pwc-archive/papers-with-abstracts huggingface dataset:
Federated Smoothing ADMM for Localization
Authors: Reza Mirzaeifard, Ashkan Moradi, Masahiro Yukawa, Stefan Werner
Date: 2025-03-12 00:00:00
Tasks: Computational Efficiency

This paper addresses the challenge of localization in federated settings, which are characterized by distributed data, non-convexity, and non-smoothness. To tackle the scalability and outlier issues inherent in such environments, we propose a robust algorithm that employs an $\ell_1$-norm formulation within a novel federated ADMM framework. This approach addresses the problem by integrating an iterative smooth approximation for the total variation consensus term and employing a Moreau envelope approximation for the convex function that appears in a subtracted form. This transformation ensures that the problem is smooth and weakly convex in each iteration, which results in enhanced computational efficiency and improved estimation accuracy. The proposed algorithm supports asynchronous updates and multiple client updates per iteration, which ensures its adaptability to real-world federated systems. To validate the reliability of the proposed algorithm, we show that the method converges to a stationary point, and numerical simulations highlight its superior performance in convergence speed and outlier resilience compared to existing state-of-the-art localization methods.
Large Language Models for Multi-Facility Location Mechanism Design
Authors: Nguyen Thach, Fei Liu, Houyu Zhou, Hau Chan
Date: 2025-03-12 00:00:00

Designing strategyproof mechanisms for multi-facility location that optimize social costs based on agent preferences had been challenging due to the extensive domain knowledge required and poor worst-case guarantees. Recently, deep learning models have been proposed as alternatives. However, these models require some domain knowledge and extensive hyperparameter tuning as well as lacking interpretability, which is crucial in practice when transparency of the learned mechanisms is mandatory. In this paper, we introduce a novel approach, named LLMMech, that addresses these limitations by incorporating large language models (LLMs) into an evolutionary framework for generating interpretable, hyperparameter-free, empirically strategyproof, and nearly optimal mechanisms. Our experimental results, evaluated on various problem settings where the social cost is arbitrarily weighted across agents and the agent preferences may not be uniformly distributed, demonstrate that the LLM-generated mechanisms generally outperform existing handcrafted baselines and deep learning models. Furthermore, the mechanisms exhibit impressive generalizability to out-of-distribution agent preferences and to larger instances with more agents.
Strategyproof Reinforcement Learning from Human Feedback
Authors: Thomas Kleine Buening, Jiarui Gan, Debmalya Mandal, Marta Kwiatkowska
Date: 2025-03-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We study Reinforcement Learning from Human Feedback (RLHF), where multiple individuals with diverse preferences provide feedback strategically to sway the final policy in their favor. We show that existing RLHF methods are not strategyproof, which can result in learning a substantially misaligned policy even when only one out of $k$ individuals reports their preferences strategically. In turn, we also find that any strategyproof RLHF algorithm must perform $k$-times worse than the optimal policy, highlighting an inherent trade-off between incentive alignment and policy alignment. We then propose a pessimistic median algorithm that, under appropriate coverage assumptions, is approximately strategyproof and converges to the optimal policy as the number of individuals and samples increases.
Manify: A Python Library for Learning Non-Euclidean Representations
Authors: Philippe Chlenski, Kaizhu Du, Dylan Satow, Itsik Pe'er
Date: 2025-03-12 00:00:00
Tasks: Representation Learning

We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings in (products of) non-Euclidean spaces, performing classification and regression with data that lives in such spaces, and estimating the curvature of a manifold. Manify aims to advance research and applications in machine learning by offering a comprehensive suite of tools for manifold-based data analysis. Our source code, examples, datasets, results, and documentation are available at https://github.com/pchlenski/manify
A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
Authors: Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P Fisher-Hoch, Joseph B Mccormick
Date: 2024-09-20 00:00:00
Tasks: Graph Neural Network

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.
Accurate INT8 Training Through Dynamic Block-Level Fallback
Authors: Pengle Zhang, Jia Wei, Jintao Zhang, Jun Zhu, Jianfei Chen
Date: 2025-03-11 00:00:00
Tasks: Quantization

