pwc-archive/papers-with-abstracts huggingface dataset:
A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning
Authors: Pan Zhao, Yifan Cui
Date: 2023-10-14 00:00:00

Recently, there has been a surge in methodological development for the difference-in-differences (DiD) approach to evaluate causal effects. Standard methods in the literature rely on the parallel trends assumption to identify the average treatment effect on the treated. However, the parallel trends assumption may be violated in the presence of unmeasured confounding, and the average treatment effect on the treated may not be useful in learning a treatment assignment policy for the entire population. In this article, we propose a general instrumented DiD approach for learning the optimal treatment policy. Specifically, we establish identification results using a binary instrumental variable (IV) when the parallel trends assumption fails to hold. Additionally, we construct a Wald estimator, novel inverse probability weighting (IPW) estimators, and a class of semiparametric efficient and multiply robust estimators, with theoretical guarantees on consistency and asymptotic normality, even when relying on flexible machine learning algorithms for nuisance parameters estimation. Furthermore, we extend the instrumented DiD to the panel data setting. We evaluate our methods in extensive simulations and a real data application.
Efficient Link Prediction via GNN Layers Induced by Negative Sampling
Authors: Yuxin Wang, Xiannian Hu, Quan Gan, Xuanjing Huang, Xipeng Qiu, David Wipf
Date: 2023-10-14 00:00:00
Tasks: Decoder, Link Prediction, Navigate

Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each node that are later combined by a simple decoder to make predictions. While extremely efficient at inference time, model expressiveness is limited such that isomorphic nodes contributing to candidate edges may not be distinguishable, compromising accuracy. In contrast, \emph{edge-wise} methods rely on the formation of edge-specific subgraph embeddings to enrich the representation of pair-wise relationships, disambiguating isomorphic nodes to improve accuracy, but with increased model complexity. To better navigate this trade-off, we propose a novel GNN architecture whereby the \emph{forward pass} explicitly depends on \emph{both} positive (as is typical) and negative (unique to our approach) edges to inform more flexible, yet still cheap node-wise embeddings. This is achieved by recasting the embeddings themselves as minimizers of a forward-pass-specific energy function that favors separation of positive and negative samples. Notably, this energy is distinct from the actual training loss shared by most existing link prediction models, where contrastive pairs only influence the \textit{backward pass}. As demonstrated by extensive empirical evaluations, the resulting architecture retains the inference speed of node-wise models, while producing competitive accuracy with edge-wise alternatives. We released our code at https://github.com/yxzwang/SubmissionverOfYinYanGNN.
Learning In-between Imagery Dynamics via Physical Latent Spaces
Authors: Jihun Han, Yoonsang Lee, Anne Gelb
Date: 2023-10-14 00:00:00

We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps. The complex nature of image data and the lack of temporal information pose significant challenges in capturing the unique evolving patterns. Our proposed method focuses on estimating the intermediary stages of image evolution, allowing for interpretability through latent dynamics while preserving spatial correlations with the image. By incorporating a latent variable that follows a physical model expressed in partial differential equations (PDEs), our approach ensures the interpretability of the learned model and provides insight into corresponding image dynamics. We demonstrate the robustness and effectiveness of our learning framework through a series of numerical tests using geoscientific imagery data.
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
Authors: Jiecheng Lu, Xu Han, Shihao Yang
Date: 2023-10-14 00:00:00
Tasks: Time Series, Time Series Forecasting

Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships. As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate LTSF Transformers to model series-wise relationships: the characteristic differences between series are often captured incorrectly. To address this, we introduce ARM: a multivariate temporal-contextual adaptive learning method, which is an enhanced architecture specifically designed for multivariate LTSF modelling. ARM employs Adaptive Univariate Effect Learning (AUEL), Random Dropping (RD) training strategy, and Multi-kernel Local Smoothing (MKLS), to better handle individual series temporal patterns and correctly learn inter-series dependencies. ARM demonstrates superior performance on multiple benchmarks without significantly increasing computational costs compared to vanilla Transformer, thereby advancing the state-of-the-art in LTSF. ARM is also generally applicable to other LTSF architecture beyond vanilla Transformer.
Signal reconstruction using determinantal sampling
Authors: Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
Date: 2023-10-13 00:00:00
Tasks: Point Processes

