pwc-archive/papers-with-abstracts huggingface dataset:
In-Context Learning with Iterative Demonstration Selection
Authors: Chengwei Qin, Aston Zhang, Chen Chen, Anirudh Dagar, Wenming Ye
Date: 2023-10-15 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, Question Answering, Sentiment Analysis, Topic Classification

Spurred by advancements in scale, large language models (LLMs) have demonstrated strong few-shot learning ability via in-context learning (ICL). However, the performance of ICL has been shown to be highly sensitive to the selection of few-shot demonstrations. Selecting the most suitable examples as context remains an ongoing challenge and an open problem. Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample while ignoring the fact that the optimal selection dimension, i.e., diversity or similarity, is task-specific. Based on how the test sample is answered, we propose Iterative Demonstration Selection (IDS) to leverage the merits of both dimensions. Using zero-shot chain-of-thought reasoning (Zero-shot-CoT), IDS iteratively selects examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. Specifically, IDS applies Zero-shot-CoT to the test sample before demonstration selection. The output reasoning path is then used to choose demonstrations that are prepended to the test sample for inference. The generated answer is followed by its corresponding reasoning path for extracting a new set of demonstrations in the next iteration. After several iterations, IDS adopts majority voting to obtain the final result. Through extensive experiments on tasks including reasoning, question answering, and topic classification, we demonstrate that IDS can consistently outperform existing ICL demonstration selection methods.
Statistical inference using machine learning and classical techniques based on accumulated local effects (ALE)
Authors: Chitu Okoli
Date: 2023-10-15 00:00:00

Accumulated Local Effects (ALE) is a model-agnostic approach for global explanations of the results of black-box machine learning (ML) algorithms. There are at least three challenges with conducting statistical inference based on ALE: ensuring the reliability of ALE analyses, especially in the context of small datasets; intuitively characterizing a variable's overall effect in ML; and making robust inferences from ML data analysis. In response, we introduce innovative tools and techniques for statistical inference using ALE, establishing bootstrapped confidence intervals tailored to dataset size and introducing ALE effect size measures that intuitively indicate effects on both the outcome variable scale and a normalized scale. Furthermore, we demonstrate how to use these tools to draw reliable statistical inferences, reflecting the flexible patterns ALE adeptly highlights, with implementations available in the 'ale' package in R. This work propels the discourse on ALE and its applicability in ML and statistical analysis forward, offering practical solutions to prevailing challenges in the field.
Federated Multi-Objective Learning
Authors: Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma
Date: 2023-10-15 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Federated Learning, Multi-Task Learning

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed nature and data privacy needs of such multi-agent multi-task learning applications. This motivates us to propose a new federated multi-objective learning (FMOL) framework with multiple clients distributively and collaboratively solving an MOO problem while keeping their training data private. Notably, our FMOL framework allows a different set of objective functions across different clients to support a wide range of applications, which advances and generalizes the MOO formulation to the federated learning paradigm for the first time. For this FMOL framework, we propose two new federated multi-objective optimization (FMOO) algorithms called federated multi-gradient descent averaging (FMGDA) and federated stochastic multi-gradient descent averaging (FSMGDA). Both algorithms allow local updates to significantly reduce communication costs, while achieving the {\em same} convergence rates as those of their algorithmic counterparts in the single-objective federated learning. Our extensive experiments also corroborate the efficacy of our proposed FMOO algorithms.
Federated Reinforcement Learning for Resource Allocation in V2X Networks
Authors: Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Date: 2023-10-15 00:00:00
Tasks: Federated Learning, reinforcement-learning, Reinforcement Learning

Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the inexact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and accelerated by an adaptive step size calculated from their second moments. The developed algorithm, PASM, is proven to be convergent under mild conditions and has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task Finetuning
Authors: Dichucheng Li, Yinghao Ma, Weixing Wei, Qiuqiang Kong, Yulun Wu, Mingjin Che, Fan Xia, Emmanouil Benetos, Wei Li
Date: 2023-10-15 00:00:00
Tasks: Instrument Playing Technique Detection, Onset Detection, Self-Supervised Learning

