pwc-archive/papers-with-abstracts huggingface dataset:
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Authors: Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S. Weld, Peter Clark
Date: 2025-03-20 00:00:00
Tasks: Articles, scientific discovery

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries.
Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
Authors: Francesca Meimeti, Loukas Triantafyllopoulos, Aikaterini Sakagianni, Vasileios Kaldis, Lazaros Tzelves, Nikolaos Theodorakis, Evgenia Paxinou, Georgios Feretzakis, Dimitris Kalles, Vassilios S. Verykios
Date: 2025-03-18 00:00:00
Tasks: Management, Specificity

The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five algorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial). Among these, RF demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data. These findings highlight the robustness of RF in handling complex datasets for admission prediction, establish MIMIC-IV as a valuable benchmark for validating models based on smaller local datasets, and provide actionable insights for improving ED management strategies.
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Authors: Bowei Tian, Xuntao Lyu, Meng Liu, Hongyi Wang, Ang Li
Date: 2025-03-25 00:00:00
Tasks: Descriptive, Fairness

Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circuits. It has proven effective in improving interpretability and control, showing that representations can emerge, propagate, and shape final model outputs in large language models (LLMs). However, in Vision-Language Models (VLMs), visual input can override factual linguistic knowledge, leading to hallucinated responses that contradict reality. To address this challenge, we make the first attempt to extend RepE to VLMs, analyzing how multimodal representations are preserved and transformed. Building on our findings and drawing inspiration from successful RepE applications, we develop a theoretical framework that explains the stability of neural activity across layers using the principal eigenvector, uncovering the underlying mechanism of RepE. We empirically validate these instrinsic properties, demonstrating their broad applicability and significance. By bridging theoretical insights with empirical validation, this work transforms RepE from a descriptive tool into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.
Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping
Authors: Mohammad Saif Nazir, Chayan Banerjee
Date: 2025-03-26 00:00:00
Tasks: continuous-control, Continuous Control

Reinforcement learning often faces challenges with reward misalignment, where agents optimize for given rewards but fail to exhibit the desired behaviors. This occurs when the reward function incentivizes proxy behaviors that diverge from the true objective. While human-in-the-loop (HIL) methods can help, they may exacerbate the problem, as humans are prone to biases that lead to inconsistent, subjective, or misaligned feedback, complicating the learning process. To address these issues, we propose two key contributions. First, we extend the use of zero-shot, off-the-shelf large language models (LLMs) for reward shaping beyond natural language processing (NLP) to continuous control tasks. By leveraging LLMs as direct feedback providers, we replace surrogate models trained on human feedback, which often suffer from the bias inherent in the feedback data it is trained on. Second, we introduce a hybrid framework (LLM-HFBF) that enables LLMs to identify and correct biases in human feedback while incorporating this feedback into the reward shaping process. The LLM-HFBF framework creates a more balanced and reliable system by addressing both the limitations of LLMs (e.g., lack of domain-specific knowledge) and human supervision (e.g., inherent biases). By enabling human feedback bias flagging and correction, our approach improves reinforcement learning performance and reduces reliance on potentially biased human guidance. Empirical experiments show that biased human feedback significantly reduces performance, with average episodic reward (AER) dropping from 28.472 in (unbiased approaches) to 7.039 (biased with conservative bias). In contrast, LLM-based approaches maintain a matching AER like unbiased feedback, even in custom edge case scenarios.
A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation
Authors: Haonan Shi, Long Tian, Jie Tao, Yufei Li, Liming Wang, Xiyang Liu
Date: 2025-03-26 00:00:00
Tasks: Weather Forecasting

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.
Uncertainty Weighted Gradients for Model Calibration
Authors: Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu
Date: 2025-03-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, model

Model calibration is essential for ensuring that the predictions of deep neural networks accurately reflect true probabilities in real-world classification tasks. However, deep networks often produce over-confident or under-confident predictions, leading to miscalibration. Various methods have been proposed to address this issue by designing effective loss functions for calibration, such as focal loss. In this paper, we analyze its effectiveness and provide a unified loss framework of focal loss and its variants, where we mainly attribute their superiority in model calibration to the loss weighting factor that estimates sample-wise uncertainty. Based on our analysis, existing loss functions fail to achieve optimal calibration performance due to two main issues: including misalignment during optimization and insufficient precision in uncertainty estimation. Specifically, focal loss cannot align sample uncertainty with gradient scaling and the single logit cannot indicate the uncertainty. To address these issues, we reformulate the optimization from the perspective of gradients, which focuses on uncertain samples. Meanwhile, we propose using the Brier Score as the loss weight factor, which provides a more accurate uncertainty estimation via all the logits. Extensive experiments on various models and datasets demonstrate that our method achieves state-of-the-art (SOTA) performance.
Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Authors: Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier, Erwan Scornet
Date: 2025-03-26 00:00:00
Tasks: Binary Classification, Event Detection