We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularly relevant when the function is assumed to belong to a reproducing kernel Hilbert space (RKHS). This work proposes to combine several natural finite-dimensional approximations based two possible probability distributions of nodes. These distributions are related to determinantal point processes, and use the kernel of the RKHS to favor RKHS-adapted regularity in the random design. While previous work on determinantal sampling relied on the RKHS norm, we prove mean-square guarantees in $L^2$ norm. We show that determinantal point processes and mixtures thereof can yield fast convergence rates. Our results also shed light on how the rate changes as more smoothness is assumed, a phenomenon known as superconvergence. Besides, determinantal sampling generalizes i.i.d. sampling from the Christoffel function which is standard in the literature. More importantly, determinantal sampling guarantees the so-called instance optimality property for a smaller number of function evaluations than i.i.d. sampling.
Offline Reinforcement Learning for Optimizing Production Bidding Policies
Authors: Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov, Lingnan Gao, Zhihao Cen, Zuobing Xu, Zheqing Zhu
Date: 2023-10-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to their customers, which act on their behalf to bid for impression opportunities in real time at scale. Because these proxy agents are owned by the platform but use advertiser funds to operate, there is a strong practical need to balance reliability and explainability of the agent with optimizing power. We propose a generalizable approach to optimizing bidding policies in production environments by learning from real data using offline reinforcement learning. This approach can be used to optimize any differentiable base policy (practically, a heuristic policy based on principles which the advertiser can easily understand), and only requires data generated by the base policy itself. We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training. We demonstrate that such an architecture achieves statistically significant performance gains in both simulated and at-scale production bidding environments. Our approach does not incur additional infrastructure, safety, or explainability costs, as it directly optimizes parameters of existing production routines without replacing them with black box-style models like neural networks.
Statistical guarantees for stochastic Metropolis-Hastings
Authors: Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs
Date: 2023-10-13 00:00:00
Tasks: regression, Uncertainty Quantification

A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo methods in uncertainty quantification. By calculating acceptance probabilities on batches, a stochastic Metropolis-Hastings step saves computational costs, but reduces the effective sample size. We show that this obstacle can be avoided by a simple correction term. We study statistical properties of the resulting stationary distribution of the chain if the corrected stochastic Metropolis-Hastings approach is applied to sample from a Gibbs posterior distribution in a nonparametric regression setting. Focusing on deep neural network regression, we prove a PAC-Bayes oracle inequality which yields optimal contraction rates and we analyze the diameter and show high coverage probability of the resulting credible sets. With a numerical example in a high-dimensional parameter space, we illustrate that credible sets and contraction rates of the stochastic Metropolis-Hastings algorithm indeed behave similar to those obtained from the classical Metropolis-adjusted Langevin algorithm.
BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval
Authors: Kaixing Yang, Xukun Zhou, Xulong Tang, Ran Diao, Hongyan Liu, Jun He, Zhaoxin Fan
Date: 2023-10-16 00:00:00
Tasks: Contrastive Learning, Retrieval

Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sports. However, existing methods often suffer from unnatural generation effects or fail to fully explore the correlation between music and dance. To overcome these challenges, we propose BeatDance, a novel beat-based model-agnostic contrastive learning framework. BeatDance incorporates a Beat-Aware Music-Dance InfoExtractor, a Trans-Temporal Beat Blender, and a Beat-Enhanced Hubness Reducer to improve dance-music retrieval performance by utilizing the alignment between music beats and dance movements. We also introduce the Music-Dance (MD) dataset, a large-scale collection of over 10,000 music-dance video pairs for training and testing. Experimental results on the MD dataset demonstrate the superiority of our method over existing baselines, achieving state-of-the-art performance. The code and dataset will be made public available upon acceptance.
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation
Authors: Liangliang Chen, Hongzhan Lin, Jinshan Ma, Guang Chen
Date: 2023-10-16 00:00:00
Tasks: Contrastive Learning, Sequential Recommendation

