GenAI4ELab/papercli-papers huggingface dataset:
p-Mean Regret for Stochastic Bandits
Authors: Anand Krishna; Philips George John; Adarsh Barik; Vincent Y. F. Tan
Venue: AAAI 2025 (main)

Abstract: In this work, we extend the concept of the p-mean welfare objective from social choice theory to study p-mean regret in stochastic multi-armed bandit problems. The p-mean regret, defined as the difference between the optimal mean among the arms and the p-mean of the expected rewards, offers a flexible framework for evaluating bandit algorithms, enabling algorithm designers to balance fairness and efficiency by adjusting the parameter p. Our framework encompasses both average cumulative regret and Nash regret as special cases.

We introduce a simple, unified UCB-based algorithm (Explore-Then-UCB) that achieves novel p-mean regret bounds. Our algorithm consists of two phases: a carefully calibrated uniform exploration phase to initialize sample means, followed by the UCB1 algorithm of Auer et al. (2002). Under mild assumptions, we prove that our algorithm achieves a p-mean regret bound of Otilde( sqrt( k / T^{1/(2|p|)} ) ) for all p <= -1, where k represents the number of arms and T the time horizon. When -1< p < 0, we achieve a regret bound of Otilde( sqrt( k^{1.5} / T^{1/2} ) ). For the range 0 < p <= 1, we achieve a p-mean regret scaling as Otilde( sqrt( k / T ) ), which matches the previously established lower bound up to logarithmic factors. This result stems from the fact that the p-mean regret of any algorithm is at least its average cumulative regret for p <= 1.

In the case of Nash regret (the limit as p approaches zero), our unified approach differs from prior work  of Barman et al. (2023), which requires a new Nash Confidence Bound algorithm. Notably, we achieve the same regret bound up to constant factors using our more general method.
Selective Uncertainty Propagation in Offline RL
Authors: Sanath Kumar Krishnamurthy; Tanmay Gangwani; Sumeet Katariya; Branislav Kveton; Shrey Modi; Anshuka Rangi
Venue: AAAI 2025 (main)

Abstract: We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effect of deviating from the behavioral policy at step h after having optimized the policy for all future steps. Since the policy at any step can affect next-state distributions, the related distributional shift challenges can make this problem far more statistically hard than estimating such treatment effects in the stochastic contextual bandit setting. However, the hardness of many real-world RL instances lies between the two regimes. We develop a flexible and general method called selective uncertainty propagation for confidence interval construction that adapts to the hardness of the associated distribution shift challenges. We show benefits of our approach on toy environments and demonstrate the benefits of these techniques for offline policy learning.
Enhanced Importance Sampling Through Latent Space Exploration in Normalizing Flows
Authors: Liam Anthony Kruse; Alexandros Tzikas; Harrison Delecki; Mansur Arief; Mykel J. Kochenderfer
Venue: AAAI 2025 (main)

Abstract: Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest.
By assigning appropriate weights to sampled points, importance sampling allows for more efficient estimation of rare events or tails of distributions. 
However, importance sampling can fail when the proposal distribution does not effectively cover the target distribution. 
In this work, we propose a method for more efficient sampling by updating the proposal distribution in the latent space of a normalizing flow. 
Normalizing flows learn an invertible mapping from a target distribution to a simpler latent distribution. 
The latent space can be more easily explored during the search for a proposal distribution, and samples from the proposal distribution are recovered in the space of the target distribution via the invertible mapping. 
We empirically validate our methodology on simulated robotics applications such as autonomous racing and aircraft ground collision avoidance.
Crossfire: An Elastic Defense Framework for Graph Neural Networks Under Bit Flip Attacks
Authors: Lorenz Kummer; Samir Moustafa; Wilfried Gansterer; Nils Morten Kriege
Venue: AAAI 2025 (main)