Transformer models have achieved remarkable success across various AI applications but face significant training costs. Low-bit training, such as INT8 training, can leverage computational units with higher throughput, and has already demonstrated its effectiveness on GPT2 models with block-level quantization. However, it struggles with modern Transformer variants incorporating GLU units. This is because those variants demonstrate complex distributions of activation outliers. To address the challenge, we propose Fallback Quantization, implementing mixed-precision GEMM that dynamically falls back 8-bit to 16-bit for activation blocks containing outliers. Experiments show that our approach is robustly competent in both fine-tuning and pretraining settings. Moreover, our method achieves a 1.57x end-to-end training speedup on RTX4090 GPUs.
Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning
Authors: Wenyi Lian, Joakim Lindblad, Patrick Micke, Nataša Sladoje
Date: 2025-03-12 00:00:00

Vision Transformers (ViTs) have achieved remarkable success in standard RGB image processing tasks. However, applying ViTs to multi-channel imaging (MCI) data, e.g., for medical and remote sensing applications, remains a challenge. In particular, MCI data often consist of layers acquired from different modalities. Directly training ViTs on such data can obscure complementary information and impair the performance. In this paper, we introduce a simple yet effective pretraining framework for large-scale MCI datasets. Our method, named Isolated Channel ViT (IC-ViT), patchifies image channels individually and thereby enables pretraining for multimodal multi-channel tasks. We show that this channel-wise patchifying is a key technique for MCI processing. More importantly, one can pretrain the IC-ViT on single channels and finetune it on downstream multi-channel datasets. This pretraining framework captures dependencies between patches as well as channels and produces robust feature representation. Experiments on various tasks and benchmarks, including JUMP-CP and CHAMMI for cell microscopy imaging, and So2Sat-LCZ42 for satellite imaging, show that the proposed IC-ViT delivers 4-14 percentage points of performance improvement over existing channel-adaptive approaches. Further, its efficient training makes it a suitable candidate for large-scale pretraining of foundation models on heterogeneous data.
Quantization for OpenAI's Whisper Models: A Comparative Analysis
Authors: Allison Andreyev
Date: 2025-03-12 00:00:00
Tasks: Quantization, speech-recognition, Speech Recognition

Automated speech recognition (ASR) models have gained prominence for applications such as captioning, speech translation, and live transcription. This paper studies Whisper and two model variants: one optimized for live speech streaming and another for offline transcription. Notably, these models have been found to generate hallucinated content, reducing transcription reliability. Furthermore, larger model variants exhibit increased latency and pose challenges for deployment on resource-constrained devices. This study analyzes the similarities and differences between three Whisper models, qualitatively examining their distinct capabilities. Next, this study quantifies the impact of model quantization on latency and evaluates its viability for edge deployment. Using the open source LibriSpeech dataset, this paper evaluates the word error rate (WER) along with latency analysis of whispercpp using 3 quantization methods (INT4, INT5, INT8). Results show that quantization reduces latency by 19\% and model size by 45\%, while preserving transcription accuracy. These findings provide insights into the optimal use cases of different Whisper models and edge device deployment possibilities. All code, datasets, and implementation details are available in a public GitHub repository: https://github.com/allisonandreyev/WhisperQuantization.git
Neural network-based identification of state-space switching nonlinear systems
Authors: Yanxin Zhang, Chengpu Yu, Filippo Fabiani
Date: 2025-03-13 00:00:00
Tasks: Management, parameter estimation

We design specific neural networks (NNs) for the identification of switching nonlinear systems in the state-space form, which explicitly model the switching behavior and address the inherent coupling between system parameters and switching modes. This coupling is specifically addressed by leveraging the expectation-maximization (EM) framework. In particular, our technique will combine a moving window approach in the E-step to efficiently estimate the switching sequence, together with an extended Kalman filter (EKF) in the M-step to train the NNs with a quadratic convergence rate. Extensive numerical simulations, involving both academic examples and a battery charge management system case study, illustrate that our technique outperforms available ones in terms of parameter estimation accuracy, model fitting, and switching sequence identification.
Towards Robust Model Evolution with Algorithmic Recourse
Authors: Hao-Tsung Yang, Jie Gao, Bo-Yi Liu, Zhi-Xuan Liu
Date: 2025-03-12 00:00:00
Tasks: Decision Making, model