Instrument playing techniques (IPTs) constitute a pivotal component of musical expression. However, the development of automatic IPT detection methods suffers from limited labeled data and inherent class imbalance issues. In this paper, we propose to apply a self-supervised learning model pre-trained on large-scale unlabeled music data and finetune it on IPT detection tasks. This approach addresses data scarcity and class imbalance challenges. Recognizing the significance of pitch in capturing the nuances of IPTs and the importance of onset in locating IPT events, we investigate multi-task finetuning with pitch and onset detection as auxiliary tasks. Additionally, we apply a post-processing approach for event-level prediction, where an IPT activation initiates an event only if the onset output confirms an onset in that frame. Our method outperforms prior approaches in both frame-level and event-level metrics across multiple IPT benchmark datasets. Further experiments demonstrate the efficacy of multi-task finetuning on each IPT class.
CoCoFormer: A controllable feature-rich polyphonic music generation method
Authors: Jiuyang Zhou, Tengfei Niu, Hong Zhu, Xingping Wang
Date: 2023-10-15 00:00:00
Tasks: Diversity, Music Generation, Rhythm

This paper explores the modeling method of polyphonic music sequence. Due to the great potential of Transformer models in music generation, controllable music generation is receiving more attention. In the task of polyphonic music, current controllable generation research focuses on controlling the generation of chords, but lacks precise adjustment for the controllable generation of choral music textures. This paper proposed Condition Choir Transformer (CoCoFormer) which controls the output of the model by controlling the chord and rhythm inputs at a fine-grained level. In this paper, the self-supervised method improves the loss function and performs joint training through conditional control input and unconditional input training. In order to alleviate the lack of diversity on generated samples caused by the teacher forcing training, this paper added an adversarial training method. CoCoFormer enhances model performance with explicit and implicit inputs to chords and rhythms. In this paper, the experiments proves that CoCoFormer has reached the current better level than current models. On the premise of specifying the polyphonic music texture, the same melody can also be generated in a variety of ways.
Explaining How a Neural Network Play the Go Game and Let People Learn
Authors: Huilin Zhou, Huijie Tang, Mingjie Li, Hao Zhang, Zhenyu Liu, Quanshi Zhang
Date: 2023-10-15 00:00:00
Tasks: Game of Go

The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players. In this way, explaining the knowledge encoded by the AI model and using it to teach human players represent a promising-yet-challenging issue in explainable AI. To this end, mathematical supports are required to ensure that human players can learn accurate and verifiable knowledge, rather than specious intuitive analysis. Thus, in this paper, we extract interaction primitives between stones encoded by the value network for the Go game, so as to enable people to learn from the value network. Experiments show the effectiveness of our method.
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Authors: Simin Li, Ruixiao Xu, Jingqiao Xiu, Yuwei Zheng, Pu Feng, Yaodong Yang, Xianglong Liu
Date: 2023-10-15 00:00:00
Tasks: Multi-agent Reinforcement Learning, Off-policy evaluation, reinforcement-learning, Reinforcement Learning, Starcraft, Starcraft II

In multi-agent reinforcement learning (MARL), ensuring robustness against unpredictable or worst-case actions by allies is crucial for real-world deployment. Existing robust MARL methods either approximate or enumerate all possible threat scenarios against worst-case adversaries, leading to computational intensity and reduced robustness. In contrast, human learning efficiently acquires robust behaviors in daily life without preparing for every possible threat. Inspired by this, we frame robust MARL as an inference problem, with worst-case robustness implicitly optimized under all threat scenarios via off-policy evaluation. Within this framework, we demonstrate that Mutual Information Regularization as Robust Regularization (MIR3) during routine training is guaranteed to maximize a lower bound on robustness, without the need for adversaries. Further insights show that MIR3 acts as an information bottleneck, preventing agents from over-reacting to others and aligning policies with robust action priors. In the presence of worst-case adversaries, our MIR3 significantly surpasses baseline methods in robustness and training efficiency while maintaining cooperative performance in StarCraft II and robot swarm control. When deploying the robot swarm control algorithm in the real world, our method also outperforms the best baseline by 14.29%.
Comparative Analysis of Optimization Strategies for K-means Clustering in Big Data Contexts: A Review
Authors: Ravil Mussabayev, Rustam Mussabayev
Date: 2023-10-15 00:00:00
Tasks: Clustering

