pwc-archive/papers-with-abstracts huggingface dataset:
Estimating the Conformal Prediction Threshold from Noisy Labels
Authors: Coby Penso, Jacob Goldberger, Ethan Fetaya
Date: 2025-01-22 00:00:00
Tasks: Conformal Prediction, image-classification, Image Classification, Medical Image Classification, Prediction

Conformal Prediction (CP) is a method to control prediction uncertainty by producing a small prediction set, ensuring a predetermined probability that the true class lies within this set. This is commonly done by defining a score, based on the model predictions, and setting a threshold on this score using a validation set. In this study, we address the problem of CP calibration when we only have access to a validation set with noisy labels. We show how we can estimate the noise-free conformal threshold based on the noisy labeled data. Our solution is flexible and can accommodate various modeling assumptions regarding the label contamination process, without needing any information about the underlying data distribution or the internal mechanisms of the machine learning classifier. We develop a coverage guarantee for uniform noise that is effective even in tasks with a large number of classes. We dub our approach Noise-Aware Conformal Prediction (NACP) and show on several natural and medical image classification datasets, including ImageNet, that it significantly outperforms current noisy label methods and achieves results comparable to those obtained with a clean validation set.
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering
Authors: Chang Zong, Jian Wan, Siliang Tang, Lei Zhang
Date: 2025-01-22 00:00:00
Tasks: Answer Generation, Generative Question Answering, Language Modeling, Language Modelling, Question Answering, RAG

When addressing professional questions in the biomedical domain, humans typically acquire multiple pieces of information as evidence and engage in multifaceted evidence analysis to provide high-quality answers. Current LLM-based answer generation methods lack a detailed definition and learning process for evidence analysis, leading to the risk of error propagation and hallucinations while using evidence. Although increasing the parameter size of LLMs can alleviate these issues, it also presents challenges in model training and deployment with limited resources. In this study, we propose EvidenceMap, which aims to enable a tiny pre-trained language model to explicitly learn multiple aspects of biomedical evidence, including supportive evaluation, logical correlation and content summarization, thereby latently guiding a small generative model (around 3B parameters) to provide textual responses. Experimental results demonstrate that our method, fine-tuning a language model with 66M parameters, exceeds the RAG method with an 8B LLM by 19.9% and 5.7% in reference-based quality and accuracy, respectively.
A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering
Authors: Matteo Esposito, Mikel Robredo, Murali Sridharan, Guilherme Horta Travassos, Rafael Peñaloza, Valentina Lenarduzzi
Date: 2025-01-22 00:00:00

Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on SE. Objective: To analyze three decades of SE research, identify mistakes in statistical methods, and evaluate experts' ability to detect and address these issues. Methods: We conducted a literature survey of ~27,000 empirical studies, using LLMs to classify statistical methodologies as adequate or inadequate. Additionally, we selected 30 primary studies and held a workshop with 33 ESE experts to assess their ability to identify and resolve statistical issues. Results: Significant statistical issues were found in the primary studies, and experts showed limited ability to detect and correct these methodological problems, raising concerns about the broader ESE community's proficiency in this area. Conclusions. Despite our study's eventual limitations, its results shed light on recurring issues from promoting information copy-and-paste from past authors' works and the continuous publication of inadequate approaches that promote dubious results and jeopardize the spread of the correct statistical strategies among researchers. Besides, it justifies further investigation into empirical rigor in software engineering to expose these recurring issues and establish a framework for reassessing our field's foundation of statistical methodology application. Therefore, this work calls for critically rethinking and reforming data analysis in empirical software engineering, paving the way for our work soon.
Practical quantum federated learning and its experimental demonstration
Authors: Zhi-Ping Liu, Xiao-Yu Cao, Hao-Wen Liu, Xiao-Ran Sun, Yu Bao, Yu-Shuo Lu, Hua-Lei Yin, Zeng-Bing Chen
Date: 2025-01-22 00:00:00
Tasks: Federated Learning, Model Compression, Privacy Preserving

