pwc-archive/papers-with-abstracts huggingface dataset:
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift
Authors: Jisheng Bai, Mou Wang, Haohe Liu, Han Yin, Yafei Jia, Siwei Huang, Yutong Du, Dongzhe Zhang, Dongyuan Shi, Woon-Seng Gan, Mark D. Plumbley, Susanto Rahardja, Bin Xiang, Jianfeng Chen
Date: 2024-02-05 00:00:00
Tasks: Acoustic Scene Classification, Scene Classification

Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an environment. One of the challenges of the ASC task is the domain shift between training and testing data. Since 2018, ASC challenges have focused on the generalization of ASC models across different recording devices. Although this task, in recent years, has achieved substantial progress in device generalization, the challenge of domain shift between different geographical regions, involving discrepancies such as time, space, culture, and language, remains insufficiently explored at present. In addition, considering the abundance of unlabeled acoustic scene data in the real world, it is important to study the possible ways to utilize these unlabelled data. Therefore, we introduce the task Semi-supervised Acoustic Scene Classification under Domain Shift in the ICME 2024 Grand Challenge. We encourage participants to innovate with semi-supervised learning techniques, aiming to develop more robust ASC models under domain shift.
Statistical Guarantees for Link Prediction using Graph Neural Networks
Authors: Alan Chung, Amin Saberi, Morgane Austern
Date: 2024-02-05 00:00:00
Tasks: Link Prediction, Prediction

This paper derives statistical guarantees for the performance of Graph Neural Networks (GNNs) in link prediction tasks on graphs generated by a graphon. We propose a linear GNN architecture (LG-GNN) that produces consistent estimators for the underlying edge probabilities. We establish a bound on the mean squared error and give guarantees on the ability of LG-GNN to detect high-probability edges. Our guarantees hold for both sparse and dense graphs. Finally, we demonstrate some of the shortcomings of the classical GCN architecture, as well as verify our results on real and synthetic datasets.
Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
Authors: Weihan Li, Chengrui Li, Yule Wang, Anqi Wu
Date: 2024-02-05 00:00:00

Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple brain regions. Two main categories are the Gaussian Process (GP) and Linear Dynamical System (LDS), each with unique strengths. The GP-based approach effectively discovers latent variables with frequency bands and communication directions. Conversely, the LDS-based approach is computationally efficient but lacks powerful expressiveness in latent representation. In this study, we merge both methodologies by creating an LDS mirroring a multi-output GP, termed Multi-Region Markovian Gaussian Process (MRM-GP). Our work establishes a connection between an LDS and a multi-output GP that explicitly models frequencies and phase delays within the latent space of neural recordings. Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
Authors: Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
Date: 2024-02-05 00:00:00
Tasks: Causal Discovery, counterfactual, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of their predictions. Several XAI models consider causal relationships to explain models by examining the input-output relationships of prediction models and the dependencies between features. The majority of these models have been based their explanations on counterfactual probabilities, assuming that the causal graph is known. However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases. Thus, this study proposed a novel XAI framework that relaxed the constraint that the causal graph is known. This framework leveraged counterfactual probabilities and additional prior information on causal structure, facilitating the integration of a causal graph estimated through causal discovery methods and a black-box classification model. Furthermore, explanatory scores were estimated based on counterfactual probabilities. Numerical experiments conducted employing artificial data confirmed the possibility of estimating the explanatory score more accurately than in the absence of a causal graph. Finally, as an application to real data, we constructed a classification model of credit ratings assigned by Shiga Bank, Shiga prefecture, Japan. We demonstrated the effectiveness of the proposed method in cases where the causal graph is unknown.
Estimation of conditional average treatment effects on distributed confidential data
Authors: Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai
Date: 2024-02-05 00:00:00
Tasks: Privacy Preserving

