pwc-archive/papers-with-abstracts huggingface dataset:
What is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models
Authors: Jeongrok Yu, Seong Ug Kim, Jacob Choi, Jinho D. Choi
Date: 2024-04-09 00:00:00
Tasks: Sentence

Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based Masked Language Models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is needed more than ever. While many studies have evaluated gender bias in English MLMs, only a few works have been conducted for the task in other languages. This paper proposes a multilingual approach to estimate gender bias in MLMs from 5 languages: Chinese, English, German, Portuguese, and Spanish. Unlike previous work, our approach does not depend on parallel corpora coupled with English to detect gender bias in other languages using multilingual lexicons. Moreover, a novel model-based method is presented to generate sentence pairs for a more robust analysis of gender bias, compared to the traditional lexicon-based method. For each language, both the lexicon-based and model-based methods are applied to create two datasets respectively, which are used to evaluate gender bias in an MLM specifically trained for that language using one existing and 3 new scoring metrics. Our results show that the previous approach is data-sensitive and not stable as it does not remove contextual dependencies irrelevant to gender. In fact, the results often flip when different scoring metrics are used on the same dataset, suggesting that gender bias should be studied on a large dataset using multiple evaluation metrics for best practice.
Achieving Tight $O(4^k)$ Runtime Bounds on Jump$_k$ by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
Authors: Andre Opris, Johannes Lengler, Dirk Sudholt
Date: 2024-04-10 00:00:00
Tasks: Diversity, Evolutionary Algorithms

The JUMP$_k$ benchmark was the first problem for which crossover was proven to give a speed-up over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper bound of $O(\text{poly}(n) + 4^k/p_c)$ for the ($\mu$+1) Genetic Algorithm ($(\mu+1)$ GA), but only for unrealistically small crossover probabilities $p_c$. To this date, it remains an open problem to prove similar upper bounds for realistic $p_c$; the best known runtime bound, in terms of function evaluations, for $p_c = \Omega(1)$ is $O((n/\chi)^{k-1})$, $\chi$ a positive constant. We provide a novel approach and analyse the evolution of the population diversity, measured as sum of pairwise Hamming distances, for a variant of the $(\mu+1)$ GA on JUMP$_k$. The $(\mu+1)$-$\lambda_c$-GA creates one offspring in each generation either by applying mutation to one parent or by applying crossover $\lambda_c$ times to the same two parents (followed by mutation), to amplify the probability of creating an accepted offspring in generations with crossover. We show that population diversity in the $(\mu+1)$-$\lambda_c$-GA converges to an equilibrium of near-perfect diversity. This yields an improved time bound of $O(\mu n \log(\mu) + 4^k)$ function evaluations for a range of $k$ under the mild assumptions $p_c = O(1/k)$ and $\mu \in \Omega(kn)$. For all constant $k$, the restriction is satisfied for some $p_c = \Omega(1)$ and it implies that the expected runtime for all constant $k$ and an appropriate $\mu = \Theta(kn)$ is bounded by $O(n^2 \log n)$, irrespective of $k$. For larger $k$, the expected time of the $(\mu+1)$-$\lambda_c$-GA is $\Theta(4^k)$, which is tight for a large class of unbiased black-box algorithms and faster than the original $(\mu+1)$ GA by a factor of $\Omega(1/p_c)$. We also show that our analysis can be extended to other unitation functions such as JUMP$_{k, \delta}$ and HURDLE.
Solving the Food-Energy-Water Nexus Problem via Intelligent Optimization Algorithms
Authors: Qi Deng, Zheng Fan, Zhi Li, Xinna Pan, Qi Kang, Mengchu Zhou
Date: 2024-04-10 00:00:00
Tasks: Evolutionary Algorithms

The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread. However, the EA research community has not paid much attention to large-scale multi-objective optimization problems arising from real-world applications. Especially, Food-Energy-Water systems are intricately linked among food, energy and water that impact each other. They usually involve a huge number of decision variables and many conflicting objectives to be optimized. Solving their related optimization problems is essentially important to sustain the high-quality life of human beings. Their solution space size expands exponentially with the number of decision variables. Searching in such a vast space is challenging because of such large numbers of decision variables and objective functions. In recent years, a number of large-scale many-objectives optimization evolutionary algorithms have been proposed. In this paper, we solve a Food-Energy-Water optimization problem by using the state-of-art intelligent optimization methods and compare their performance. Our results conclude that the algorithm based on an inverse model outperforms the others. This work should be highly useful for practitioners to select the most suitable method for their particular large-scale engineering optimization problems.
Phylogeny-Informed Interaction Estimation Accelerates Co-Evolutionary Learning
Authors: Jack Garbus, Thomas Willkens, Alexander Lalejini, Jordan Pollack
Date: 2024-04-09 00:00:00
Tasks: Evolutionary Algorithms

Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in one population is defined by its interactions with many (or all) members of one or more other populations. To accelerate co-evolutionary systems, we introduce phylogeny-informed interaction estimation, which uses runtime phylogenetic analysis to estimate interaction outcomes between individuals based on how their relatives performed against each other. We test our interaction estimation method with three distinct co-evolutionary systems: two systems focused on measuring problem-solving success and one focused on measuring evolutionary open-endedness. We find that phylogeny-informed estimation can substantially reduce the computation required to solve problems, particularly at the beginning of long-term evolutionary runs. Additionally, we find that our estimation method initially jump-starts the evolution of neural complexity in our open-ended domain, but estimation-free systems eventually "catch-up" if given enough time. More broadly, continued refinements to these phylogeny-informed interaction estimation methods offers a promising path to reducing the computational cost of running co-evolutionary systems while maintaining their open-endedness.
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
Authors: C. J. Rodriguez, S. L. Thomson, T. Alderliesten, P. A. N. Bosman
Date: 2024-04-09 00:00:00
Tasks: Evolutionary Algorithms

Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes induced by surrogate models is limited, and even non-existent for multi-objective problems. This study addresses this critical gap by comparing landscapes of the true fitness function with those of surrogate models for multi-objective functions. Moreover, it does so temporally by examining landscape features at different points in time during optimisation, in the vicinity of the population at that point in time. We consider the BBOB bi-objective benchmark functions in our experiments. The results of the fitness landscape analysis reveals significant differences between true and surrogate features at different time points during optimisation. Despite these differences, the true and surrogate landscape features still show high correlations between each other. Furthermore, this study identifies which landscape features are related to search and demonstrates that both surrogate and true landscape features are capable of predicting algorithm performance. These findings indicate that temporal analysis of the landscape features may help to facilitate the design of surrogate switching approaches to improve performance in multi-objective optimisation.
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
Authors: Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale Durran, Morteza Mardani, Noah Brenowitz
Date: 2024-04-04 00:00:00

This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction.
Quati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers
Authors: Mirelle Bueno, Eduardo Seiti de Oliveira, Rodrigo Nogueira, Roberto A. Lotufo, Jayr Alencar Pereira
Date: 2024-04-10 00:00:00
Tasks: Information Retrieval, Retrieval

Despite Portuguese being one of the most spoken languages in the world, there is a lack of high-quality information retrieval datasets in that language. We present Quati, a dataset specifically designed for the Brazilian Portuguese language. It comprises a collection of queries formulated by native speakers and a curated set of documents sourced from a selection of high-quality Brazilian Portuguese websites. These websites are frequented more likely by real users compared to those randomly scraped, ensuring a more representative and relevant corpus. To label the query-document pairs, we use a state-of-the-art LLM, which shows inter-annotator agreement levels comparable to human performance in our assessments. We provide a detailed description of our annotation methodology to enable others to create similar datasets for other languages, providing a cost-effective way of creating high-quality IR datasets with an arbitrary number of labeled documents per query. Finally, we evaluate a diverse range of open-source and commercial retrievers to serve as baseline systems. Quati is publicly available at https://huggingface.co/datasets/unicamp-dl/quati and all scripts at https://github.com/unicamp-dl/quati .
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Authors: Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen
Date: 2024-04-10 00:00:00
Tasks: Passage Re-Ranking, Re-Ranking

Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise. Pairwise and listwise models allow passage interactions, which typically makes them more effective than pointwise models but less efficient and less robust to input passage order permutations. To enable efficient permutation-invariant passage interactions during re-ranking, we propose a new cross-encoder architecture with inter-passage attention: the Set-Encoder. In experiments on TREC Deep Learning and TIREx, the Set-Encoder is as effective as state-of-the-art listwise models while being more efficient and invariant to input passage order permutations. Compared to pointwise models, the Set-Encoder is particularly more effective when considering inter-passage information, such as novelty, and retains its advantageous properties compared to other listwise models. Our code is publicly available at https://github.com/webis-de/ECIR-25.
NFARec: A Negative Feedback-Aware Recommender Model
Authors: Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Dongjin Yu
Date: 2024-04-10 00:00:00
Tasks: Graph Neural Network, model, Recommendation Systems

