GenAI4ELab/papercli-papers huggingface dataset:
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
Authors: Marharyta Domnich; Julius Välja; Rasmus Moorits Veski; Giacomo Magnifico; Kadi Tulver; Eduard Barbu; Raul Vicente
Venue: AAAI 2025 (main)

Abstract: As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks.
Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling
Authors: Jinzong Dong; Zhaohui Jiang; Dong Pan; Haoyang Yu
Venue: AAAI 2025 (main)

Abstract: Confidence calibration of classification models is a technique to estimate the true posterior probability of the predicted class, which is critical for ensuring reliable decision-making in practical applications. Existing confidence calibration methods mostly use statistical techniques to estimate the calibration curve from data or fit a user-defined calibration function, but often overlook fully mining and utilizing the prior distribution behind the calibration curve. However, a well-informed prior distribution can provide valuable insights beyond the empirical data under the limited data or low-density regions of confidence scores. To fill this gap, this paper proposes a new method that integrates the prior distribution behind the calibration curve with empirical data to estimate a continuous calibration curve, which is realized by modeling the sampling process of calibration data as a binomial process and maximizing the likelihood function of the binomial process. We prove that the calibration curve estimating method is Lipschitz continuous with respect to data distribution and requires smaller sample sizes than histogram binning. Also, a new calibration metric has been designed, leveraging the estimated calibration curve to estimate the true calibration error, and it has been proven to be a consistent calibration measure. Furthermore, realistic calibration datasets can be generated by the binomial process modeling from a preset true calibration curve and confidence score distribution, which can serve as a benchmark to measure and compare the discrepancy between existing calibration metrics and the true calibration error. The effectiveness of our calibration method and metric are verified in real-world and simulated data. We believe our exploration of integrating prior distributions with empirical data will guide the development of better-calibrated models, contributing to trustworthy AI.
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Authors: Peijie Dong; Lujun Li; Zhenheng Tang; Xiang Liu; Zimian Wei; Qiang Wang; Xiaowen Chu
Venue: AAAI 2025 (main)

Abstract: Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the ability of zero-cost proxies in identifying superior architecture. However, we identify a critical issue with current zero-cost proxies: they aggregate node-wise zero-cost statistics without considering that not all nodes in a neural network equally impact performance estimation. Our observations reveal that node-wise zero-cost statistics significantly vary in their contributions to performance, with each node exhibiting a degree of uncertainty. Based on this insight, we introduce a novel method called Parametric Zero-Cost Proxies (ParZC) framework to enhance the adaptability of zero-cost proxies through parameterization. To address the node indiscrimination, we propose a Mixer Architecture with Bayesian Network (MABN) to explore the node-wise zero-cost statistics and estimate node-specific uncertainty. Moreover, we propose DiffKendall as a loss function to improve ranking consistency. Comprehensive experiments on NAS-Bench-101, 201, and NDS demonstrate the superiority of our proposed ParZC compared to existing zero-shot NAS methods. Additionally, we demonstrate the versatility and adaptability of ParZC on Vision Transformer search space.
Beyond Mandatory Federations: Balancing Egoism, Utilitarianism and Egalitarianism in Mixed-Motive Games
Authors: Shaokang Dong; Chao Li; Shangdong Yang; Hongye Cao; Wanqi Yang; Yang Gao
Venue: AAAI 2025 (main)

Abstract: In the field of mixed-motive games, extensive multi-agent learning studies have explored the balance between egoism (individual interest), utilitarianism (collective interest), and egalitarianism (fairness). Traditional approaches often rely on manually designed reward functions, social norms, and alliance/federation mechanisms to transition agents from individualistic behaviors toward cooperative strategies. However, these methods typically require all agents to share private local information or to mandatorily participate in federations, which is impractical in real-world applications. To address these issues, this paper proposes a Flexible-Participation Federation (FPF) framework that allows agents to participate in the federation voluntarily. Furthermore, we extend the federation from a global to a Local Multi-Federation (LMF) framework, enabling agents to form multiple localized federations, thereby promoting more efficient and adaptive cooperation. Theoretical evidence demonstrates that the global FPF model, along with the discrepancy between decentralized egoistic policies and federated utilitarian policies, achieves an O(1/T) convergence rate. Agents in the LMF framework also reach consensus within a sublinear gap. Extensive experiments show that agents opting out of federation participation experience a reduction in egoism, and our approach outperforms multiple baselines in terms of both utilitarianism and egalitarianism.
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Authors: Shuyu Dong; Michele Sebag; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi
Venue: AAAI 2025 (main)

