gestures and text-driven daily actions) and the complex optimization of mixed conditions with varying granularities (e.g., text and audio). Additionally, inconsistent motion formats across different tasks and datasets hinder effective training toward multimodal motion generation. In this paper, we propose $\textbf{MotionCraft}$, a unified diffusion transformer that crafts whole-body motion with plug-and-play multimodal control. Our framework employs a coarse-to-fine training strategy, starting with the first stage of text-to-motion semantic pre-training, followed by the second stage of multimodal low-level control adaptation to handle conditions of varying granularities. To effectively learn and transfer motion knowledge across different distributions, we design $\textbf{MC-Attn}$ for parallel modeling of static and dynamic human topology graphs. To overcome the motion format inconsistency of existing benchmarks, we introduce $\textbf{MC-Bench}$, the first available multimodal whole-body motion generation benchmark based on the unified SMPL-X format. Extensive experiments show that $\textbf{MotionCraft}$ achieves state-of-the-art performance on various standard motion generation tasks.

MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls

This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It promotes shared representations to preserve necessary information for all tasks and task-specific representations to eliminate redundant information for each task, which can enhance language understanding of pre-trained language models (PLMs) under the multi-task paradigm. Firstly, a shared information maximization principle is proposed to learn more sufficient shared representations for all target task. It adopts an opposite way with existing IB-based MTL to avoid the insufficiency issue. Secondly, a task-specific information minimization (TIMin) is proposed to mitigate the negative effect of potential redundant features in the shared representations for the target task. It alleviates the redundancy issue during the decoding process, which can compress task-irrelevant redundant information and preserves necessary information relevant to the target for multi-task prediction. Experiments on six classification benchmarks demonstrate that our method outperforms comparative multi-task learning approaches with different PLMs. Extensive experiments show that the shared and output representations are more sufficient, data-efficient, and robust.

An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding

The performance of Large Language Models (LLMs) is intrinsically linked to the quality of its training data. Although several studies have proposed methods for high-quality data selection, they do not consider the importance of knowledge richness in text corpora. In this paper, we propose a novel and gradient-free High-Knowledge Scorer (HKS) to select high-quality data from the dimension of knowledge, to alleviate the problem of knowledge scarcity in the pre-trained corpus. We propose a comprehensive multi-domain knowledge element pool and introduce knowledge density and coverage as metrics to assess the knowledge content of the text. Based on this, we propose a comprehensive knowledge scorer to select data with intensive knowledge, which can also be utilized for domain-specific high-knowledge data selection by restricting knowledge elements to the specific domain. We train a 1.1B model on a high-knowledge bilingual dataset, and experimental results demonstrate that our scorer improves the model's performance in knowledge-intensive and general comprehension tasks, and is effective in enhancing both the generic and domain-specific capabilities of the model.

Enhancing LLMs via High-Knowledge Data Selection

Data sharing is necessary for AI to be widely used, but sharing sensitive data with others with privacy is risky.
To solve these problems, it is necessary to synthesize realistic tabular data.
In many cases, tabular data contains a mixture of continuous, mixed, categorical columns.
Moreover, columns of the same type may have multimodal distribution or be highly imbalanced.
These issues make it challenging to synthesize tabular data.
The synthesized tabular data should reflect the relational meaning between columns of tabular data, so modeling the probability distribution of tabular data is a non-trivial task.
Traditional tabular data synthesizing models are based on GAN or diffusion models and are built using fully connected or convolutional layers.
However, fully connected layers have the disadvantage of low inductive bias, and convolutional layers are not invariant to the column order of tabular data.
Therefore, we assume that converting tabular data into graph-structured data and using a graph neural network would produce better synthetic data than using fully connected layers or convolutional layers.
Our study aims to show that GANs constructed with graph neural networks can outperform existing GAN models using fully connected layers or convolutional layers.
We propose CG-TGAN, a conditional GAN built using graph neural networks. To learn how to synthesize realistic data, the graph neural networks in the discriminator and generator learn graph-level tasks and node-level tasks together.
The discriminator of CG-TGAN learns a graph-level task to distinguish between real and synthetic data and node-level tasks to predict the value of the target node.
CG-TGAN’s generator learns a graph-level task to synthesize an overall graph similar to real data and node-level tasks to learn how to synthesize a fake graph with the proper relation between nodes.
In this paper, we show that CG-TGAN outperforms GAN-based models and is comparable to diffusion-based models.

CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing

Offline reinforcement methods (RL) aim to learn optimal policies with access only to trajectories in a fixed dataset. Policy constraint methods formulate policy learning as an optimization problem that balances maximizing reward with minimizing deviation from the behavior policy. Closed form solutions to this problem can be derived as weighted behavioral cloning objectives that, in theory, must compute an intractable partition function. Reinforcement learning has gained popularity in language modeling to align models with human preferences; some recent works consider paired completions that are ranked by a preference model following which the likelihood of the preferred completion is directly increased. We adapt this approach of paired comparison. By reformulating the paired-sample optimization problem, we fit the maximum-mode of the Q function while maximizing behavioral consistency of policy actions. This yields our algorithm, Behavior Preference Regression for offline RL (BPR). We empirically evaluate BPR on the widely used D4RL Locomotion and Antmaze datasets, as well as the more challenging V-D4RL suite, which operates in image-based state spaces. BPR demonstrates state-of-the-art performance over all domains. Our on-policy experiments suggest that BPR takes advantage of the stability of on-policy value functions with minimal performance degradation on Locomotion datasets.

