pwc-archive/papers-with-abstracts huggingface dataset:
Identification and Estimation of Simultaneous Equation Models Using Higher-Order Cumulant Restrictions
Authors: Ziyu Jiang
Date: 2025-01-12 00:00:00

Identifying structural parameters in linear simultaneous equation models is a fundamental challenge in economics and related fields. Recent work leverages higher-order distributional moments, exploiting the fact that non-Gaussian data carry more structural information than the Gaussian framework. While many of these contributions still require zero-covariance assumptions for structural errors, this paper shows that such an assumption can be dispensed with. Specifically, we demonstrate that under any diagonal higher-cumulant condition, the structural parameter matrix can be identified by solving an eigenvector problem. This yields a direct identification argument and motivates a simple sample-analogue estimator that is both consistent and asymptotically normal. Moreover, when uncorrelatedness may still be plausible -- such as in vector autoregression models -- our framework offers a transparent way to test for it, all within the same higher-order orthogonality setting employed by earlier studies. Monte Carlo simulations confirm desirable finite-sample performance, and we further illustrate the method's practical value in two empirical applications.
A Survey on Algorithmic Developments in Optimal Transport Problem with Applications
Authors: Sina Moradi
Date: 2025-01-08 00:00:00
Tasks: Dynamic Time Warping

Optimal Transport (OT) has established itself as a robust framework for quantifying differences between distributions, with applications that span fields such as machine learning, data science, and computer vision. This paper offers a detailed examination of the OT problem, beginning with its theoretical foundations, including the classical formulations of Monge and Kantorovich and their extensions to modern computational techniques. It explores cutting-edge algorithms, including Sinkhorn iterations, primal-dual strategies, and reduction-based approaches, emphasizing their efficiency and scalability in addressing high-dimensional problems. The paper also highlights emerging trends, such as integrating OT into machine learning frameworks, the development of novel problem variants, and ongoing theoretical advancements. Applications of OT are presented across a range of domains, with particular attention to its innovative application in time series data analysis via Optimal Transport Warping (OTW), a robust alternative to methods like Dynamic Time Warping. Despite the significant progress made, challenges related to scalability, robustness, and ethical considerations remain, necessitating further research. The paper underscores OT's potential to bridge theoretical depth and practical utility, fostering impactful advancements across diverse disciplines.
Multimodal semantic retrieval for product search
Authors: Dong Liu, Esther Lopez Ramos
Date: 2025-01-13 00:00:00
Tasks: Retrieval, Semantic Retrieval

Semantic retrieval (also known as dense retrieval) based on textual data has been extensively studied for both web search and product search application fields, where the relevance of a query and a potential target document is computed by their dense vector representation comparison. Product image is crucial for e-commence search interactions and is a key factor for customers at product explorations. But its impact for semantic retrieval has not been well studied yet. In this research, we build a multimodal representation for product items in e-commerece search in contrast to pure-text representation of products, and investigate the impact of such representations. The models are developed and evaluated on e-commerce datasets. We demonstrate that a multimodal representation scheme for a product can show improvement either on purchase recall or relevance accuracy in semantic retrieval. Additionally, we provide numerical analysis for exclusive matches retrieved by a multimodal semantic retrieval model versus a text-only semantic retrieval model, to demonstrate the validation of multimodal solutions.
Dataset-Agnostic Recommender Systems
Authors: Tri Kurniawan Wijaya, Edoardo D'Amico, Xinyang Shao
Date: 2025-01-13 00:00:00
Tasks: Feature Engineering, feature selection, Hyperparameter Optimization, Imputation, Missing Values, Recommendation Systems

Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and configuration. To this end, we introduce a novel paradigm: Dataset-Agnostic Recommender Systems (DAReS) that aims to enable a single codebase to autonomously adapt to various datasets without the need for fine-tuning, for a given recommender system task. Central to this approach is the Dataset Description Language (DsDL), a structured format that provides metadata about the dataset's features and labels, and allow the system to understand dataset's characteristics, allowing it to autonomously manage processes like feature selection, missing values imputation, noise removal, and hyperparameter optimization. By reducing the need for domain-specific expertise and manual adjustments, DAReS offers a more efficient and scalable solution for building recommender systems across diverse application domains. It addresses critical challenges in the field, such as reusability, reproducibility, and accessibility for non-expert users or entry-level researchers.
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Authors: Chongming Gao, Kexin Huang, Ziang Fei, Jiaju Chen, Jiawei Chen, Jianshan Sun, Shuchang Liu, Qingpeng Cai, Peng Jiang
Date: 2025-01-13 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning (RL)

Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. Current recommendation strategies grapple with two significant hurdles. Firstly, the future impacts of recommendation decisions remain obscured, rendering it impractical to evaluate them through direct optimization of immediate metrics. Secondly, conflicts often emerge between multiple objectives, like enhancing accuracy versus exploring diverse recommendations. Existing strategies, trapped in a "training, evaluation, and retraining" loop, grow more labor-intensive as objectives evolve. To address these challenges, we introduce a future-conditioned strategy for multi-objective controllable recommendations, allowing for the direct specification of future objectives and empowering the model to generate item sequences that align with these goals autoregressively. We present the Multi-Objective Controllable Decision Transformer (MocDT), an offline Reinforcement Learning (RL) model capable of autonomously learning the mapping from multiple objectives to item sequences, leveraging extensive offline data. Consequently, it can produce recommendations tailored to any specified objectives during the inference stage. Our empirical findings emphasize the controllable recommendation strategy's ability to produce item sequences according to different objectives while maintaining performance that is competitive with current recommendation strategies across various objectives.
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation
Authors: Yijin Choi, Chiehyeon Lim
Date: 2025-01-13 00:00:00
Tasks: Contrastive Learning, Disentanglement, Recommendation Systems, Sequential Recommendation

Recommender systems aim to provide personalized item recommendations by capturing user behaviors derived from their interaction history. Considering that user interactions naturally occur sequentially based on users' intents in mind, user behaviors can be interpreted as user intents. Therefore, intent-based sequential recommendations are actively studied recently to model user intents from historical interactions for a more precise user understanding beyond traditional studies that often overlook the underlying semantics behind user interactions. However, existing studies face three challenges: 1) the limited understanding of user behaviors by focusing solely on intents, 2) the lack of robustness in categorizing intents due to arbitrary fixed numbers of intent categories, and 3) the neglect of interacted items in modeling of user intents. To address these challenges, we propose Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation (IDCLRec). IDCLRec disentangles user behaviors into intents which are dynamic motivations and interests which are stable tastes of users for a comprehensive understanding of user behaviors. A causal cross-attention mechanism is used to identify consistent interests across interactions, while residual behaviors are modeled as intents by modeling their temporal dynamics through a similarity adjustment loss. In addition, without predefining the number of intent categories, an importance-weighted attention mechanism captures user-specific categorical intent considering the importance of intent for each interaction. Furthermore, we introduce item-aware contrastive learning which aligns intents that occurred the same interaction and aligns intent with item combinations occurred by the corresponding intent. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of IDCLRec.
Unveiling Temporal Trends in 19th Century Literature: An Information Retrieval Approach
Authors: Suchana Datta, Dwaipayan Roy, Derek Greene, Gerardine Meaney
Date: 2025-01-12 00:00:00
Tasks: Diversity, Information Retrieval, Retrieval

In English literature, the 19th century witnessed a significant transition in styles, themes, and genres. Consequently, the novels from this period display remarkable diversity. This paper explores these variations by examining the evolution of term usage in 19th century English novels through the lens of information retrieval. By applying a query expansion-based approach to a decade-segmented collection of fiction from the British Library, we examine how related terms vary over time. Our analysis employs multiple standard metrics including Kendall's tau, Jaccard similarity, and Jensen-Shannon divergence to assess overlaps and shifts in expanded query term sets. Our results indicate a significant degree of divergence in the related terms across decades as selected by the query expansion technique, suggesting substantial linguistic and conceptual changes throughout the 19th century novels.
Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation
Authors: Yuanna Liu, Ming Li, Mohammad Aliannejadi, Maarten de Rijke
Date: 2025-01-10 00:00:00
Tasks: Diversity, Fairness, Next-basket recommendation