Abstract: Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov blanket MB(Xi) of each variable Xi is identified, and causal learning subproblems associated with each MB(Xi) are independently addressed in parallel. This approach benefits from a more favorable ratio between the number of data samples and the number of variables considered. In counterpart, it can be adversely affected by the presence of hidden confounders, as variables external to MB(Xi) might influence those within it. The reconciliation of the local causal graphs generated during the divide phase is a challenging combinatorial optimization problem, especially in large-scale applications. The main novelty of DCILP is an original formulation of this reconciliation as an integer linear programming (ILP) problem, which can be delegated and efficiently handled by an ILP solver. Through experiments on medium to large scale graphs, and comparisons with state-of-the-art methods, DCILP demonstrates significant improvements in terms of computational complexity, while preserving the learning accuracy on real-world problem and suffering at most a slight loss of accuracy on synthetic problems.
AtomNet: Designing Tiny Models from Operators Under Extreme MCU Constraints
Authors: Zhiwei Dong; Mingzhu Shen; Shihao Bai; Xiuying Wei; Jinyang Guo; Ruihao Gong; Song-Lu Chen; Xianglong Liu; Xu-Cheng Yin
Venue: AAAI 2025 (main)

Abstract: Tiny machine learning (TinyML) has attracted heightened attention for its ability to provide low-cost and instantaneous performance on edge devices. Particularly, the commonly used microcontroller unit (MCU) imposes extreme constraints on peak memory (SRAM) and storage (Flash). Existing TinyML methods often rely on a customized and hard-to-obtain inference libraries, as well as necessitate a time-consuming search for a deployable architecture using advanced Neural Architecture Search (NAS) algorithms. To solve these problems, we fully exploit the resources on MCU and deduce hardware-oriented guidelines for designing models under extreme MCU constraints. In detail, we delve into thorough information about the atom operators by collecting the runtime data of Flash, SRAM, and latency to build a dataset named AtomDB. Based on AtomDB, several critical operator guidelines are established to fully utilize limited Flash and SRAM, while minimizing latency. By transferring the guidelines to analyze blocks, we propose a hybrid pattern that organizes appropriate blocks at different network stages to form the AtomNet, a more hardware-oriented architecture, to handle the former SRAM bottleneck and the latter Flash bottleneck.  Extensive experiments demonstrate the effectiveness of the exploitation of the hardware characteristics. Remarkably, AtomNet pioneeringly achieve 3.5% accuracy enhancement and more than 15% latency reduction on 320KB MCU using readily available official inference libraries for ImageNet tasks, surpassing the current state-of-the-art method.
On the Hardness of Training Deep Neural Networks Discretely
Authors: Ilan Doron-Arad
Venue: AAAI 2025 (main)

Abstract: We study neural network training (NNT): optimizing a neural network's parameters to minimize the training loss over a given dataset. NNT has been studied extensively under theoretic lenses, mainly on two-layer networks with linear or ReLU activation functions where the parameters can take any real value (here referred to as continuous NNT (C-NNT)). However, less is known about deeper neural networks, which exhibit substantially stronger capabilities in practice. In addition, the complexity of the discrete variant of the problem (D-NNT in short), in which the parameters are taken from a given finite set of options, has remained less explored despite its theoretical and practical significance.     
		
In this work, we show that the hardness of NNT is dramatically affected by the network depth. Specifically, we show that, under standard complexity assumptions, D-NNT is not in the complexity class NP even for instances with fixed dimensions and dataset size, having a deep architecture. This separates D-NNT from any NP-complete problem. Furthermore, using a polynomial reduction we show that the above result also holds for C-NNT, albeit with more structured instances. We complement these results with a comprehensive list of NP-hardness lower bounds for D-NNT on two-layer networks, showing that fixing the number of dimensions, the dataset size, or the number of neurons in the hidden layer leaves the problem challenging. Finally, we obtain a pseudo-polynomial algorithm for D-NNT on a two-layer network with a fixed dataset size.
Rebalancing Multi-Label Class-Incremental Learning
Authors: Kaile Du; Yifan Zhou; Fan Lyu; Yuyang Li; Junzhou Xie; Yixi Shen; Fuyuan Hu; Guangcan Liu
Venue: AAAI 2025 (main)