Abstract: Bit Flip Attacks (BFAs) are a well-established class of adversarial attacks, originally developed for Convolutional Neural Networks within the computer vision domain. Most recently, these attacks have been extended to target Graph Neural Networks (GNNs), revealing significant vulnerabilities. This new development naturally raises questions about the best strategies to defend GNNs against BFAs, a challenge for which no solutions currently exist. Given the applications of GNNs in critical fields, any defense mechanism must not only maintain network performance, but also verifiably restore the network to its pre-attack state. Verifiably restoring the network to its pre-attack state also eliminates the need for costly evaluations on test data to ensure network quality. We offer first insights into the effectiveness of existing honeypot- and hashing-based defenses against BFAs adapted from the computer vision domain to GNNs, and characterize the shortcomings of these approaches. To overcome their limitations, we propose Crossfire, a hybrid approach that exploits weight sparsity and combines hashing and honeypots with bit-level correction of out-of-distribution weight elements to restore network integrity. Crossfire is retraining-free and does not require labeled data. Averaged over 2,160 experiments on six benchmark datasets, Crossfire offers a 21.8% higher probability than its competitors of reconstructing a GNN attacked by a BFA to its pre-attack state. These experiments cover up to 55 bit flips from various attacks. Moreover, it improves post repair prediction quality by 10.85%. Computational and storage overheads are negligible compared to the inherent complexity of even the simplest GNNs.
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Authors: Jiahao Lai; Jiaqi Li; Jian Xu; Yanru Wu; Boshi Tang; Siqi Chen; Yongfeng Huang; Wenbo Ding; Yang Li
Venue: AAAI 2025 (main)

Abstract: Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server. Traditional methods, such as Federated Averaging (FedAvg), linearly aggregate these parameters which are usually trained on heterogeneous data distributions, potentially overlooking the complex, high-dimensional nature of the parameter space. This can result in degraded performance of the aggregated model. While personalized FL approaches can mitigate the heterogeneous data issue to some extent, the limitation of linear aggregation remains unresolved. To alleviate this issue, we investigate the generative approach of diffusion model and propose a novel generative parameter aggregation framework for personalized FL, pFedGPA. In this framework, we deploy a diffusion model on the server to integrate the diverse parameter distributions and propose a parameter inversion method to efficiently generate a set of personalized parameters for each client. This inversion method transforms the uploaded parameters into a latent code, which is then aggregated through denoising sampling to produce the final personalized parameters. By encoding the dependence of a client's model parameters on the specific data distribution using the high-capacity diffusion model, pFedGPA can effectively decouple the complexity of the overall distribution of all clients' model parameters from the complexity of each individual client's parameter distribution. Our experimental results consistently demonstrate the superior performance of the proposed method across multiple datasets, surpassing baseline approaches.
Pareto Continual Learning: Preference-Conditioned Learning and Adaption for Dynamic Stability-Plasticity Trade-off
Authors: Song Lai; Zhe Zhao; Fei Zhu; Xi Lin; Qingfu Zhang; Gaofeng Meng
Venue: AAAI 2025 (main)

Abstract: Continual learning aims to learn multiple tasks sequentially. A key challenge in continual learning is balancing between two objectives: retaining knowledge from old tasks (stability) and adapting to new tasks (plasticity). Experience replay methods, which store and replay past data alongside new data, have become a widely adopted approach to mitigate catastrophic forgetting. However, these methods neglect the dynamic nature of the stability-plasticity trade-off and aim to find a fixed and unchanging balance, resulting in suboptimal adaptation during training and inference. In this paper, we propose Pareto Continual Learning (ParetoCL), a novel framework that reformulates the stability-plasticity trade-off in continual learning as a multi-objective optimization (MOO) problem. ParetoCL introduces a preference-conditioned model to efficiently learn a set of Pareto optimal solutions representing different trade-offs and enables dynamic adaptation during inference. From a generalization perspective, ParetoCL can be seen as an objective augmentation approach that learns from different objective combinations of stability and plasticity. Extensive experiments across multiple datasets and settings demonstrate that ParetoCL outperforms state-of-the-art methods and adapts to diverse continual learning scenarios.
CoffeeBoost: Gradient Boosting Native Conformal Inference for Bayesian Optimization
Authors: Yuanhao Lai; Pengfei Zheng; Chenpeng Ji; Cheng Qiu; Tingkai Wang; Songhan Zhang; Zhengang Wang; Yunfei Du
Venue: AAAI 2025 (main)

Abstract: Bayesian optimization (BO) is a key technique for solving black-box optimization problems. This study extends the scope of BO from conventional applications (e.g., AutoML and robotics learning) to automated tuning of software systems. Despite GP (Gaussian Process) implementing a foundation formalism for exploitation and exploration in BO, its limited predictive power and unrealistic assumptions (e.g., continuity and Gaussianity) can severely affect its effectiveness and efficiency in tuning complex software systems. To overcome these limitations, we propose a BO framework CoffeeBoost, which implements exploitation and exploration with a GBDT-native distribution-free probabilistic surrogate model. CoffeeBoost constructs surrogate models via stochastic gradient boosting ensembles (SGBE) and quantifies probabilistic distributions via distribution-free conformal predictive systems. Moreover, CoffeeBoost leverages the residual paths in SGBE to improve the local adaptiveness of the resulting predictive distributions in a GBDT-native manner. Across eight auto-tuning benchmarks for database management systems (DBMS), we evaluate CoffeeBoost and show its superior learnability and optimizability against existing GP-based and tree-ensemble-based BO schemes. Detailed analysis further shows CoffeeBoost's predictive distributions excel in both coverage and tightness.
De-singularity Subgradient for the q-th-Powered lₚ-Norm Weber Location Problem
Authors: Zhao-Rong Lai; Xiaotian Wu; Liangda Fang; Ziliang Chen; Cheng Li
Venue: AAAI 2025 (main)