In next basket recommendation (NBR) a set of items is recommended to users based on their historical basket sequences. In many domains, the recommended baskets consist of both repeat items and explore items. Some state-of-the-art NBR methods are heavily biased to recommend repeat items so as to maximize utility. The evaluation and optimization of beyond-accuracy objectives for NBR, such as item fairness and diversity, has attracted increasing attention. How can such beyond-accuracy objectives be pursued in the presence of heavy repeat bias? We find that only optimizing diversity or item fairness without considering repeat bias may cause NBR algorithms to recommend more repeat items. To solve this problem, we propose a model-agnostic repeat-bias-aware optimization algorithm to post-process the recommended results obtained from NBR methods with the objective of mitigating repeat bias when optimizing diversity or item fairness. We consider multiple variations of our optimization algorithm to cater to multiple NBR methods. Experiments on three real-world grocery shopping datasets show that the proposed algorithms can effectively improve diversity and item fairness, and mitigate repeat bias at acceptable Recall loss.
StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
Authors: Karine Karine, Benjamin M. Marlin
Date: 2024-11-01 00:00:00
Tasks: Reinforcement Learning (RL)

The use of reinforcement learning (RL) to learn policies for just-in-time adaptive interventions (JITAIs) is of significant interest in many behavioral intervention domains including improving levels of physical activity. In a messaging-based physical activity JITAI, a mobile health app is typically used to send messages to a participant to encourage engagement in physical activity. In this setting, RL methods can be used to learn what intervention options to provide to a participant in different contexts. However, deploying RL methods in real physical activity adaptive interventions comes with challenges: the cost and time constraints of real intervention studies result in limited data to learn adaptive intervention policies. Further, commonly used RL simulation environments have dynamics that are of limited relevance to physical activity adaptive interventions and thus shed little light on what RL methods may be optimal for this challenging application domain. In this paper, we introduce StepCountJITAI, an RL environment designed to foster research on RL methods that address the significant challenges of policy learning for adaptive behavioral interventions.
ML Mule: Mobile-Driven Context-Aware Collaborative Learning
Authors: Haoxiang Yu, Javier Berrocal, Christine Julien
Date: 2025-01-13 00:00:00
Tasks: object-detection, Object Detection

Artificial intelligence has been integrated into nearly every aspect of daily life, powering applications from object detection with computer vision to large language models for writing emails and compact models for use in smart homes. These machine learning models at times cater to the needs of individual users but are often detached from them, as they are typically stored and processed in centralized data centers. This centralized approach raises privacy concerns, incurs high infrastructure costs, and struggles to provide real time, personalized experiences. Federated and fully decentralized learning methods have been proposed to address these issues, but they still depend on centralized servers or face slow convergence due to communication constraints. We propose ML Mule, an approach that utilizes individual mobile devices as 'mules' to train and transport model snapshots as the mules move through physical spaces, sharing these models with the physical 'spaces' the mules inhabit. This method implicitly forms affinity groups among devices associated with users who share particular spaces, enabling collaborative model evolution and protecting users' privacy. Our approach addresses several major shortcomings of traditional, federated, and fully decentralized learning systems. ML Mule represents a new class of machine learning methods that are more robust, distributed, and personalized, bringing the field closer to realizing the original vision of intelligent, adaptive, and genuinely context-aware smart environments. Our results show that ML Mule converges faster and achieves higher model accuracy compared to other existing methods.
Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks
Authors: Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Date: 2025-01-13 00:00:00

Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper studies convolutional neural network input representations in more detail. We investigate different methods of representing scalar features in convolutional neural networks. Specifically, we compare using frequency and distance as input channels to convolutional layers or as scalar inputs to regression layers. We assess model performance using three different feature configurations and find that representing scalar features as image channels results in the strongest generalization.
Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning
Authors: Haonan Xu, Alessio Brini
Date: 2025-01-13 00:00:00
Tasks: Deep Reinforcement Learning, Management