Estimation of conditional average treatment effects (CATEs) is an important topic in sciences. CATEs can be estimated with high accuracy if distributed data across multiple parties can be centralized. However, it is difficult to aggregate such data owing to confidential or privacy concerns. To address this issue, we proposed data collaboration double machine learning, a method that can estimate CATE models from privacy-preserving fusion data constructed from distributed data, and evaluated our method through simulations. Our contributions are summarized in the following three points. First, our method enables estimation and testing of semi-parametric CATE models without iterative communication on distributed data. Our semi-parametric CATE method enable estimation and testing that is more robust to model mis-specification than parametric methods. Second, our method enables collaborative estimation between multiple time points and different parties through the accumulation of a knowledge base. Third, our method performed equally or better than other methods in simulations using synthetic, semi-synthetic and real-world datasets.
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Authors: Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel Ramos, Roxana Rădulescu, Willem Röpke, Diederik M. Roijers
Date: 2024-02-05 00:00:00
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards. In this paper we extend this paradigm to the context of single-objective reinforcement learning (RL), and outline multiple potential benefits including the ability to perform multi-policy learning across tasks relating to uncertain objectives, risk-aware RL, discounting, and safe RL. We also examine the algorithmic implications of adopting a utility-based approach.
Counterfactual Fairness Is Not Demographic Parity, and Other Observations
Authors: Ricardo Silva
Date: 2024-02-05 00:00:00
Tasks: counterfactual, Fairness

Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care. In this short note, I examine a recent claim that counterfactual fairness is equivalent to demographic parity. The claim fails to hold up upon closer examination. I will take the opportunity to address some broader misunderstandings about counterfactual fairness.
Variational DAG Estimation via State Augmentation With Stochastic Permutations
Authors: Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane
Date: 2024-02-04 00:00:00
Tasks: Causal Discovery, Uncertainty Quantification, Variational Inference

Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery. Bayesian approaches are a promising direction for solving this task, as they allow for uncertainty quantification and deal with well-known identifiability issues. From a probabilistic inference perspective, the main challenges are (i) representing distributions over graphs that satisfy the DAG constraint and (ii) estimating a posterior over the underlying combinatorial space. We propose an approach that addresses these challenges by formulating a joint distribution on an augmented space of DAGs and permutations. We carry out posterior estimation via variational inference, where we exploit continuous relaxations of discrete distributions. We show that our approach performs competitively when compared with a wide range of Bayesian and non-Bayesian benchmarks on a range of synthetic and real datasets.
$C^*$-Algebraic Machine Learning: Moving in a New Direction
Authors: Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Date: 2024-02-04 00:00:00

Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: $C^*$-algebraic ML $-$ a cross-fertilization between $C^*$-algebra and machine learning. The mathematical concept of $C^*$-algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use $C^*$-algebras in machine learning, and provide technical considerations that go into the design of $C^*$-algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in $C^*$-algebraic ML and give our thoughts for future development and applications.
Can Large Language Models Learn Independent Causal Mechanisms?
Authors: Gaël Gendron, Bao Trung Nguyen, Alex Yuxuan Peng, Michael Witbrock, Gillian Dobbie
Date: 2024-02-04 00:00:00
Tasks: Language Modelling

Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting a lack of generalisation ability. By contrast, systems such as causal models, that learn abstract variables and causal relationships, can demonstrate increased robustness against changes in the distribution. One reason for this success is the existence and use of Independent Causal Mechanisms (ICMs) representing high-level concepts that only sparsely interact. In this work, we apply two concepts from causality to learn ICMs within LLMs. We develop a new LLM architecture composed of multiple sparsely interacting language modelling modules. We show that such causal constraints can improve out-of-distribution performance on abstract and causal reasoning tasks. We also investigate the level of independence and domain specialisation and show that LLMs rely on pre-trained partially domain-invariant mechanisms resilient to fine-tuning.
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity
Authors: Eric Khiu, Hasti Toossi, David Anugraha, Jinyu Liu, Jiaxu Li, Juan Armando Parra Flores, Leandro Acros Roman, A. Seza Doğruöz, En-Shiun Annie Lee
Date: 2024-02-04 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Machine Translation