Behaviour Preference Regression for Offline Reinforcement Learning

Scientific discovery serves as the cornerstone for advances in various fields, from the fundamental laws of physics to the intricate mechanisms of biology. 
However, two existing mainstream methods---symbolic regression and dimensional analysis, are significantly limited in this task: the former suffers from low computational efficiency due to the vast search space and often results in formulas without physical meaning; the latter provides a useful theoretical framework but also struggles in searching in a huge space because of lacking effective analysis for the latent variables.
To address this issue, here we propose a framework for efficiently discovering underlying formulas in data, named FIND. We draw inspiration from Buckingham’s Pi theorem, imposing dimensional constraints on the input and output, thereby ensuring discovered expressions possess physical meaning. Additionally, we propose a theoretical scheme for identifying the latent structure as well as a coarse-to-fine framework, significantly reducing the search space of latent variables. This framework not only improves computational efficiency but also enhances model interpretability. From comprehensive experimental validation, FIND showcases its potential to uncover meaningful scientific insights across various domains, providing a robust tool for advancing our understanding of unknown systems.

FIND: A Framework for Discovering Formulas in Data

The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundaries and limited perception ranges, which undermine the robustness of plane model construction. Building on previous work (APD-MVS), we propose the DPE-MVS method. By introducing dual-level precision edge information, including fine and coarse edges, we enhance the robustness of plane model construction, thereby improving reconstruction accuracy in low-textured areas. Furthermore, by leveraging edge information, we refine the sampling strategy in conventional PatchMatch MVS and propose an adaptive patch size adjustment approach to optimize matching cost calculation in both stochastic and low-textured areas. This additional use of edge information allows for more precise and robust matching. Our method achieves state-of-the-art performance on the ETH3D and Tanks & Temples benchmarks. Notably, our method outperforms all published methods on the
 ETH3D benchmark.

Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization

Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement without training. However, directly applying them to video restoration and enhancement results in severe temporal flickering artifacts. In this paper, we propose the first framework for zero-shot video restoration and enhancement based on the pre-trained image diffusion model. By replacing the spatial self-attention layer with the proposed SLR temporal attention layer, the pre-trained image diffusion model can take advantage of the temporal correlation between frames. We further propose temporal consistency guidance, spatial-temporal noise sharing, and an early stopping sampling strategy for better temporally consistent sampling. Our method is a plug-and-play module that can be inserted into any diffusion-based zero-shot image restoration or enhancement methods to further improve their performance. Experimental results demonstrate the superiority of our proposed method.

Zero-shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model

Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are currently focusing on leveraging the rich semantic information of pre-trained models (PTMs) in CIL tasks. Prompt learning has been adopted in CIL for its ability to adjust data distribution to better align with pre-trained knowledge. This paper critically examines the limitations of existing methods from the perspective of prompt learning, which heavily rely on input information. To address this issue, we propose a novel PTM-based CIL method called Input-Agnostic  **P**rompt **E**nhancement with Neg**A**tive Feedback **R**egu**L**ation (**PEARL**). In PEARL, we implement an input-agnostic global prompt coupled with an adaptive momentum update strategy to reduce the model's dependency on data distribution, thereby effectively mitigating catastrophic forgetting. Guided by negative feedback regulation, this adaptive momentum update addresses the parameter sensitivity inherent in fixed-weight momentum updates. Furthermore, it fosters the continuous enhancement of the prompt for new tasks by harnessing correlations between different tasks in CIL. Experiments on six benchmarks demonstrate that our method achieves state-of-the-art performance. The code will be made available.

PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning

Graph-based fraud detection is crucial in identifying illegal activities in social networks, finance, and other sectors. Despite recent progress in this area, most of current researches typically require a large amount of annotated data to demonstrate its benefits. In practice, obtaining sufficient high-quality annotated data is challenging, limiting the effectiveness of model training. Therefore, leveraging extremely limited label information is crucial to enhance model performance. We propose a context-aware graph neural network (CGNN) to address this. CGNN performs category semantic decomposition on the contextual neighbor features of the center node to enrich the category semantics. In the neighbor message aggregation stage, the denoising attention mechanism enables the center node to adaptively aggregate heterophilic and homophilic information from neighbors. Particularly for unlabeled data, feature augmentation within the category subspace and consistency regularization driven by entropy minimization ensure that such data can further enhance model performance under explicit semantic guidance. We demonstrate on four real-world datasets that CGNN significantly outperforms other baseline methods with extremely limited labels.

Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions

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