In the sequential recommendation task, the recommender generally learns multiple embeddings from a user's historical behaviors, to catch the diverse interests of the user. Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence, which may fail to capture the user's inherent preference for the potential interests generalization and unavoidably make the recommended items homogeneous with the historical behaviors. In this paper, we propose a novel Dual-Scale Interest Extraction framework (DSIE) to precisely estimate the user's current interests. Specifically, DSIE explicitly models the user's inherent preference with contrastive learning by attending over his/her entire interaction sequence at the global scale and catches the user's diverse interests in a fine granularity at the local scale. Moreover, we develop a novel interest aggregation module to integrate the multi-interests according to the inherent preference to generate the user's current interests for the next-item prediction. Experiments conducted on three real-world benchmark datasets demonstrate that DSIE outperforms the state-of-the-art models in terms of recommendation preciseness and novelty.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Authors: Jiahao Wu, Qijiong Liu, Hengchang Hu, Wenqi Fan, Shengcai Liu, Qing Li, Xiao-Ming Wu, Ke Tang
Date: 2023-10-15 00:00:00
Tasks: Dataset Condensation, Reading Comprehension

Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensation for textual CBR in this paper. The goal of dataset condensation is to synthesize a small yet informative dataset, upon which models can achieve performance comparable to those trained on large datasets. While existing condensation approaches are tailored to classification tasks for continuous data like images or embeddings, direct application of them to CBR has limitations. To bridge this gap, we investigate efficient dataset condensation for content-based recommendation. Inspired by the remarkable abilities of large language models (LLMs) in text comprehension and generation, we leverage LLMs to empower the generation of textual content during condensation. To handle the interaction data involving both users and items, we devise a dual-level condensation method: content-level and user-level. At content-level, we utilize LLMs to condense all contents of an item into a new informative title. At user-level, we design a clustering-based synthesis module, where we first utilize LLMs to extract user interests. Then, the user interests and user embeddings are incorporated to condense users and generate interactions for condensed users. Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset. Extensive empirical findings from our study, conducted on three authentic datasets, substantiate the efficacy of the proposed method. Particularly, we are able to approximate up to 97% of the original performance while reducing the dataset size by 95% (i.e., on dataset MIND).
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
Authors: Zihan Wang, Ziqi Zhao, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren
Date: 2023-10-15 00:00:00
Tasks: Data Augmentation, few-shot-ner, Few-shot NER, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Prompt Learning, Transfer Learning

Few-shot named entity recognition (NER) has shown remarkable progress in identifying entities in low-resource domains. However, few-shot NER methods still struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques. Two important challenges remain: First, augmentation is limited to the training data, resulting in minimal overlap between the generated data and OOD examples. Second, knowledge transfer is implicit and insufficient, severely hindering model generalizability and the integration of knowledge from the source domain. In this paper, we propose a framework, prompt learning with type-related features (PLTR), to address these challenges. To identify useful knowledge in the source domain and enhance knowledge transfer, PLTR automatically extracts entity type-related features (TRFs) based on mutual information criteria. To bridge the gap between training and OOD data, PLTR generates a unique prompt for each unseen example by selecting relevant TRFs. We show that PLTR achieves significant performance improvements on in-domain and cross-domain datasets. The use of PLTR facilitates model adaptation and increases representation similarities between the source and unseen domains.
Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?
Authors: Renyang Liu, Jun Zhao, Xing Chu, Yu Liang, Wei Zhou, Jing He
Date: 2023-10-15 00:00:00
Tasks: GPU