This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic samples from the minority class. However, SMOTE is intrinsically designed for continuous input variables. In fact, despite SMOTE-NC-its default extension to handle mixed features (continuous and categorical variables)-very few works propose procedures to synthesize mixed features. On the other hand, many real-world classification tasks, such as in banking sector, deal with mixed features, which have a significant impact on predictive performances. To this purpose, we introduce MGS-GRF, an oversampling strategy designed for mixed features. This method uses a kernel density estimator with locally estimated full-rank covariances to generate continuous features, while categorical ones are drawn from the original samples through a generalized random forest. Empirically, contrary to SMOTE-NC, we show that MGS-GRF exhibits two important properties: (i) the coherence i.e. the ability to only generate combinations of categorical features that are already present in the original dataset and (ii) association, i.e. the ability to preserve the dependence between continuous and categorical features. We also evaluate the predictive performances of LightGBM classifiers trained on data sets, augmented with synthetic samples from various strategies. Our comparison is performed on simulated and public real-world data sets, as well as on a private data set from a leading financial institution. We observe that synthetic procedures that have the properties of coherence and association display better predictive performances in terms of various predictive metrics (PR and ROC AUC...), with MGS-GRF being the best one. Furthermore, our method exhibits promising results for the private banking application, with development pipeline being compliant with regulatory constraints.
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Authors: Alexander Koebler, Ingo Thon, Florian Buettner
Date: 2025-03-26 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

To ensure the trustworthiness and interpretability of AI systems, it is essential to align machine learning models with human domain knowledge. This can be a challenging and time-consuming endeavor that requires close communication between data scientists and domain experts. Recent leaps in the capabilities of Large Language Models (LLMs) can help alleviate this burden. In this paper, we propose a Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) which combines a data-driven black-box model with knowledge extracted from an LLM to enable domain knowledge-aligned and transparent predictions. While the introduced Mixture of Rule Experts (MoRE) steers the discovery of local rule-based surrogates during training and their utilization for the classification task, the LLM is responsible for enhancing the domain knowledge alignment of the rules by correcting and contextualizing them. Importantly, our method does not rely on access to the LLM during test time and ensures interpretability while not being prone to LLM-based confabulations. We evaluate our method on several tabular data sets and compare its performance with interpretable and non-interpretable baselines. Besides performance, we evaluate our grey-box method with respect to the utilization of interpretable rules. In addition to our quantitative evaluation, we shed light on how the LLM can provide additional context to strengthen the comprehensibility and trustworthiness of the model's reasoning process.
A Methodology to extract Geo-Referenced Standard Routes from AIS Data
Authors: Michela Corvino, Filippo Daffinà, Chiara Francalanci, Paolo Giacomazzi, Martina Magliani, Paolo Ravanelli, Torbjorn Stahl
Date: 2025-03-26 00:00:00

Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent patterns when travelling in certain maritime areas due to geographical, environmental, or economic factors. Deviations from these patterns may be attributed to weather conditions, seasonality, or illicit activities. This enables maritime surveillance authorities to analyze the navigational behavior between ports, providing insights on vessel route patterns, possibly categorized by vessel characteristics (type, flag, or size). Our methodological process begins by segmenting AIS data into distinct routes using a finite state machine (FSM), which describes routes as seg-ments connecting pairs of points of interest. The extracted segments are ag-gregated based on their departure and destination ports and then modelled using iterative density-based clustering to connect these ports. The cluster-ing parameters are assigned manually to sample and then extended to the en-tire dataset using linear regression. Overall, the approach proposed in this paper is unsupervised and does not require any ground truth to be trained. The approach has been tested on data on the on a six-year AIS dataset cover-ing the Arctic region and the Europe, Middle East, North Africa areas. The total size of our dataset is 1.15 Tbytes. The approach has proved effective in extracting standard routes, with less than 5% outliers, mostly due to routes with either their departure or their destination port not included in the test areas.
Cyborg Data: Merging Human with AI Generated Training Data
Authors: Kai North, Christopher Ormerod
Date: 2025-03-26 00:00:00

Automated scoring (AS) systems used in large-scale assessment have traditionally used small statistical models that require a large quantity of hand-scored data to make accurate predictions, which can be time-consuming and costly. Generative Large Language Models are trained on many tasks and have shown impressive abilities to generalize to new tasks with little to no data. While these models require substantially more computational power to make predictions, they still require some fine-tuning to meet operational standards. Evidence suggests that these models can exceed human-human levels of agreement even when fine-tuned on small amounts of data. With this in mind, we propose a model distillation pipeline in which a large generative model, a Teacher, teaches a much smaller model, a Student. The Teacher, trained on a small subset of the training data, is used to provide scores on the remaining training data, which is then used to train the Student. We call the resulting dataset "Cyborg Data", as it combines human and machine-scored responses. Our findings show that Student models trained on "Cyborg Data" show performance comparable to training on the entire dataset, while only requiring 10% of the original hand-scored data.
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
Authors: Sida Lin, Yankai Chen, Yiyan Qi, Chenhao Ma, Bokai Cao, Yifei Zhang, Xue Liu, Jian Guo
Date: 2025-03-26 00:00:00
Tasks: Benchmarking

