pwc-archive/papers-with-abstracts huggingface dataset:
Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints
Authors: Dan-Xuan Liu, Chao Qian
Date: 2024-06-18 00:00:00

Subset selection with cost constraints aims to select a subset from a ground set to maximize a monotone objective function without exceeding a given budget, which has various applications such as influence maximization and maximum coverage. In real-world scenarios, the budget, representing available resources, may change over time, which requires that algorithms must adapt quickly to new budgets. However, in this dynamic environment, previous algorithms either lack theoretical guarantees or require a long running time. The state-of-the-art algorithm, POMC, is a Pareto optimization approach designed for static problems, lacking consideration for dynamic problems. In this paper, we propose BPODC, enhancing POMC with biased selection and warm-up strategies tailored for dynamic environments. We focus on the ability of BPODC to leverage existing computational results while adapting to budget changes. We prove that BPODC can maintain the best known $(\alpha_f/2)(1-e^{-\alpha_f})$-approximation guarantee when the budget changes. Experiments on influence maximization and maximum coverage show that BPODC adapts more effectively and rapidly to budget changes, with a running time that is less than that of the static greedy algorithm.
Lexidate: Model Evaluation and Selection with Lexicase
Authors: Jose Guadalupe Hernandez, Anil Kumar Saini, Jason H. Moore
Date: 2024-06-17 00:00:00
Tasks: model

Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages the accuracy of a pipeline's predictions. This single value, however, may not fully describe the generalizability of the pipeline. Here, we present Lexicase-based Validation (lexidate), a method that uses multiple, independent prediction values for selection. Lexidate splits training data into a learning set and a selection set. Pipelines are trained on the learning set and make predictions on the selection set. The predictions are graded for correctness and used by lexicase selection to identify parent pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test the effectiveness of three lexidate configurations within the Tree-based Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification tasks. In one configuration, we detected no difference in the accuracy of the final model returned from TPOT2 on most tasks compared to 10-fold CV. All configurations studied here returned similar or less complex final pipelines compared to 10-fold CV.
EvIL: Evolution Strategies for Generalisable Imitation Learning
Authors: Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster
Date: 2024-06-15 00:00:00
Tasks: Behavioural cloning, continuous-control, Continuous Control, Imitation Learning

Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation but deployment in the real world). Compared to policy-centric approaches to IL like behavioural cloning, reward-centric approaches like inverse reinforcement learning (IRL) often better replicate expert behaviour in new environments. This transfer is usually performed by optimising the recovered reward under the dynamics of the target environment. However, (a) we find that modern deep IL algorithms frequently recover rewards which induce policies far weaker than the expert, even in the same environment the demonstrations were collected in. Furthermore, (b) these rewards are often quite poorly shaped, necessitating extensive environment interaction to optimise effectively. We provide simple and scalable fixes to both of these concerns. For (a), we find that reward model ensembles combined with a slightly different training objective significantly improves re-training and transfer performance. For (b), we propose a novel evolution-strategies based method EvIL to optimise for a reward-shaping term that speeds up re-training in the target environment, closing a gap left open by the classical theory of IRL. On a suite of continuous control tasks, we are able to re-train policies in target (and source) environments more interaction-efficiently than prior work.
Sisteme Hibride de Invatare Automata si Aplicatii
Authors: Eduard Hogea, Darian Onchis
Date: 2024-05-29 00:00:00
Tasks: Tensor Networks

In this paper, a deep neural network approach and a neuro-symbolic one are proposed for classification and regression. The neuro-symbolic predictive models based on Logic Tensor Networks are capable of discriminating and in the same time of explaining the characterization of bad connections, called alerts or attacks, and of normal connections. The proposed hybrid systems incorporate both the ability of deep neural networks to improve on their own through experience and the interpretability of the results provided by symbolic artificial intelligence approach. To justify the need for shifting towards hybrid systems, explanation, implementation, and comparison of the dense neural network and the neuro-symbolic network is performed in detail. For the comparison to be relevant, the same datasets were used in training and the metrics resulted have been compared. A review of the resulted metrics shows that while both methods have similar precision in their predictive models, with Logic Tensor Networks being also possible to have interactive accuracy and deductive reasoning over data. Other advantages and disadvantages such as overfitting mitigation and scalability issues are also further discussed.
Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model
Authors: Vahe Karagulyan, Mohamed Ndaoud
Date: 2024-06-18 00:00:00