Abstract: Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the positive-negative imbalance problem, which manifests at both the label and loss levels because of the task-level partial label issue. The imbalance at the label level arises from the substantial absence of negative labels, while the imbalance at the loss level stems from the asymmetric contributions of the positive and negative loss parts to the optimization. To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR). AKD is proposed to rebalance at the loss level by emphasizing the negative label learning in classification loss and down-weighting the contribution of overconfident predictions in distillation loss. OR is designed for label rebalance, which restores the original class distribution in memory by online relabeling the missing classes. Our comprehensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate that this rebalancing strategy significantly improves performance, achieving new state-of-the-art results even with a vanilla CNN backbone.
Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization
Authors: Liang Du; Henghui Jiang; Xiaodong Li; Yiqing Guo; Yan Chen; Feijiang Li; Peng Zhou; Yuhua Qian
Venue: AAAI 2025 (main)

Abstract: Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture high-order correlations across views. To address these limitations, we present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. Our analysis establishes a convergence rate of O(1/n), significantly improving upon the existing rate in the order of O(sqrt(k/n)). Building on this insight, we propose a low-pass graph filtering strategy within a multiple linear K-means framework to mitigate noise and redundancy, further refining the principal eigenvalue proportion and enhancing clustering accuracy. Experimental results on benchmark datasets confirm that our approach outperforms state-of-the-art methods in clustering performance and robustness.
OpenViewer: Openness-Aware Multi-View Learning
Authors: Shide Du; Zihan Fang; Yanchao Tan; Changwei Wang; Shiping Wang; Wenzhong Guo
Venue: AAAI 2025 (main)

Abstract: Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples.
Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs
Authors: Wei Du; Hongmin Sun; Hang Gao; Gaoyang Li; Ying Li
Venue: AAAI 2025 (main)

Abstract: In recent years, methods based on heterogeneous graph neural networks (HGNNs) have been widely used for embedding heterogeneous graphs (HGs) due to their ability to effectively encode the rich information from HGs into low-dimensional node embeddings. Existing HGNNs focus on neighbor aggregation and semantic fusion while neglecting the HG structure and learning paradigms. However, the original HG data might lack node features, which existing models may not effectively account for. Additionally, exclusively relying on a single supervised learning approach may only partially leverage the invariant information in graph data. To address these challenges, we introduce the Contrastive Auxiliary Learning Model for Heterogeneous Graphs (CALHG). This model combines edge perturbation and graph diffusion to enhance graph data, allowing it to capture the inherent structural information within heterogeneous graphs fully. Additionally, we employ a category-guided multi-view contrastive learning approach, which does not rely on positive and negative samples for model training, enabling us to capture the intrinsic invariances in heterogeneous graph data. Extensive experiments and analyses on five benchmark datasets without node features and three benchmark datasets with node features demonstrate the effectiveness and efficiency of our novel method compared with several state-of-the-art methods.
Information-Theoretic Generative Clustering of Documents
Authors: Xin Du; Kumiko Tanaka-Ishii
Venue: AAAI 2025 (main)

Abstract: We present *generative clustering* (GC) for clustering a set of documents, X, by using texts Y generated by large language models (LLMs) instead of by clustering the original documents X.  Because LLMs provide probability distributions, the similarity between two documents can be rigorously defined in an information-theoretic manner by the KL divergence. We also propose a natural, novel clustering algorithm by using importance sampling. We show that GC outperforms any previous clustering method, often by a large margin. Furthermore, we show an application to generative document retrieval in which documents are indexed via hierarchical clustering and our method improves the retrieval accuracy.
Active Reinforcement Learning Strategies for Offline Policy Improvement
Authors: Ambedkar Dukkipati; Ranga Shaarad Ayyagari; Bodhisattwa Dasgupta; Parag Dutta; Prabhas Reddy Onteru
Venue: AAAI 2025 (main)