Abstract: The Weber location problem is widely used in several artificial intelligence scenarios. However, the gradient of the objective does not exist at a considerable set of singular points. Recently, a de-singularity subgradient method has been proposed to fix this problem, but it can only handle the q-th-powered l_2-norm case (1<= q<2), which has only finite singular points. In this paper, we further establish the de-singularity subgradient for the q-th-powered l_p-norm case with 1<= q<= p and 1<= p<2, which includes all the rest unsolved situations in this problem. This is a challenging task because the singular set is a continuum. The geometry of the objective function is also complicated so that the characterizations of the subgradients, minimum and descent direction are very difficult. We develop a q-th-powered l_p-norm Weiszfeld Algorithm without Singularity (qPpNWAWS) for this problem, which ensures convergence and the descent property of the objective function. Extensive experiments on six real-world data sets demonstrate that qPpNWAWS successfully solves the singularity problem and achieves a linear computational convergence rate in practical scenarios.
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Authors: Jian Lang; Zhangtao Cheng; Ting Zhong; Fan Zhou
Venue: AAAI 2025 (main)

Abstract: Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MMTs) under missing modality conditions by applying learnable prompts. However, these prompt-based methods face several limitations: (1) incomplete modalities provide restricted modal cues for task-specific inference, (2) dummy imputation for missing content causes information loss and introduces noise, and (3) static prompts are instance-agnostic, offering limited knowledge for instances with various missing conditions. To address these issues, we propose RAGPT, a novel Retrieval-AuGmented dynamic Prompt Tuning framework. RAGPT comprises three modules: (I) the multi-channel retriever, which identifies similar instances through a within-modality retrieval strategy, (II) the missing modality generator, which recovers missing information using retrieved contexts, and (III) the context-aware prompter, which captures contextual knowledge from relevant instances and generates dynamic prompts to largely enhance the MMT’s robustness. Extensive experiments conducted on three real-world datasets show that RAGPT consistently outperforms all competitive baselines in handling incomplete modality problems.
Explainable Neural Networks with Guarantee: A Sparse Estimation Approach
Authors: Antoine Ledent; Peng Liu
Venue: AAAI 2025 (main)

Abstract: Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being a sum of rescaled versions of these features. We demonstrate the ability to select significant features while maintaining comparable predictive performance and direct interpretability through extensive experiments on synthetic and real-world datasets. We also provide theoretical analysis on the generalization bounds of our framework, which is favorably linear in the number of selected features and only logarithmic in the number of input features. We further lift any dependence of sample complexity on the number of parameters or the architectural details under very mild conditions. Our research paves the way for further research on sparse and explainable neural networks with guarantees.
Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP
Authors: Anton Lee; Heitor Murilo Gomes; Yaqian Zhang; W. Bastiaan Kleijn
Venue: AAAI 2025 (main)

Abstract: Catastrophic forgetting is when a neural network loses previously learnt information after learning a new task sequentially. Avoiding catastrophic forgetting could reduce the resources necessary to update neural networks. Recently, Kolmogorov–Arnold Networks (KAN) gained the community's attention as preliminary experiments suggest KAN avoid catastrophic forgetting. KAN replace neural network edges with learnable B-splines and sum incoming edges in nodes. Proponents of KAN argue they avoid forgetting, are more accurate, are interpretable, and use fewer parameters. Our work investigates the claims that KAN avoid catastrophic forgetting, finding that they fail to do so on more complex datasets containing features that overlap between tasks. We give a simple explanation as to why and how KAN catastrophically forget. Motivated by evidence suggesting KAN are superior for symbolic regression, we augment KAN in the same ways as multilayer perceptron (MLP) to perform continual learning tasks, making special accommodations to support KAN. Our experiments found that unmodified KAN often forget more than MLP, but KAN can be better than MLP when combined with continual learning strategies. We aim to highlight some of the current shortcomings and strengths associated with KAN for continual learning.
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
Authors: Bruce D. Lee; Leonardo F. Toso; Thomas T. Zhang; James Anderson; Nikolai Matni
Venue: AAAI 2025 (main)