We investigate the problem of center estimation in the high dimensional binary sub-Gaussian Mixture Model with Hidden Markov structure on the labels. We first study the limitations of existing results in the high dimensional setting and then propose a minimax optimal procedure for the problem of center estimation. Among other findings, we show that our procedure reaches the optimal rate that is of order $\sqrt{\delta d/n} + d/n$ instead of $\sqrt{d/n} + d/n$ where $\delta \in(0,1)$ is a dependence parameter between labels. Along the way, we also develop an adaptive variant of our procedure that is globally minimax optimal. In order to do so, we rely on a more refined and localized analysis of the estimation risk. Overall, leveraging the hidden Markovian dependence between the labels, we show that it is possible to get a strict improvement of the rates adaptively at almost no cost.
Minimax Linear Regression under the Quantile Risk
Authors: Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
Date: 2024-06-17 00:00:00
Tasks: regression

We study the problem of designing minimax procedures in linear regression under the quantile risk. We start by considering the realizable setting with independent Gaussian noise, where for any given noise level and distribution of inputs, we obtain the exact minimax quantile risk for a rich family of error functions and establish the minimaxity of OLS. This improves on the known lower bounds for the special case of square error, and provides us with a lower bound on the minimax quantile risk over larger sets of distributions. Under the square error and a fourth moment assumption on the distribution of inputs, we show that this lower bound is tight over a larger class of problems. Specifically, we prove a matching upper bound on the worst-case quantile risk of a variant of the recently proposed min-max regression procedure, thereby establishing its minimaxity, up to absolute constants. We illustrate the usefulness of our approach by extending this result to all $p$-th power error functions for $p \in (2, \infty)$. Along the way, we develop a generic analogue to the classical Bayesian method for lower bounding the minimax risk when working with the quantile risk, as well as a tight characterization of the quantiles of the smallest eigenvalue of the sample covariance matrix.
Model-Based Inference and Experimental Design for Interference Using Partial Network Data
Authors: Steven Wilkins Reeves, Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
Date: 2024-06-17 00:00:00
Tasks: Experimental Design

The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated. Interference can generically be thought of as mediated through some network structure. In many empirically relevant situations however, complete network data (required to adjust for these spillover effects) are too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g., subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies are only beginning to be explored. In this paper, we present a framework for the estimation and inference of treatment effect adjustments using partial network data through the lens of structural causal models. We also illustrate procedures to assign treatments using only partial network data, with the goal of either minimizing estimator variance or optimally seeding. We derive single network asymptotic results applicable to a variety of choices for an underlying graph model. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Authors: Chengkai Liu, Jianghao Lin, Hanzhou Liu, Jianling Wang, James Caverlee
Date: 2024-06-18 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models generally fail to achieve the three golden principles for sequential recommendation simultaneously, i.e., training efficiency, low-cost inference, and strong performance. To this end, we propose RecBLR, an Efficient Sequential Recommendation Model based on Behavior-Dependent Linear Recurrent Units to accomplish the impossible triangle of the three principles. By incorporating gating mechanisms and behavior-dependent designs into linear recurrent units, our model significantly enhances user behavior modeling and recommendation performance. Furthermore, we unlock the parallelizable training as well as inference efficiency for our model by designing a hardware-aware scanning acceleration algorithm with a customized CUDA kernel. Extensive experiments on real-world datasets with varying lengths of user behavior sequences demonstrate RecBLR's remarkable effectiveness in simultaneously achieving all three golden principles - strong recommendation performance, training efficiency, and low-cost inference, while exhibiting excellent scalability to datasets with long user interaction histories.
Predicting Award Winning Research Papers at Publication Time
Authors: Riccardo Vella, Andrea Vitaletti, Fabrizio Silvestri
Date: 2024-06-18 00:00:00