Abstract: Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively expensive and may involve some constraints, such as a limited budget for agent-environment interactions and restricted exploration in certain regions of the state space. Examples include selecting candidates for medical trials and training agents in complex navigation environments. This problem necessitates the study of active reinforcement learning strategies that collect minimal additional experience trajectories by reusing existing offline data previously collected by some unknown behavior policy.  In this work, we propose an active reinforcement learning method capable of collecting trajectories that can augment existing offline data. With extensive experimentation, we demonstrate that our proposed method reduces additional online interaction with the environment by up to 75% over competitive baselines across various continuous control environments such as Gym-MuJoCo locomotion environments as well as Maze2d, AntMaze, CARLA and IsaacSimGo1. To the best of our knowledge, this is the first work that addresses the active learning problem in the context of sequential decision-making and reinforcement learning.
Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation
Authors: Chen Dun; Mirian Del Carmen Hipolito Garcia; Guoqing Zheng; Ahmed Hassan Awadallah; Robert Sim; Anastasios Kyrillidis
Venue: AAAI 2025 (main)

Abstract: Prompt instruction tuning is a popular approach to better adjust pretrained LLMs for specific downstream tasks. How to extend this approach to simultaneously handle multiple tasks and data distributions is an interesting question. We propose Mixture of Prompts (MoPs) with smart gating functionality. Our proposed system identifies relevant skills embedded in different groups of prompts and dynamically weighs experts (i.e., collection of prompts) based on the target task. Experiments show that MoPs are resilient to model compression, data source, and task composition, making them highly versatile and applicable in various contexts. In practice, MoPs can simultaneously mitigate prompt training ``interference'' in multi-task, multi-source scenarios (e.g., task and data heterogeneity across sources) and possible implications from model approximations. Empirically, MoPs show particular effectiveness in compressed model scenarios, while maintaining favorable performance in uncompressed settings: MoPs can reduce final perplexity from 9% up to 70% in non-i.i.d. distributed cases and from 3% up to 30% in centralized cases, compared to baselines.
Proportionally Fair Matching via Randomized Rounding
Authors: Sharmila Duppala; Nathaniel Grammel; Juan Luque; Calum MacRury; Aravind Srinivasan
Venue: AAAI 2025 (main)

Abstract: Given an edge-colored graph, the goal of the proportional fair matching problem is to find a maximum weight matching while ensuring proportional representation (with respect to the number of edges) of each color. The colors may correspond to demographic groups or other protected traits where we seek to ensure roughly equal representation from each group. It is known that, assuming ETH, it is impossible to approximate the problem with ℓ colors in time subexponential in ℓ even on unweighted path graphs. Further, even determining the existence of a non-empty matching satisfying proportionality is NP-Hard. To overcome this hardness, we relax the stringent proportional fairness constraints to a probabilistic notion. We introduce a notion we call δ-PʀᴏʙᴀʙʟʏFᴀɪʀ where we ensure proportionality up to a factor of at most (1 ± δ) for some small δ > 0 with high probability. The violation δ can be brought arbitrarily close to 0 for some good instances with large values of matching size. We propose and analyze simple and fast algorithms for bipartite graphs that achieve constant-factor approximation guarantees, and return a δ-PʀᴏʙᴀʙʟʏFᴀɪʀ matching.
Linking Industry Sectors and Financial Statements: A Hybrid Approach for Company Classification
Authors: Guy Stephane Waffo Dzuyo; Gaël Guibon; Christophe Cerisara; Luis Belmar-Letelier
Venue: AAAI 2025 (main)

Abstract: The identification of the financial characteristics of industry sectors has a large importance in accounting audit, allowing auditors to prioritize the most important area during audit.
Existing company classification standards such as the Standard Industry Classification (SIC) code allow to map a company to a category based on its activity and products. In this paper, we explore the potential of machine learning algorithms and language models to analyze the relationship between those categories and companies' financial statements. 
We propose a supervised company classification methodology and analyze several types of representations for financial statements. Existing works address this task using solely numerical information in financial records. 
Our findings show that beyond numbers, textual information occurring in financial records can be leveraged by language models to match the performance of dedicated decision tree-based classifiers, while providing better explainability and more generic accounting representations. We think this work can serve as a preliminary work towards semi-automatic auditing.