This paper presents a comparative analysis of different optimization techniques for the K-means algorithm in the context of big data. K-means is a widely used clustering algorithm, but it can suffer from scalability issues when dealing with large datasets. The paper explores different approaches to overcome these issues, including parallelization, approximation, and sampling methods. The authors evaluate the performance of various clustering techniques on a large number of benchmark datasets, comparing them according to the dominance criterion provided by the "less is more" approach (LIMA), i.e., simultaneously along the dimensions of speed, clustering quality, and simplicity. The results show that different techniques are more suitable for different types of datasets and provide insights into the trade-offs between speed and accuracy in K-means clustering for big data. Overall, the paper offers a comprehensive guide for practitioners and researchers on how to optimize K-means for big data applications.
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Authors: Xiangnan Chen, Wen Zhang, Zhen Yao, Mingyang Chen, Siliang Tang
Date: 2023-10-15 00:00:00
Tasks: Denoising, Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Link Prediction

Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative triples. In the training process of KGEs, negative sampling is essential to find high-quality negative triples since KGs only contain positive triples. Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples. However, negative triples sampled by these methods are likely to contain noise. Specifically, they ignore that non-existent triples with high scores might also be true facts due to the incompleteness of KGs, which are usually called false negative triples. To alleviate the above issue, we propose an easily pluggable denoising mixup method called DeMix, which generates high-quality triples by refining sampled negative triples in a self-supervised manner. Given a sampled unlabeled triple, DeMix firstly classifies it into a marginal pseudo-negative triple or a negative triple based on the judgment of the KGE model itself. Secondly, it selects an appropriate mixup partner for the current triple to synthesize a partially positive or a harder negative triple. Experimental results on the knowledge graph completion task show that the proposed DeMix is superior to other negative sampling techniques, ensuring corresponding KGEs a faster convergence and better link prediction results.
Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology
Authors: Naofumi Hama
Date: 2023-10-15 00:00:00
Tasks: Topological Data Analysis

Data analysis that uses the output of topological data analysis as input for machine learning algorithms has been the subject of extensive research. This approach offers a means of capturing the global structure of data. Persistent homology (PH), a common methodology within the field of TDA, has found wide-ranging applications in machine learning. One of the key reasons for the success of the PH-ML pipeline lies in the deterministic nature of feature extraction conducted through PH. The ability to achieve satisfactory levels of accuracy with relatively simple downstream machine learning models, when processing these extracted features, underlines the pipeline's superior interpretability. However, it must be noted that this interpretation has encountered issues. Specifically, it fails to accurately reflect the feasible parameter region in the data generation process, and the physical or chemical constraints that restrict this process. Against this backdrop, we explore the potential application of explainable AI methodologies to this PH-ML pipeline. We apply this approach to the specific problem of predicting gas adsorption in metal-organic frameworks and demonstrate that it can yield suggestive results. The codes to reproduce our results are available at https://github.com/naofumihama/xai_ph_ml
Worst-Case Analysis is Maximum-A-Posteriori Estimation
Authors: Hongjun Wu, Di Wang
Date: 2023-10-15 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming

The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery. This paper presents a generic, adaptive, and sound fuzzing framework, called DSE-SMC, for estimating worst-case resource usage. DSE-SMC is generic because it is black-box as long as the user provides an interface for retrieving resource-usage information on a given input; adaptive because it automatically balances between exploration and exploitation of candidate inputs; and sound because it is guaranteed to converge to the true resource-usage distribution of the analyzed program. DSE-SMC is built upon a key observation: resource accumulation in a program is isomorphic to the soft-conditioning mechanism in Bayesian probabilistic programming; thus, worst-case resource analysis is isomorphic to the maximum-a-posteriori-estimation problem of Bayesian statistics. DSE-SMC incorporates sequential Monte Carlo (SMC) -- a generic framework for Bayesian inference -- with adaptive evolutionary fuzzing algorithms, in a sound manner, i.e., DSE-SMC asymptotically converges to the posterior distribution induced by resource-usage behavior of the analyzed program. Experimental evaluation on Java applications demonstrates that DSE-SMC is significantly more effective than existing black-box fuzzing methods for worst-case analysis.
VLIS: Unimodal Language Models Guide Multimodal Language Generation
Authors: Jiwan Chung, Youngjae Yu
Date: 2023-10-15 00:00:00
Tasks: Caption Generation, Explanation Generation, Image Paragraph Captioning, Language Modeling, Language Modelling, Text Generation, Visual Question Answering (VQA), Zero-Shot Image Paragraph Captioning