In recent years, many studies have been focusing on predicting the scientific impact of research papers. Most of these predictions are based on citations count or rely on features obtainable only from already published papers. In this study, we predict the likelihood for a research paper of winning an award only relying on information available at publication time. For each paper, we build the citation subgraph induced from its bibliography. We initially consider some features of this subgraph, such as the density and the global clustering coefficient, to make our prediction. Then, we mix this information with textual features, extracted from the abstract and the title, to obtain a more accurate final prediction. We made our experiments considering the ArnetMiner citation graph, while the ground truth on award-winning papers has been obtained from a collection of best paper awards from 32 computer science conferences. In our experiment, we obtained an encouraging F1 score of 0.694. Remarkably, The high recall and the low false negatives rate, show how the model performs very well at identifying papers that will not win an award. This behavior can help researchers in getting a first evaluation of their work at publication time. Lastly, we made some first experiments on interpretability. Our results highlight some interesting patterns both in topological and textual features.
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Authors: Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia, Zhanhui Kang, Leyu Lin
Date: 2024-06-18 00:00:00
Tasks: Denoising, Recommendation Systems

Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1) noisy multi-modal content, (2) noisy user feedback, and (3) aligning multi-modal content with user feedback. In order to tackle these challenges, we propose Denoising and Aligning Multi-modal Recommender System (DA-MRS). To mitigate multi-modal noise, DA-MRS first constructs item-item graphs determined by consistent content similarity across modalities. To denoise user feedback, DA-MRS associates the probability of observed feedback with multi-modal content and devises a denoised BPR loss. Furthermore, DA-MRS implements Alignment guided by User preference to enhance task-specific item representation and Alignment guided by graded Item relations to provide finer-grained alignment. Extensive experiments verify that DA-MRS is a plug-and-play framework and achieves significant and consistent improvements across various datasets, backbone models, and noisy scenarios.
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
Authors: Jingtong Gao, Bo Chen, Weiwen Liu, Xiangyang Li, Yichao Wang, Wanyu Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
Date: 2024-06-18 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems, Reranking

Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration of additional criteria such as diversity and fairness. Existing reranking approaches often fail to harmonize these diverse criteria effectively at the model level. Moreover, these models frequently encounter challenges with scalability and personalization due to their complexity and the varying significance of different reranking criteria in diverse scenarios. In response, we introduce a comprehensive reranking framework enhanced by LLM, designed to seamlessly integrate various reranking criteria while maintaining scalability and facilitating personalized recommendations. This framework employs a fully connected graph structure, allowing the LLM to simultaneously consider multiple aspects such as accuracy, diversity, and fairness through a coherent Chain-of-Thought (CoT) process. A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs. We validate our approach using three popular public datasets, where our framework demonstrates superior performance over existing state-of-the-art reranking models in balancing multiple criteria.
A Gradient Accumulation Method for Dense Retriever under Memory Constraint
Authors: Jaehee Kim, Yukyung Lee, Pilsung Kang
Date: 2024-06-18 00:00:00
Tasks: Information Retrieval, Retrieval

InfoNCE loss is commonly used to train dense retriever in information retrieval tasks. It is well known that a large batch is essential to stable and effective training with InfoNCE loss, which requires significant hardware resources. Due to the dependency of large batch, dense retriever has bottleneck of application and research. Recently, memory reduction methods have been broadly adopted to resolve the hardware bottleneck by decomposing forward and backward or using a memory bank. However, current methods still suffer from slow and unstable training. To address these issues, we propose Contrastive Accumulation (ContAccum), a stable and efficient memory reduction method for dense retriever trains that uses a dual memory bank structure to leverage previously generated query and passage representations. Experiments on widely used five information retrieval datasets indicate that ContAccum can surpass not only existing memory reduction methods but also high-resource scenario. Moreover, theoretical analysis and experimental results confirm that ContAccum provides more stable dual-encoder training than current memory bank utilization methods.
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval
Authors: Zizhong Li, Haopeng Zhang, Jiawei Zhang
Date: 2024-06-18 00:00:00
Tasks: Information Retrieval, Knowledge Distillation, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Recent research has explored distilling knowledge from large language models (LLMs) to optimize retriever models, especially within the retrieval-augmented generation (RAG) framework. However, most existing training methods rely on extracting supervision signals from LLMs' weights or their output probabilities, which is not only resource-intensive but also incompatible with black-box LLMs. In this paper, we introduce \textit{Intermediate Distillation}, a data-efficient knowledge distillation training scheme that treats LLMs as black boxes and distills their knowledge via an innovative LLM-ranker-retriever pipeline, solely using LLMs' ranking generation as the supervision signal. Extensive experiments demonstrate that our proposed method can significantly improve the performance of retriever models with only 1,000 training instances. Moreover, our distilled retriever model significantly boosts performance in question-answering tasks within the RAG framework, demonstrating the potential of LLMs to economically and effectively train smaller models.
Balancing Embedding Spectrum for Recommendation
Authors: Shaowen Peng, Kazunari Sugiyama, Xin Liu, Tsunenori Mine
Date: 2024-06-17 00:00:00
Tasks: Contrastive Learning, Recommendation Systems