Algorithmic Recourse is a way for users to modify their attributes to align with a model's expectations, thereby improving their outcomes after receiving unfavorable decisions. In real-world scenarios, users often need to strategically adjust their attributes to compete for limited resources. However, such strategic behavior induces users to "game" algorithms, causing model collapse due to distribution shifts. These shifts arise from user competition, resource constraints, and adaptive user responses. While prior research on Algorithmic Recourse has explored its effects on both systems and users, the impact of resource constraints and competition over time remains underexplored. In this work, we develop a general framework to model user strategic behaviors and their interactions with decision-making systems under resource constraints and competitive dynamics. Through theoretical analysis and empirical evaluation, we identify three key phenomena that arise consistently in both synthetic and real-world datasets: escalating decision boundaries, non-robust model predictions, and inequitable recourse actions. Finally, we discuss the broader social implications of these findings and present two algorithmic strategies aimed at mitigating these challenges.
CoSTA$\ast$: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing
Authors: Advait Gupta, NandaKiran Velaga, Dang Nguyen, Tianyi Zhou
Date: 2025-03-13 00:00:00

Text-to-image models like stable diffusion and DALLE-3 still struggle with multi-turn image editing. We decompose such a task as an agentic workflow (path) of tool use that addresses a sequence of subtasks by AI tools of varying costs. Conventional search algorithms require expensive exploration to find tool paths. While large language models (LLMs) possess prior knowledge of subtask planning, they may lack accurate estimations of capabilities and costs of tools to determine which to apply in each subtask. Can we combine the strengths of both LLMs and graph search to find cost-efficient tool paths? We propose a three-stage approach "CoSTA*" that leverages LLMs to create a subtask tree, which helps prune a graph of AI tools for the given task, and then conducts A* search on the small subgraph to find a tool path. To better balance the total cost and quality, CoSTA* combines both metrics of each tool on every subtask to guide the A* search. Each subtask's output is then evaluated by a vision-language model (VLM), where a failure will trigger an update of the tool's cost and quality on the subtask. Hence, the A* search can recover from failures quickly to explore other paths. Moreover, CoSTA* can automatically switch between modalities across subtasks for a better cost-quality trade-off. We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference.
Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning
Authors: Duc Kien Doan, Bang Giang Le, Viet Cuong Ta
Date: 2025-03-13 00:00:00
Tasks: Contrastive Learning, Representation Learning, Safe Reinforcement Learning

In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-function from the related environments to prevent unsafe actions. However, because of the large number of false positives, some safe actions are never executed, leading to inadequate exploration in sparse-reward environments. In this work, we aim to learn an efficient state representation to balance the exploration and safety-prefer action in a sparse-reward environment. Firstly, the image input is mapped to latent representation by an auto-encoder. A further contrastive learning objective is employed to distinguish safe and unsafe states. In the learning phase, the latent distance is used to construct an additional safety check, which allows the agent to bias the exploration if it visits an unsafe state. To verify the effectiveness of our method, the experiment is carried out in three navigation-based MiniGrid environments. The result highlights that our method can explore the environment better while maintaining a good balance between safety and efficiency.
The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation
Authors: Ho Kei Cheng, Alexander Schwing
Date: 2025-03-13 00:00:00