Graph neural network (GNN)-based models have been extensively studied for recommendations, as they can extract high-order collaborative signals accurately which is required for high-quality recommender systems. However, they neglect the valuable information gained through negative feedback in two aspects: (1) different users might hold opposite feedback on the same item, which hampers optimal information propagation in GNNs, and (2) even when an item vastly deviates from users' preferences, they might still choose it and provide a negative rating. In this paper, we propose a negative feedback-aware recommender model (NFARec) that maximizes the leverage of negative feedback. To transfer information to multi-hop neighbors along an optimal path effectively, NFARec adopts a feedback-aware correlation that guides hypergraph convolutions (HGCs) to learn users' structural representations. Moreover, NFARec incorporates an auxiliary task - predicting the feedback sentiment polarity (i.e., positive or negative) of the next interaction - based on the Transformer Hawkes Process. The task is beneficial for understanding users by learning the sentiment expressed in their previous sequential feedback patterns and predicting future interactions. Extensive experiments demonstrate that NFARec outperforms competitive baselines. Our source code and data are released at https://github.com/WangXFng/NFARec.
CaDRec: Contextualized and Debiased Recommender Model
Authors: Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, Dongjin Yu
Date: 2024-04-10 00:00:00
Tasks: model

Recommender models aimed at mining users' behavioral patterns have raised great attention as one of the essential applications in daily life. Recent work on graph neural networks (GNNs) or debiasing methods has attained remarkable gains. However, they still suffer from (1) over-smoothing node embeddings caused by recursive convolutions with GNNs, and (2) the skewed distribution of interactions due to popularity and user-individual biases. This paper proposes a contextualized and debiased recommender model (CaDRec). To overcome the over-smoothing issue, we explore a novel hypergraph convolution operator that can select effective neighbors during convolution by introducing both structural context and sequential context. To tackle the skewed distribution, we propose two strategies for disentangling interactions: (1) modeling individual biases to learn unbiased item embeddings, and (2) incorporating item popularity with positional encoding. Moreover, we mathematically show that the imbalance of the gradients to update item embeddings exacerbates the popularity bias, thus adopting regularization and weighting schemes as solutions. Extensive experiments on four datasets demonstrate the superiority of the CaDRec against state-of-the-art (SOTA) methods. Our source code and data are released at https://github.com/WangXFng/CaDRec.
Milgram's experiment in the knowledge space: Individual navigation strategies
Authors: Manran Zhu, János Kertész
Date: 2024-04-09 00:00:00
Tasks: Graph Embedding, Navigate, Social Navigation

Data deluge characteristic for our times has led to information overload, posing a significant challenge to effectively finding our way through the digital landscape. Addressing this issue requires an in-depth understanding of how we navigate through the abundance of information. Previous research has discovered multiple patterns in how individuals navigate in the geographic, social, and information spaces, yet individual differences in strategies for navigation in the knowledge space has remained largely unexplored. To bridge the gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed questionnaires about their personal information. Utilizing the hierarchical structure of the English Wikipedia and a graph embedding trained on it, we identified two navigation strategies and found that there are significant individual differences in the choices of them. Older, white and female participants tend to adopt a proximity-driven strategy, while younger participants prefer a hub-driven strategy. Our study connects social navigation to knowledge navigation: individuals' differing tendencies to use geographical and occupational information about the target person to navigate in the social space can be understood as different choices between the hub-driven and proximity-driven strategies in the knowledge space.
Interpretable Subgraph Feature Extraction for Hyperlink Prediction
Authors: Peiyan Li, Liming Pan, Kai Li, Claudia Plant, Christian Böhm
Date: 2023-12-01 00:00:00
Proceeding: ICDM 2023 12
Tasks: hyperedge classification, Hyperedge Prediction, Prediction