Federated learning is essential for decentralized, privacy-preserving model training in the data-driven era. Quantum-enhanced federated learning leverages quantum resources to address privacy and scalability challenges, offering security and efficiency advantages beyond classical methods. However, practical and scalable frameworks addressing privacy concerns in the quantum computing era remain undeveloped. Here, we propose a practical quantum federated learning framework on quantum networks, utilizing distributed quantum secret keys to protect local model updates and enable secure aggregation with information-theoretic security. We experimentally validate our framework on a 4-client quantum network with a scalable structure. Extensive numerical experiments on both quantum and classical datasets show that adding a quantum client significantly enhances the trained global model's ability to classify multipartite entangled and non-stabilizer quantum datasets. Simulations further demonstrate scalability to 200 clients with classical models trained on the MNIST dataset, reducing communication costs by $75\%$ through advanced model compression techniques and achieving rapid training convergence. Our work provides critical insights for building scalable, efficient, and quantum-secure machine learning systems for the coming quantum internet era.
HEPPO: Hardware-Efficient Proximal Policy Optimization -- A Universal Pipelined Architecture for Generalized Advantage Estimation
Authors: Hazem Taha, Ameer M. S. Abdelhadi
Date: 2025-01-22 00:00:00
Tasks: CPU, GPU, Quantization

This paper introduces HEPPO, an FPGA-based accelerator designed to optimize the Generalized Advantage Estimation (GAE) stage in Proximal Policy Optimization (PPO). Unlike previous approaches that focused on trajectory collection and actor-critic updates, HEPPO addresses GAE's computational demands with a parallel, pipelined architecture implemented on a single System-on-Chip (SoC). This design allows for the adaptation of various hardware accelerators tailored for different PPO phases. A key innovation is our strategic standardization technique, which combines dynamic reward standardization and block standardization for values, followed by 8-bit uniform quantization. This method stabilizes learning, enhances performance, and manages memory bottlenecks, achieving a 4x reduction in memory usage and a 1.5x increase in cumulative rewards. We propose a solution on a single SoC device with programmable logic and embedded processors, delivering throughput orders of magnitude higher than traditional CPU-GPU systems. Our single-chip solution minimizes communication latency and throughput bottlenecks, significantly boosting PPO training efficiency. Experimental results show a 30% increase in PPO speed and a substantial reduction in memory access time, underscoring HEPPO's potential for broad applicability in hardware-efficient reinforcement learning algorithms.
Growth strategies for arbitrary DAG neural architectures
Authors: Stella Douka, Manon Verbockhaven, Théo Rudkiewicz, Stéphane Rivaud, François P Landes, Sylvain Chevallier, Guillaume Charpiat
Date: 2025-01-22 00:00:00

Deep learning has shown impressive results obtained at the cost of training huge neural networks. However, the larger the architecture, the higher the computational, financial, and environmental costs during training and inference. We aim at reducing both training and inference durations. We focus on Neural Architecture Growth, which can increase the size of a small model when needed, directly during training using information from the backpropagation. We expand existing work and freely grow neural networks in the form of any Directed Acyclic Graph by reducing expressivity bottlenecks in the architecture. We explore strategies to reduce excessive computations and steer network growth toward more parameter-efficient architectures.
NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations
Authors: Myunsoo Kim, Hayeong Lee, Seong-Woong Shim, JunHo Seo, Byung-Jun Lee
Date: 2025-01-22 00:00:00

Intelligent agents are able to make decisions based on different levels of granularity and duration. Recent advances in skill learning enabled the agent to solve complex, long-horizon tasks by effectively guiding the agent in choosing appropriate skills. However, the practice of using fixed-length skills can easily result in skipping valuable decision points, which ultimately limits the potential for further exploration and faster policy learning. In this work, we propose to learn a simple and effective termination condition that identifies decision points through a state-action novelty module that leverages agent experience data. Our approach, Novelty-based Decision Point Identification (NBDI), outperforms previous baselines in complex, long-horizon tasks, and remains effective even in the presence of significant variations in the environment configurations of downstream tasks, highlighting the importance of decision point identification in skill learning.
The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories
Authors: Raj Sanjay Shah, Sashank Varma
Date: 2025-01-22 00:00:00

Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Recently, the focus has expanded to the developmental alignment of these models: identifying phases during training where improvements in model performance track improvements in children's thinking over development. However, there are many challenges to the use of PLMs as cognitive science theories, including different architectures, different training data modalities and scales, and limited model interpretability. In this paper, we distill lessons learned from treating PLMs, not as engineering artifacts but as cognitive science and developmental science models. We review assumptions used by researchers to map measures of PLM performance to measures of human performance. We identify potential pitfalls of this approach to understanding human thinking, and we end by enumerating criteria for using PLMs as credible accounts of cognition and cognitive development.
Dynamics of Toxicity in Political Podcasts
Authors: Naquee Rizwan, Nayandeep Deb, Sarthak Roy, Vishwajeet Singh Solanki, Kiran Garimella, Animesh Mukherjee
Date: 2025-01-22 00:00:00