Abstract: Representation learning is a powerful tool that enables learning over large multitudes of agents or domains by enforcing that all agents operate on a shared set of learned features. However, many robotics or controls applications that would benefit from collaboration operate in settings with changing environments and goals, whereas most guarantees for representation learning are stated for static settings. Toward rigorously establishing the benefit of representation learning in dynamic settings, we analyze the regret of multi-task representation learning for linear-quadratic control. This setting introduces unique challenges. Firstly, we must account for and balance the misspecification introduced by an approximate representation. Secondly, we cannot rely on the parameter update schemes of single-task online LQR, for which least-squares often suffices, and must devise a novel scheme to ensure sufficient improvement. We demonstrate that for settings where exploration is "benign", the regret of any agent after T timesteps scales with the square root of T/H, where H is the number of agents. In settings with "difficult" exploration, the regret scales as the square root of the input dimension times the parameter dimension multiplied by T, plus a term which scales with T to the three quarters divided by H to the one fifth. In both cases, by comparing to the minimax single-task regret, we see a benefit of a large number of agents. Notably, in the difficult exploration case, by sharing a representation across tasks, the effective task-specific parameter count can often be small. Lastly, we validate the trends we predict.
Truncated Gaussian Policy for Debiased Continuous Control
Authors: Ganghun Lee; Minji Kim; Minsu Lee; Byoung-Tak Zhang
Venue: AAAI 2025 (main)

Abstract: In continuous domains, reinforcement learning policies are often based on Gaussian distributions for their generality. However, the unbounded support of Gaussian policy can cause a bias toward sampling boundary actions in many continuous control tasks that impose action limits due to physical constraints. This "boundary action bias'' can negatively impact training in algorithms like Proximal Policy Optimization. Despite this, it has been overlooked in many existing research and applications. In this paper, we revisit this issue by presenting illustrative explanations and analysis from the sampling point of view. Then, we introduce a truncated Gaussian policy with inherent bounds as a minimal alternative to mitigate the bias. However, we find that the plain truncated Gaussian policy may lay the counter-bias, preferring interior actions: to balance the bias, we ultimately propose a scale-adjusted truncated Gaussian policy, where the distribution scale shrinks if the location is near the boundaries. This property makes boundary actions deterministic more than in plain truncated Gaussian, but still less than in original Gaussian. Extensive empirical studies and comparisons on various continuous control tasks demonstrate that the truncated Gaussian policies significantly reduce the rate of boundary action usage, while scale-adjusted ones successfully balance the bias and counter-bias. It generally outperforms the Gaussian policy and shows competitive results compared to other approaches designed to counteract the bias.
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
Authors: Geon Lee; Wenchao Yu; Kijung Shin; Wei Cheng; Haifeng Chen
Venue: AAAI 2025 (main)

Abstract: Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions. In addition, TimeCAP employs a multi-modal encoder that synergizes with the LLM agents, enhancing predictive performance through mutual augmentation of inputs with in-context examples. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction, including those utilizing LLMs as predictors, achieving an average improvement of 28.75% in F1 score.
RILQ: Rank-Insensitive LoRA-Based Quantization Error Compensation for Boosting 2-Bit Large Language Model Accuracy
Authors: Geonho Lee; Janghwan Lee; Sukjin Hong; Minsoo Kim; Euijai Ahn; Du-Seong Chang; Jungwook Choi
Venue: AAAI 2025 (main)

Abstract: Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful tool for recovering accuracy in compressed LLMs. However, LQEC has underperformed in sub-4-bit scenarios, with no prior investigation into understanding this limitation. We propose RILQ (Rank-Insensitive LoRA-based Quantization Error Compensation) to boost 2-bit LLM accuracy. Based on rank analysis revealing model-wise activation discrepancy loss's rank-insensitive nature, RILQ employs this loss to adjust adapters cooperatively across layers, enabling robust error compensation with low-rank adapters. Evaluations on LLaMA-2 and LLaMA-3 demonstrate RILQ's consistent improvements in 2-bit quantized inference across various state-of-the-art quantizers and enhanced accuracy in task-specific fine-tuning. RILQ maintains computational efficiency comparable to existing LoRA methods, enabling adapter-merged weight-quantized LLM inference with significantly enhanced accuracy, making it a promising approach for boosting 2-bit LLM performance.