Modern recommender systems heavily rely on high-quality representations learned from high-dimensional sparse data. While significant efforts have been invested in designing powerful algorithms for extracting user preferences, the factors contributing to good representations have remained relatively unexplored. In this work, we shed light on an issue in the existing pair-wise learning paradigm (i.e., the embedding collapse problem), that the representations tend to span a subspace of the whole embedding space, leading to a suboptimal solution and reducing the model capacity. Specifically, optimization on observed interactions is equivalent to a low pass filter causing users/items to have the same representations and resulting in a complete collapse. While negative sampling acts as a high pass filter to alleviate the collapse by balancing the embedding spectrum, its effectiveness is only limited to certain losses, which still leads to an incomplete collapse. To tackle this issue, we propose a novel method called DirectSpec, acting as a reliable all pass filter to balance the spectrum distribution of the embeddings during training, ensuring that users/items effectively span the entire embedding space. Additionally, we provide a thorough analysis of DirectSpec from a decorrelation perspective and propose an enhanced variant, DirectSpec+, which employs self-paced gradients to optimize irrelevant samples more effectively. Moreover, we establish a close connection between DirectSpec+ and uniformity, demonstrating that contrastive learning (CL) can alleviate the collapse issue by indirectly balancing the spectrum. Finally, we implement DirectSpec and DirectSpec+ on two popular recommender models: MF and LightGCN. Our experimental results demonstrate its effectiveness and efficiency over competitive baselines.
Solar Power Prediction Using Satellite Data in Different Parts of Nepal
Authors: Raj Krishna Nepal, Bibek Khanal, Vibek Ghimire, Kismat Neupane, Atul Pokharel, Kshitij Niraula, Baburam Tiwari, Nawaraj Bhattarai, Khem N. Poudyal, Nawaraj Karki, Mohan B Dangi, John Biden
Date: 2024-06-08 00:00:00
Tasks: Unity

Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest Neighbors, and deep learning models like LSTM and ANN-MLP are employed and evaluated for their performance. The results indicate high accuracy in predicting solar irradiance, with R-squared(R2) scores close to unity for both train and test datasets. The impact of parameter integration on model performance is analyzed, revealing the significance of various parameters in enhancing predictive accuracy. Each model demonstrates strong performance across all parameters, consistently achieving MAE values below 6, RMSE values under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the datasets, the model's performance is significantly affected. This exclusion leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and MBE up to |7|. Among the models, KNN displays the weakest performance, with an R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural Network (ANN) performs exceptionally well, showcasing its versatility even under sparse data parameter conditions.
Strategy-proof Selling: a Geometric Approach
Authors: Mridu Prabal Goswami
Date: 2024-06-18 00:00:00

We consider one buyer and one seller. For a bundle $(t,q)\in [0,\infty[\times [0,1]=\mathbb{Z}$, $q$ either refers to the wining probability of an object or a share of a good, and $t$ denotes the payment that the buyer makes. We define classical and restricted classical preferences of the buyer on $\mathbb{Z}$; they incorporate quasilinear, non-quasilinear, risk averse preferences with multidimensional pay-off relevant parameters. We define rich single-crossing subsets of the two classes, and characterize strategy-proof mechanisms by using monotonicity of the mechanisms and continuity of the indirect preference correspondences. We also provide a computationally tractable optimization program to compute the optimal mechanism. We do not use revenue equivalence and virtual valuations as tools in our proofs. Our proof techniques bring out the geometric interaction between the single-crossing property and the positions of bundles $(t,q)$s. Our proofs are simple and provide computationally tractable optimization program to compute the optimal mechanism. The extension of the optimization program to the $n-$ buyer environment is immediate.