This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets.
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Authors: Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Ziyu Liu, Ion Stoica, Florian Tramer, Chiyuan Zhang
Date: 2025-01-13 00:00:00
Tasks: Chatbot

It is now common to evaluate Large Language Models (LLMs) by having humans manually vote to evaluate model outputs, in contrast to typical benchmarks that evaluate knowledge or skill at some particular task. Chatbot Arena, the most popular benchmark of this type, ranks models by asking users to select the better response between two randomly selected models (without revealing which model was responsible for the generations). These platforms are widely trusted as a fair and accurate measure of LLM capabilities. In this paper, we show that if bot protection and other defenses are not implemented, these voting-based benchmarks are potentially vulnerable to adversarial manipulation. Specifically, we show that an attacker can alter the leaderboard (to promote their favorite model or demote competitors) at the cost of roughly a thousand votes (verified in a simulated, offline version of Chatbot Arena). Our attack consists of two steps: first, we show how an attacker can determine which model was used to generate a given reply with more than $95\%$ accuracy; and then, the attacker can use this information to consistently vote for (or against) a target model. Working with the Chatbot Arena developers, we identify, propose, and implement mitigations to improve the robustness of Chatbot Arena against adversarial manipulation, which, based on our analysis, substantially increases the cost of such attacks. Some of these defenses were present before our collaboration, such as bot protection with Cloudflare, malicious user detection, and rate limiting. Others, including reCAPTCHA and login are being integrated to strengthen the security in Chatbot Arena.
Distance Measure Based on an Embedding of the Manifold of K-Component Gaussian Mixture Models into the Manifold of Symmetric Positive Definite Matrices
Authors: Amit Vishwakarma, KS Subrahamanian Moosath
Date: 2025-01-13 00:00:00

In this paper, a distance between the Gaussian Mixture Models(GMMs) is obtained based on an embedding of the K-component Gaussian Mixture Model into the manifold of the symmetric positive definite matrices. Proof of embedding of K-component GMMs into the manifold of symmetric positive definite matrices is given and shown that it is a submanifold. Then, proved that the manifold of GMMs with the pullback of induced metric is isometric to the submanifold with the induced metric. Through this embedding we obtain a general lower bound for the Fisher-Rao metric. This lower bound is a distance measure on the manifold of GMMs and we employ it for the similarity measure of GMMs. The effectiveness of this framework is demonstrated through an experiment on standard machine learning benchmarks, achieving accuracy of 98%, 92%, and 93.33% on the UIUC, KTH-TIPS, and UMD texture recognition datasets respectively.
MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations
Authors: Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort
Date: 2025-01-13 00:00:00

Machine learning techniques in multi-view settings face significant challenges, particularly when integrating heterogeneous data, aligning feature spaces, and managing view-specific biases. These issues are prominent in neuroscience, where data from multiple subjects exposed to the same stimuli are analyzed to uncover brain activity dynamics. In magnetoencephalography (MEG), where signals are captured at the scalp level, estimating the brain's underlying sources is crucial, especially in group studies where sources are assumed to be similar for all subjects. Common methods, such as Multi-View Independent Component Analysis (MVICA), assume identical sources across subjects, but this assumption is often too restrictive due to individual variability and age-related changes. Multi-View Independent Component Analysis with Delays (MVICAD) addresses this by allowing sources to differ up to a temporal delay. However, temporal dilation effects, particularly in auditory stimuli, are common in brain dynamics, making the estimation of time delays alone insufficient. To address this, we propose Multi-View Independent Component Analysis with Delays and Dilations (MVICAD2), which allows sources to differ across subjects in both temporal delays and dilations. We present a model with identifiable sources, derive an approximation of its likelihood in closed form, and use regularization and optimization techniques to enhance performance. Through simulations, we demonstrate that MVICAD2 outperforms existing multi-view ICA methods. We further validate its effectiveness using the Cam-CAN dataset, and showing how delays and dilations are related to aging.