Toxicity in digital media poses significant challenges, yet little attention has been given to its dynamics within the rapidly growing medium of podcasts. This paper addresses this gap by analyzing political podcast data to study the emergence and propagation of toxicity, focusing on conversation chains-structured reply patterns within podcast transcripts. Leveraging state-of-the-art transcription models and advanced conversational analysis techniques, we systematically examine toxic discourse in over 30 popular political podcasts in the United States. Our key contributions include: (1) creating a comprehensive dataset of transcribed and diarized political podcasts, identifying thousands of toxic instances using Google's Perspective API, (2) uncovering concerning trends where a majority of episodes contain at least one toxic instance, (3) introducing toxic conversation chains and analyzing their structural and linguistic properties, revealing characteristics such as longer durations, repetitive patterns, figurative language, and emotional cues tied to anger and annoyance, (4) identifying demand-related words like 'want', 'like', and 'know' as precursors to toxicity, and (5) developing predictive models to anticipate toxicity shifts based on annotated change points. Our findings provide critical insights into podcast toxicity and establish a foundation for future research on real-time monitoring and intervention mechanisms to foster healthier discourse in this influential medium.
Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors
Authors: Jingyang Ke, Feiyang Wu, Jiyi Wang, Jeffrey Markowitz, Anqi Wu
Date: 2025-01-22 00:00:00
Tasks: Decision Making

Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals. In natural environments, animals exhibit more complex, long-term behaviors driven by intrinsic motivations that are often unobservable. Recent works in time-varying inverse reinforcement learning (IRL) aim to capture shifting motivations in long-term, freely moving behaviors. However, a crucial challenge remains: animals make decisions based on their history, not just their current state. To address this, we introduce SWIRL (SWitching IRL), a novel framework that extends traditional IRL by incorporating time-varying, history-dependent reward functions. SWIRL models long behavioral sequences as transitions between short-term decision-making processes, each governed by a unique reward function. SWIRL incorporates biologically plausible history dependency to capture how past decisions and environmental contexts shape behavior, offering a more accurate description of animal decision-making. We apply SWIRL to simulated and real-world animal behavior datasets and show that it outperforms models lacking history dependency, both quantitatively and qualitatively. This work presents the first IRL model to incorporate history-dependent policies and rewards to advance our understanding of complex, naturalistic decision-making in animals.
Towards Robust Multi-tab Website Fingerprinting
Authors: Xinhao Deng, Xiyuan Zhao, Qilei Yin, Zhuotao Liu, Qi Li, Mingwei Xu, Ke Xu, Jianping Wu
Date: 2025-01-22 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, ARES extracts local patterns based on multi-level traffic aggregation features and utilizes the improved self-attention mechanism to analyze the correlations between these local patterns, effectively identifying websites. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that ARES achieves optimal performance in several realistic scenarios. Further, ARES remains robust even against various WF defenses.
Adaptive Data Exploitation in Deep Reinforcement Learning
Authors: Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng
Date: 2025-01-22 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Specifically, ADEPT adaptively manages the use of sampled data across different learning stages via multi-armed bandit (MAB) algorithms, optimizing data utilization while mitigating overfitting. Moreover, ADEPT can significantly reduce the computational overhead and accelerate a wide range of RL algorithms. We test ADEPT on benchmarks including Procgen, MiniGrid, and PyBullet. Extensive simulation demonstrates that ADEPT can achieve superior performance with remarkable computational efficiency, offering a practical solution to data-efficient RL. Our code is available at https://github.com/yuanmingqi/ADEPT.
Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?
Authors: Taiming Wang, Yuxia Zhang, Lin Jiang, Yi Tang, Guangjie Li, Hui Liu
Date: 2025-01-22 00:00:00
Tasks: Contrastive Learning, Retrieval

Concise and meaningful method names are crucial for program comprehension and maintenance. However, method names may become inconsistent with their corresponding implementations, causing confusion and errors. Several deep learning (DL)-based approaches have been proposed to identify such inconsistencies, with initial evaluations showing promising results. However, these evaluations typically use a balanced dataset, where the number of inconsistent and consistent names are equal. This setup, along with flawed dataset construction, leads to false positives, making reported performance less reliable in real-world scenarios, where most method names are consistent. In this paper, we present an empirical study that evaluates state-of-the-art DL-based methods for identifying inconsistent method names. We create a new benchmark by combining automatic identification from commit histories and manual developer inspections, reducing false positives. We evaluate five representative DL approaches (one retrieval-based and four generation-based) on this benchmark. Our results show that performance drops substantially when moving from the balanced dataset to the new benchmark. We further conduct quantitative and qualitative analyses to understand the strengths and weaknesses of the approaches. Retrieval-based methods perform well on simple methods and those with popular name sub-tokens but fail due to inefficient representation techniques. Generation-based methods struggle with inaccurate similarity calculations and immature name generation. Based on these findings, we propose improvements using contrastive learning and large language models (LLMs). Our study suggests that significant improvements are needed before these DL approaches can be effectively applied to real-world software systems.
GATE: Adaptive Learning with Working Memory by Information Gating in Multi-lamellar Hippocampal Formation
Authors: Yuechen Liu, Zishun Wang, Chen Qiao, Zongben Xu
Date: 2025-01-22 00:00:00