Fine-tuning and testing a multilingual large language model is expensive and challenging for low-resource languages (LRLs). While previous studies have predicted the performance of natural language processing (NLP) tasks using machine learning methods, they primarily focus on high-resource languages, overlooking LRLs and shifts across domains. Focusing on LRLs, we investigate three factors: the size of the fine-tuning corpus, the domain similarity between fine-tuning and testing corpora, and the language similarity between source and target languages. We employ classical regression models to assess how these factors impact the model's performance. Our results indicate that domain similarity has the most critical impact on predicting the performance of Machine Translation models.
Learning to Understand: Identifying Interactions via the Möbius Transform
Authors: Justin S. Kang, Yigit E. Erginbas, Landon Butler, Ramtin Pedarsani, Kannan Ramchandran
Date: 2024-02-04 00:00:00
Tasks: Learning Theory

One of the key challenges in machine learning is to find interpretable representations of learned functions. The M\"obius transform is essential for this purpose, as its coefficients correspond to unique importance scores for sets of input variables. This transform is closely related to widely used game-theoretic notions of importance like the Shapley and Bhanzaf value, but it also captures crucial higher-order interactions. Although computing the obius Transform of a function with $n$ inputs involves $2^n$ coefficients, it becomes tractable when the function is sparse and of low-degree as we show is the case for many real-world functions. Under these conditions, the complexity of the transform computation is significantly reduced. When there are $K$ non-zero coefficients, our algorithm recovers the M\"obius transform in $O(Kn)$ samples and $O(Kn^2)$ time asymptotically under certain assumptions, the first non-adaptive algorithm to do so. We also uncover a surprising connection between group testing and the M\"obius transform. For functions where all interactions involve at most $t$ inputs, we use group testing results to compute the M\"obius transform with $O(Kt\log n)$ sample complexity and $O(K\mathrm{poly}(n))$ time. A robust version of this algorithm withstands noise and maintains this complexity. This marks the first $n$ sub-linear query complexity, noise-tolerant algorithm for the M\"obius transform. In several examples, we observe that representations generated via sparse M\"obius transform are up to twice as faithful to the original function, as compared to Shaply and Banzhaf values, while using the same number of terms.
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
Authors: Chen Feng, Ziquan Liu, Zhuo Zhi, Ilija Bogunovic, Carsten Gerner-Beuerle, Miguel Rodrigues
Date: 2024-02-04 00:00:00
Tasks: Adversarial Attack, Bayesian Optimization

It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore increasingly relevant to develop capabilities to certify their performance in the presence of the most effective adversarial attacks. Our paper offers a new approach to certify the performance of machine learning models in the presence of adversarial attacks with population level risk guarantees. In particular, we introduce the notion of $(\alpha,\zeta)$-safe machine learning model. We propose a hypothesis testing procedure, based on the availability of a calibration set, to derive statistical guarantees providing that the probability of declaring that the adversarial (population) risk of a machine learning model is less than $\alpha$ (i.e. the model is safe), while the model is in fact unsafe (i.e. the model adversarial population risk is higher than $\alpha$), is less than $\zeta$. We also propose Bayesian optimization algorithms to determine efficiently whether a machine learning model is $(\alpha,\zeta)$-safe in the presence of an adversarial attack, along with statistical guarantees. We apply our framework to a range of machine learning models - including various sizes of vision Transformer (ViT) and ResNet models - impaired by a variety of adversarial attacks, such as PGDAttack, MomentumAttack, GenAttack and BanditAttack, to illustrate the operation of our approach. Importantly, we show that ViT's are generally more robust to adversarial attacks than ResNets, and large models are generally more robust than smaller models. Our approach goes beyond existing empirical adversarial risk-based certification guarantees. It formulates rigorous (and provable) performance guarantees that can be used to satisfy regulatory requirements mandating the use of state-of-the-art technical tools.
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
Authors: Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali
Date: 2024-02-04 00:00:00
Tasks: Quantization