With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (Deep Neural Network)-based learned locality-sensitive hashing, called LLSH, to efficiently and flexibly map high-dimensional data to low-dimensional space. LLSH replaces the traditional LSH (Locality-sensitive Hashing) function families with parallel multi-layer neural networks, which reduces the time and memory consumption and guarantees query accuracy simultaneously. The proposed LLSH demonstrate the feasibility of replacing the hash index with learning-based neural networks and open a new door for developers to design and configure data organization more accurately to improve information-searching performance. Extensive experiments on different types of datasets show the superiority of the proposed method in query accuracy, time consumption, and memory usage.
Context-aware Session-based Recommendation with Graph Neural Networks
Authors: Zhihui Zhang, Jianxiang Yu, Xiang Li
Date: 2023-10-14 00:00:00
Tasks: Session-Based Recommendations

Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploiting cross-session contexts; 2) they learn a fixed embedding vector for each item, which lacks the flexibility to reflect the variation of user interests across sessions; 3) they generally use the one-hot encoded vector of the target item as the hard label to predict, thus failing to capture the true user preference. To solve these issues, we propose CARES, a novel context-aware session-based recommendation model with graph neural networks, which utilizes different types of contexts in sessions to capture user interests. Specifically, we first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. Further, to encode the variation of user interests, we design personalized item representations. Finally, we employ a label collaboration strategy for generating soft user preference distribution as labels. Experiments on three benchmark datasets demonstrate that CARES consistently outperforms state-of-the-art models in terms of P@20 and MRR@20. Our data and codes are publicly available at https://github.com/brilliantZhang/CARES.
Findability: A Novel Measure of Information Accessibility
Authors: Aman Sinha, Priyanshu Raj Mall, Dwaipayan Roy
Date: 2023-10-14 00:00:00
Tasks: Information Retrieval, Retrieval

The overwhelming volume of data generated and indexed by search engines poses a significant challenge in retrieving documents from the index efficiently and effectively. Even with a well-crafted query, several relevant documents often get buried among a multitude of competing documents, resulting in reduced accessibility or `findability' of the desired document. Consequently, it is crucial to develop a robust methodology for assessing this dimension of Information Retrieval (IR) system performance. While previous studies have focused on measuring document accessibility disregarding user queries and document relevance, there exists no metric to quantify the findability of a document within a given IR system without resorting to manual labor. This paper aims to address this gap by defining and deriving a metric to evaluate the findability of documents as perceived by end-users. Through experiments, we demonstrate the varying impact of different retrieval models and collections on the findability of documents. Furthermore, we establish the findability measure as an independent metric distinct from retrievability, an accessibility measure introduced in prior literature.
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Authors: Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon
Date: 2023-10-14 00:00:00
Tasks: Document Ranking

We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at \url{https://github.com/ielab/llm-rankers}.
A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
Authors: Yunyong Ko, Seongeun Ryu, Sang-Wook Kim
Date: 2023-10-13 00:00:00
Tasks: Articles, Disentanglement, News Recommendation

Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem. Although many recent works have been studied for better personalized news recommendation, the following challenges should be explored more: (C1) Comprehending manifold intents coupled within a news article, (C2) Differentiating varying post-read preferences of news articles, and (C3) Addressing the cold-start user problem. To tackle the aforementioned challenges together, in this paper, we propose a novel personalized news recommendation framework (CROWN) that employs (1) category-guided intent disentanglement for (C1), (2) consistency-based news representation for (C2), and (3) GNN-enhanced hybrid user representation for (C3). Furthermore, we incorporate a category prediction into the training process of CROWN as an auxiliary task, which provides supplementary supervisory signals to enhance intent disentanglement. Extensive experiments on two real-world datasets reveal that (1) CROWN provides consistent performance improvements over ten state-of-the-art news recommendation methods and (2) the proposed strategies significantly improve the accuracy of CROWN.