Hippocampal formation (HF) can rapidly adapt to varied environments and build flexible working memory (WM). To mirror the HF's mechanism on generalization and WM, we propose a model named Generalization and Associative Temporary Encoding (GATE), which deploys a 3-D multi-lamellar dorsoventral (DV) architecture, and learns to build up internally representation from externally driven information layer-wisely. In each lamella, regions of HF: EC3-CA1-EC5-EC3 forms a re-entrant loop that discriminately maintains information by EC3 persistent activity, and selectively readouts the retained information by CA1 neurons. CA3 and EC5 further provides gating function that controls these processes. After learning complex WM tasks, GATE forms neuron representations that align with experimental records, including splitter, lap, evidence, trace, delay-active cells, as well as conventional place cells. Crucially, DV architecture in GATE also captures information, range from detailed to abstract, which enables a rapid generalization ability when cue, environment or task changes, with learned representations inherited. GATE promises a viable framework for understanding the HF's flexible memory mechanisms and for progressively developing brain-inspired intelligent systems.
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Authors: Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, Chuning Tang, Congcong Wang, Dehao Zhang, Enming Yuan, Enzhe Lu, Fengxiang Tang, Flood Sung, Guangda Wei, Guokun Lai, Haiqing Guo, Han Zhu, Hao Ding, Hao Hu, Hao Yang, Hao Zhang, Haotian Yao, Haotian Zhao, Haoyu Lu, Haoze Li, Haozhen Yu, Hongcheng Gao, Huabin Zheng, Huan Yuan, Jia Chen, Jianhang Guo, Jianlin Su, Jianzhou Wang, Jie Zhao, Jin Zhang, Jingyuan Liu, Junjie Yan, Junyan Wu, Lidong Shi, Ling Ye, Longhui Yu, Mengnan Dong, Neo Zhang, Ningchen Ma, Qiwei Pan, Qucheng Gong, Shaowei Liu, Shengling Ma, Shupeng Wei, Sihan Cao, Siying Huang, Tao Jiang, Weihao Gao, Weimin Xiong, Weiran He, Weixiao Huang, Wenhao Wu, Wenyang He, Xianghui Wei, Xianqing Jia, Xingzhe Wu, Xinran Xu, Xinxing Zu, Xinyu Zhou, Xuehai Pan, Y. Charles, Yang Li, Yangyang Hu, Yangyang Liu, Yanru Chen, Yejie Wang, Yibo Liu, Yidao Qin, Yifeng Liu, Ying Yang, Yiping Bao, Yulun Du, Yuxin Wu, Yuzhi Wang, Zaida Zhou, Zhaoji Wang, Zhaowei Li, Zhen Zhu, Zheng Zhang, Zhexu Wang, Zhilin Yang, Zhiqi Huang, Zihao Huang, Ziyao Xu, Zonghan Yang
Date: 2025-01-22 00:00:00
Tasks: Math, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learning (RL) unlocks a new axis for the continued improvement of artificial intelligence, with the promise that large language models (LLMs) can scale their training data by learning to explore with rewards. However, prior published work has not produced competitive results. In light of this, we report on the training practice of Kimi k1.5, our latest multi-modal LLM trained with RL, including its RL training techniques, multi-modal data recipes, and infrastructure optimization. Long context scaling and improved policy optimization methods are key ingredients of our approach, which establishes a simplistic, effective RL framework without relying on more complex techniques such as Monte Carlo tree search, value functions, and process reward models. Notably, our system achieves state-of-the-art reasoning performance across multiple benchmarks and modalities -- e.g., 77.5 on AIME, 96.2 on MATH 500, 94-th percentile on Codeforces, 74.9 on MathVista -- matching OpenAI's o1. Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e.g., 60.8 on AIME, 94.6 on MATH500, 47.3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3.5 by a large margin (up to +550%).