This paper analyzes two competing rule extraction methodologies: quantization and equivalence query. We trained $3600$ RNN models, extracting $18000$ DFA with a quantization approach (k-means and SOM) and $3600$ DFA by equivalence query($L^{*}$) methods across $10$ initialization seeds. We sampled the datasets from $7$ Tomita and $4$ Dyck grammars and trained them on $4$ RNN cells: LSTM, GRU, O2RNN, and MIRNN. The observations from our experiments establish the superior performance of O2RNN and quantization-based rule extraction over others. $L^{*}$, primarily proposed for regular grammars, performs similarly to quantization methods for Tomita languages when neural networks are perfectly trained. However, for partially trained RNNs, $L^{*}$ shows instability in the number of states in DFA, e.g., for Tomita 5 and Tomita 6 languages, $L^{*}$ produced more than $100$ states. In contrast, quantization methods result in rules with number of states very close to ground truth DFA. Among RNN cells, O2RNN produces stable DFA consistently compared to other cells. For Dyck Languages, we observe that although GRU outperforms other RNNs in network performance, the DFA extracted by O2RNN has higher performance and better stability. The stability is computed as the standard deviation of accuracy on test sets on networks trained across $10$ seeds. On Dyck Languages, quantization methods outperformed $L^{*}$ with better stability in accuracy and the number of states. $L^{*}$ often showed instability in accuracy in the order of $16\% - 22\%$ for GRU and MIRNN while deviation for quantization methods varied in $5\% - 15\%$. In many instances with LSTM and GRU, DFA's extracted by $L^{*}$ even failed to beat chance accuracy ($50\%$), while those extracted by quantization method had standard deviation in the $7\%-17\%$ range. For O2RNN, both rule extraction methods had deviation in the $0.5\% - 3\%$ range.
Position bias in features
Authors: Richard Demsyn-Jones
Date: 2024-02-04 00:00:00
Tasks: Position

The purpose of modeling document relevance for search engines is to rank better in subsequent searches. Document-specific historical click-through rates can be important features in a dynamic ranking system which updates as we accumulate more sample. This paper describes the properties of several such features, and tests them in controlled experiments. Extending the inverse propensity weighting method to documents creates an unbiased estimate of document relevance. This feature can approximate relevance accurately, leading to near-optimal ranking in ideal circumstances. However, it has high variance that is increasing with respect to the degree of position bias. Furthermore, inaccurate position bias estimation leads to poor performance. Under several scenarios this feature can perform worse than biased click-through rates. This paper underscores the need for accurate position bias estimation, and is unique in suggesting simultaneous use of biased and unbiased position bias features.
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging
Authors: Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret, Martin Jaggi
Date: 2024-02-04 00:00:00

The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. We propose DenseFormer, a simple modification to the standard architecture that improves the perplexity of the model without increasing its size -- adding a few thousand parameters for large-scale models in the 100B parameters range. Our approach relies on an additional averaging step after each transformer block, which computes a weighted average of current and past representations -- we refer to this operation as Depth-Weighted-Average (DWA). The learned DWA weights exhibit coherent patterns of information flow, revealing the strong and structured reuse of activations from distant layers. Experiments demonstrate that DenseFormer is more data efficient, reaching the same perplexity of much deeper transformer models, and that for the same perplexity, these new models outperform transformer baselines in terms of memory efficiency and inference time.
Increasing Trust in Language Models through the Reuse of Verified Circuits
Authors: Philip Quirke, Clement Neo, Fazl Barez
Date: 2024-02-04 00:00:00

Language Models (LMs) are increasingly used for a wide range of prediction tasks, but their training can often neglect rare edge cases, reducing their reliability. Here, we define a stringent standard of trustworthiness whereby the task algorithm and circuit implementation must be verified, accounting for edge cases, with no known failure modes. We show that a model can be trained to meet this standard if built using mathematically and logically specified frameworks. In this paper, we fully verify an auto-regressive transformer model for n-digit integer addition. To exhibit the reusability of verified modules, we insert the trained integer addition model into a larger untrained model and train the combined model to perform both addition and subtraction. We find extensive reuse of the addition circuits for both tasks, easing verification of the more complex subtractor model. We discuss how inserting verified task modules into LMs can leverage model reuse to improve verifiability and trustworthiness of language models built using them. The reuse of verified circuits reduces the effort to verify more complex composite models which we believe to be a significant step towards safety of language models.