Minibatch optimal transport coupling straightens paths in unconditional flow matching. This leads to computationally less demanding inference as fewer integration steps and less complex numerical solvers can be employed when numerically solving an ordinary differential equation at test time. However, in the conditional setting, minibatch optimal transport falls short. This is because the default optimal transport mapping disregards conditions, resulting in a conditionally skewed prior distribution during training. In contrast, at test time, we have no access to the skewed prior, and instead sample from the full, unbiased prior distribution. This gap between training and testing leads to a subpar performance. To bridge this gap, we propose conditional optimal transport C^2OT that adds a conditional weighting term in the cost matrix when computing the optimal transport assignment. Experiments demonstrate that this simple fix works with both discrete and continuous conditions in 8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method performs better overall compared to the existing baselines across different function evaluation budgets. Code is available at https://hkchengrex.github.io/C2OT
MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation
Authors: Yuxiang Fu, Qi Yan, Lele Wang, Ke Li, Renjie Liao
Date: 2025-03-13 00:00:00
Proceeding: CVPR 2025 1
Tasks: motion prediction, Trajectory Forecasting

In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues. We propose a novel motion prediction conditional flow matching model, termed MoFlow, to predict K-shot future trajectories for all agents in a given scene. We design a novel flow matching loss function that not only ensures at least one of the $K$ sets of future trajectories is accurate but also encourages all $K$ sets of future trajectories to be diverse and plausible. Furthermore, by leveraging the implicit maximum likelihood estimation (IMLE), we propose a novel distillation method for flow models that only requires samples from the teacher model. Extensive experiments on the real-world datasets, including SportVU NBA games, ETH-UCY, and SDD, demonstrate that both our teacher flow model and the IMLE-distilled student model achieve state-of-the-art performance. These models can generate diverse trajectories that are physically and socially plausible. Moreover, our one-step student model is $\textbf{100}$ times faster than the teacher flow model during sampling. The code, model, and data are available at our project page: https://moflow-imle.github.io
Label Unbalance in High-frequency Trading
Authors: Zijian Zhao, Xuming Zhang, Jiayu Wen, Mingwen Liu, Xiaoteng Ma
Date: 2025-03-13 00:00:00
Tasks: Deep Learning

In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample, labeling each time/event as profitable or unprofitable, under the transaction cost, especially in the high-frequency trading world, suffers from serious label imbalance issue.In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods and succeed in predicting in high-frequency return in the Chinese future market. The code for our method is publicly available at https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading .
DGNN: A Neural PDE Solver Induced by Discontinuous Galerkin Methods
Authors: GuanYu Chen, Shengze Xu, Dong Ni, Tieyong Zeng
Date: 2025-03-13 00:00:00

We propose a general framework for the Discontinuous Galerkin-induced Neural Network (DGNN), inspired by the Interior Penalty Discontinuous Galerkin Method (IPDGM). In this approach, the trial space consists of piecewise neural network space defined over the computational domain, while the test function space is composed of piecewise polynomials. We demonstrate the advantages of DGNN in terms of accuracy and training efficiency across several numerical examples, including stationary and time-dependent problems. Specifically, DGNN easily handles high perturbations, discontinuous solutions, and complex geometric domains.
Vision-Language Model IP Protection via Prompt-based Learning
Authors: Lianyu Wang, Meng Wang, Huazhu Fu, Daoqiang Zhang
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Language Modeling, Language Modelling

Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage; it also necessitates restricting model deployment to authorized data domains, particularly when the model is fine-tuned for specific target domains. However, current IP protection methods often rely solely on the visual backbone, which may lack sufficient semantic richness. To bridge this gap, we introduce IP-CLIP, a lightweight IP protection strategy tailored to CLIP, employing a prompt-based learning approach. By leveraging the frozen visual backbone of CLIP, we extract both image style and content information, incorporating them into the learning of IP prompt. This strategy acts as a robust barrier, effectively preventing the unauthorized transfer of features from authorized domains to unauthorized ones. Additionally, we propose a style-enhancement branch that constructs feature banks for both authorized and unauthorized domains. This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP's capability to block features from unauthorized domains. Finally, we present new three metrics designed to better balance the performance degradation of authorized and unauthorized domains. Comprehensive experiments in various scenarios demonstrate its promising potential for application in IP protection tasks for VLMs.