Hyperlink prediction aims to predict interactions among multiple entries, constituting a practical yet challenging problem in the literature. While a handful of solutions have been proposed, they generally operate on the entire hypergraph. A practical subgraph-based solution not only enables better identification of localized characteristics of the central hyperedge but also alleviates scalability concerns. In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features. The rationale behind SSF is that hyperedges and non-hyperedges exhibit distinct local patterns, which can be unveiled through the assimilation of subgraph structural features. To this end, we utilize well-established structural heuristics such as walks and loops as the fundamental building blocks. We commence by extracting a subgraph encompassing each focal hyperedge, subsequently integrating an edge weakening scheme to facilitate feature extraction from the initial subgraph and its variations. The extracted feature vector is interpretable, and the designed edge weakening scheme empowers SSF with an adaptive capability to handle hypergraphs with varying densities. Lastly, a multilayer perceptron classifier is trained for prediction. Experiment results on ten real-world hypergraph networks demonstrate the effectiveness of the proposed approach.
Object Tracking Using Siamese Network-Based Reinforcement Learning
Authors: S. J. Park, S. J. Hwang, J. H. Baek
Date: 2022-06-13 00:00:00
Proceeding: IEEE Access 2022 6
Tasks: Object, Object Tracking, reinforcement-learning, Reinforcement Learning, Visual Object Tracking

Object tracking is a technique for tracking a specific object appearing in a video sequence while observing its features or changes. Recently, many algorithms showing high performance have emerged by applying the Siamese network to the object tracking field. A Siamese network is designed to learn the similarity between two images. In object tracking, the Siamese network tracks the object by finding the location most similar to the target image in the search image. Algorithms based on Siamese networks are vulnerable to partial and total occlusion of objects. In addition, since the object is tracked using only the similarity with the image obtained using the ground-truth bounding box of the first frame, if an object is missed once, then errors are accumulated, and a situation where the object drifts away from the object of interest frequently occurs. Therefore, in this paper, we propose a reinforcement learning model that can maximize the reward for tracking success after partial and total occlusion of an object. We also propose a dynamic template exchange method using a template that has been successfully tracked in a recent frame to solve the drift problem. When the proposed model is applied to the existing tracking models to evaluate the quantitative performance in representative object tracking benchmarks VOT2018 and OTB50, it is confirmed that the accuracy is improved, and the number of tracking failures decreases compared to the existing method. As a result, an accuracy of 0.618, robustness of 0.234, and expected average overlap (EAO) of 0.416 are achieved in VOT2018, and success of 0.673 and precision of 0.881 are achieved in OTB50.
On Bounds for Greedy Schemes in String Optimization based on Greedy Curvatures
Authors: Bowen Li, Brandon Van Over, Edwin K. P. Chong, Ali Pezeshki
Date: 2024-04-10 00:00:00

We consider the celebrated bound introduced by Conforti and Cornu\'ejols (1984) for greedy schemes in submodular optimization. The bound assumes a submodular function defined on a collection of sets forming a matroid and is based on greedy curvature. We show that the bound holds for a very general class of string problems that includes maximizing submodular functions over set matroids as a special case. We also derive a bound that is computable in the sense that they depend only on quantities along the greedy trajectory. We prove that our bound is superior to the greedy curvature bound of Conforti and Cornu\'ejols. In addition, our bound holds under a condition that is weaker than submodularity.
Best Response Shaping
Authors: Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron Courville
Date: 2024-04-05 00:00:00
Tasks: Deep Reinforcement Learning, Question Answering

We investigate the challenge of multi-agent deep reinforcement learning in partially competitive environments, where traditional methods struggle to foster reciprocity-based cooperation. LOLA and POLA agents learn reciprocity-based cooperative policies by differentiation through a few look-ahead optimization steps of their opponent. However, there is a key limitation in these techniques. Because they consider a few optimization steps, a learning opponent that takes many steps to optimize its return may exploit them. In response, we introduce a novel approach, Best Response Shaping (BRS), which differentiates through an opponent approximating the best response, termed the "detective." To condition the detective on the agent's policy for complex games we propose a state-aware differentiable conditioning mechanism, facilitated by a question answering (QA) method that extracts a representation of the agent based on its behaviour on specific environment states. To empirically validate our method, we showcase its enhanced performance against a Monte Carlo Tree Search (MCTS) opponent, which serves as an approximation to the best response in the Coin Game. This work expands the applicability of multi-agent RL in partially competitive environments and provides a new pathway towards achieving improved social welfare in general sum games.