The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-based approaches have made significant progress in capturing the intricate and ever-evolving data patterns of modern markets. With the rapid expansion of the stock market, it presents two characteristics, i.e., stock exogeneity and volatility heterogeneity, that heighten the complexity of price forecasting. Specifically, while stock exogeneity reflects the influence of external market factors on price movements, volatility heterogeneity showcases the varying difficulty in movement forecasting against price fluctuations. In this work, we introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO). Specifically, CSPO implements an effective deep neural architecture to leverage external futures knowledge. This enriches stock embeddings with cross-market insights and thus enhances the CSPO's predictive capability. Furthermore, CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, enabling a dynamic adaptation of its optimization process to improve accuracy and robustness. Our extensive experiments, encompassing industrial evaluation and public benchmarking, highlight CSPO's superior performance over existing methods and effectiveness of all proposed modules contained therein.
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection
Authors: Alice Zhang, Chao Li
Date: 2025-03-26 00:00:00
Tasks: Anomaly Detection, Mamba, State Space Models, Style Transfer, Time Series, Time Series Forecasting

State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an \emph{Adaptive State-Space Mamba} (\textbf{ASSM}) framework for real-time sensor data anomaly detection. While state-space models have been previously employed for image processing applications (e.g., style transfer \cite{wang2024stylemamba}), our approach leverages the core idea of sequential hidden states to tackle a significantly different domain: detecting anomalies on streaming sensor data. In particular, we introduce an adaptive gating mechanism that dynamically modulates the hidden state update based on contextual and learned statistical cues. This design ensures that our model remains computationally efficient and scalable, even under rapid data arrival rates. Extensive experiments on real-world and synthetic sensor datasets demonstrate that our method achieves superior detection performance compared to existing baselines. Our approach is easily extensible to other time-series tasks that demand rapid and reliable detection capabilities.
Ignite Forecasting with SPARK: An Efficient Generative Framework for Refining LLMs in Temporal Knowledge Graph Forecasting
Authors: Gongzhu Yin, Hongli Zhang, Yi Luo, Yuchen Yang, Kun Lu, Chao Meng
Date: 2025-03-27 00:00:00

Temporal Knowledge Graph (TKG) forecasting is crucial for predicting future events using historical data. With the surge of Large Language Models (LLMs), recent studies have begun exploring their integration into TKG forecasting and achieved some success. However, they still face limitations such as limited input length, inefficient output generation, and resource-intensive refinement, which undermine their performance and practical applicability. To address these limitations, we introduce SPARK, a Sequence-level Proxy-Adapting framework for Refining LLMs in TKG forecasting. Inspired by inference-time algorithms adopted in controlling generation, SPARK offers a cost-effective, plug-and-play solution through two key innovations: (1) Beam Sequence-Level Generation, which reframes TKG forecasting as a top-K sequence-level generation task, using beam search for efficiently generating next-entity distribution in a single forward pass. (2) TKG Adapter for Refinement, which employs traditional TKG models as trainable proxy adapters to leverage global graph information and refine LLM outputs, overcoming both the input length and the resource-intensive fine-tuning problems. Experiments across diverse datasets validate SPARK's forecasting performance, robust generalization capabilities, and high efficiency. We release source codes at https://github.com/yin-gz/SPARK.
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
Authors: Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana, David Smith, Ming Ding, Thierry Rakotoarivelo, Aruna Seneviratne
Date: 2025-03-27 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Privacy Preserving

In the era of data-driven machine-learning applications, privacy concerns and the scarcity of labeled data have become paramount challenges. These challenges are particularly pronounced in the domain of few-shot learning, where the ability to learn from limited labeled data is crucial. Privacy-preserving few-shot learning algorithms have emerged as a promising solution to address such pronounced challenges. However, it is well-known that privacy-preserving techniques often lead to a drop in utility due to the fundamental trade-off between data privacy and model performance. To enhance the utility of privacy-preserving few-shot learning methods, we introduce a novel approach called Meta-Clip. This technique is specifically designed for meta-learning algorithms, including Differentially Private (DP) model-agnostic meta-learning, DP-Reptile, and DP-MetaSGD algorithms, with the objective of balancing data privacy preservation with learning capacity maximization. By dynamically adjusting clipping thresholds during the training process, our Adaptive Clipping method provides fine-grained control over the disclosure of sensitive information, mitigating overfitting on small datasets and significantly improving the generalization performance of meta-learning models. Through comprehensive experiments on diverse benchmark datasets, we demonstrate the effectiveness of our approach in minimizing utility degradation, showcasing a superior privacy-utility trade-off compared to existing privacy-preserving techniques. The adoption of Adaptive Clipping represents a substantial step forward in the field of privacy-preserving few-shot learning, empowering the development of secure and accurate models for real-world applications, especially in scenarios where there are limited data availability.