Multimodal language generation, which leverages the synergy of language and vision, is a rapidly expanding field. However, existing vision-language models face challenges in tasks that require complex linguistic understanding. To address this issue, we introduce Visual-Language models as Importance Sampling weights (VLIS), a novel framework that combines the visual conditioning capability of vision-language models with the language understanding of unimodal text-only language models without further training. It extracts pointwise mutual information of each image and text from a visual-language model and uses the value as an importance sampling weight to adjust the token likelihood from a text-only model. VLIS improves vision-language models on diverse tasks, including commonsense understanding (WHOOPS, OK-VQA, and ScienceQA) and complex text generation (Concadia, Image Paragraph Captioning, and ROCStories). Our results suggest that VLIS represents a promising new direction for multimodal language generation.
MILPaC: A Novel Benchmark for Evaluating Translation of Legal Text to Indian Languages
Authors: Sayan Mahapatra, Debtanu Datta, Shubham Soni, Adrijit Goswami, Saptarshi Ghosh
Date: 2023-10-15 00:00:00
Tasks: Machine Translation, Translation

Most legal text in the Indian judiciary is written in complex English due to historical reasons. However, only a small fraction of the Indian population is comfortable in reading English. Hence legal text needs to be made available in various Indian languages, possibly by translating the available legal text from English. Though there has been a lot of research on translation to and between Indian languages, to our knowledge, there has not been much prior work on such translation in the legal domain. In this work, we construct the first high-quality legal parallel corpus containing aligned text units in English and nine Indian languages, that includes several low-resource languages. We also benchmark the performance of a wide variety of Machine Translation (MT) systems over this corpus, including commercial MT systems, open-source MT systems and Large Language Models. Through a comprehensive survey by Law practitioners, we check how satisfied they are with the translations by some of these MT systems, and how well automatic MT evaluation metrics agree with the opinions of Law practitioners.
DropMix: Better Graph Contrastive Learning with Harder Negative Samples
Authors: Yueqi Ma, Minjie Chen, Xiang Li
Date: 2023-10-15 00:00:00
Tasks: Contrastive Learning

While generating better negative samples for contrastive learning has been widely studied in the areas of CV and NLP, very few work has focused on graph-structured data. Recently, Mixup has been introduced to synthesize hard negative samples in graph contrastive learning (GCL). However, due to the unsupervised learning nature of GCL, without the help of soft labels, directly mixing representations of samples could inadvertently lead to the information loss of the original hard negative and further adversely affect the quality of the newly generated harder negative. To address the problem, in this paper, we propose a novel method DropMix to synthesize harder negative samples, which consists of two main steps. Specifically, we first select some hard negative samples by measuring their hardness from both local and global views in the graph simultaneously. After that, we mix hard negatives only on partial representation dimensions to generate harder ones and decrease the information loss caused by Mixup. We conduct extensive experiments to verify the effectiveness of DropMix on six benchmark datasets. Our results show that our method can lead to better GCL performance. Our data and codes are publicly available at https://github.com/Mayueq/DropMix-Code.
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
Authors: Boan Liu, Liang Ding, Li Shen, Keqin Peng, Yu Cao, Dazhao Cheng, DaCheng Tao
Date: 2023-10-15 00:00:00
Tasks: Diversity, Mixture-of-Experts, Question Answering

The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without significant additional computational cost. Even in the era of large-scale language models (LLMs), MoE continues to play a crucial role, as some researchers have indicated that GPT-4 adopts the MoE structure to ensure diverse inference results. However, MoE is susceptible to performance degeneracy, particularly evident in the issues of imbalance and homogeneous representation among experts. While previous studies have extensively addressed the problem of imbalance, the challenge of homogeneous representation remains unresolved. In this study, we shed light on the homogeneous representation problem, wherein experts in the MoE fail to specialize and lack diversity, leading to frustratingly high similarities in their representations (up to 99\% in a well-performed MoE model). This problem restricts the expressive power of the MoE and, we argue, contradicts its original intention. To tackle this issue, we propose a straightforward yet highly effective solution: OMoE, an orthogonal expert optimizer. Additionally, we introduce an alternating training strategy that encourages each expert to update in a direction orthogonal to the subspace spanned by other experts. Our algorithm facilitates MoE training in two key ways: firstly, it explicitly enhances representation diversity, and secondly, it implicitly fosters interaction between experts during orthogonal weights computation. Through extensive experiments, we demonstrate that our proposed optimization algorithm significantly improves the performance of fine-tuning the MoE model on the GLUE benchmark, SuperGLUE benchmark, question-answering task, and name entity recognition tasks.