pwc-archive/papers-with-abstracts huggingface dataset:
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
Authors: Aleksandar Armacki, Pranay Sharma, Gauri Joshi, Dragana Bajovic, Dusan Jakovetic, Soummya Kar
Date: 2023-10-28 00:00:00
Tasks: Quantization

We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fashion, as new information is observed, without storing any additional data. To combat the heavy-tailed noise, we consider a general framework of nonlinear stochastic gradient descent (SGD), providing several strong results. First, for non-convex costs and component-wise nonlinearities, we establish a convergence rate arbitrarily close to $\mathcal{O}\left(t^{-\frac{1}{4}}\right)$, whose exponent is independent of noise and problem parameters. Second, for strongly convex costs and component-wise nonlinearities, we establish a rate arbitrarily close to $\mathcal{O}\left(t^{-\frac{1}{2}}\right)$ for the weighted average of iterates, with exponent again independent of noise and problem parameters. Finally, for strongly convex costs and a broader class of nonlinearities, we establish convergence of the last iterate, with a rate $\mathcal{O}\left(t^{-\zeta} \right)$, where $\zeta \in (0,1)$ depends on problem parameters, noise and nonlinearity. As we show analytically and numerically, $\zeta$ can be used to inform the preferred choice of nonlinearity for given problem settings. Compared to state-of-the-art, who only consider clipping, require bounded noise moments of order $\eta \in (1,2]$, and establish convergence rates whose exponents go to zero as $\eta \rightarrow 1$, we provide high-probability guarantees for a much broader class of nonlinearities and symmetric density noise, with convergence rates whose exponents are bounded away from zero, even when the noise has finite first moment only. Moreover, in the case of strongly convex functions, we demonstrate analytically and numerically that clipping is not always the optimal nonlinearity, further underlining the value of our general framework.
A Data-driven Recommendation Framework for Optimal Walker Designs
Authors: Advaith Narayanan
Date: 2023-10-28 00:00:00

The rapidly advancing fields of statistical modeling and machine learning have significantly enhanced data-driven design and optimization. This paper focuses on leveraging these design algorithms to optimize a medical walker, an integral part of gait rehabilitation and physiological therapy of the lower extremities. To achieve the desirable qualities of a walker, we train a predictive machine-learning model to identify trade-offs between performance objectives, thus enabling the use of efficient optimization algorithms. To do this, we use an Automated Machine Learning model utilizing a stacked-ensemble approach shown to outperform traditional ML models. However, training a predictive model requires vast amounts of data for accuracy. Due to limited publicly available walker designs, this paper presents a dataset of more than 5,000 parametric walker designs with performance values to assess mass, structural integrity, and stability. These performance values include displacement vectors for the given load case, stress coefficients, mass, and other physical properties. We also introduce a novel method of systematically calculating the stability index of a walker. We use MultiObjective Counterfactuals for Design (MCD), a novel genetic-based optimization algorithm, to explore the diverse 16-dimensional design space and search for high-performing designs based on numerous objectives. This paper presents potential walker designs that demonstrate up to a 30% mass reduction while increasing structural stability and integrity. This work takes a step toward the improved development of assistive mobility devices.
Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness
Authors: Boya Zhang, Weijian Luo, Zhihua Zhang
Date: 2023-10-28 00:00:00
Tasks: Adversarial Purification

Adversarial attacks can mislead neural network classifiers. The defense against adversarial attacks is important for AI safety. Adversarial purification is a family of approaches that defend adversarial attacks with suitable pre-processing. Diffusion models have been shown to be effective for adversarial purification. Despite their success, many aspects of diffusion purification still remain unexplored. In this paper, we investigate and improve upon three limiting designs of diffusion purification: the use of an improved diffusion model, advanced numerical simulation techniques, and optimal control of randomness. Based on our findings, we propose Purify++, a new diffusion purification algorithm that is now the state-of-the-art purification method against several adversarial attacks. Our work presents a systematic exploration of the limits of diffusion purification methods.
Optimization of utility-based shortfall risk: A non-asymptotic viewpoint
Authors: Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
Date: 2023-10-28 00:00:00

We consider the problems of estimation and optimization of utility-based shortfall risk (UBSR), which is a popular risk measure in finance. In the context of UBSR estimation, we derive a non-asymptotic bound on the mean-squared error of the classical sample average approximation (SAA) of UBSR. Next, in the context of UBSR optimization, we derive an expression for the UBSR gradient under a smooth parameterization. This expression is a ratio of expectations, both of which involve the UBSR. We use SAA for the numerator as well as denominator in the UBSR gradient expression to arrive at a biased gradient estimator. We derive non-asymptotic bounds on the estimation error, which show that our gradient estimator is asymptotically unbiased. We incorporate the aforementioned gradient estimator into a stochastic gradient (SG) algorithm for UBSR optimization. Finally, we derive non-asymptotic bounds that quantify the rate of convergence of our SG algorithm for UBSR optimization.
TLM: Token-Level Masking for Transformers
Authors: Yangjun Wu, Kebin Fang, Dongxiang Zhang, Han Wang, Hao Zhang, Gang Chen
Date: 2023-10-28 00:00:00
Tasks: Data-to-Text Generation, Grammatical Error Correction, Natural Language Understanding, Text Generation

Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers. In this paper, we propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting. Specifically, we devise a novel Token-Level Masking (TLM) training strategy for Transformers to regularize the connections of self-attention, which consists of two masking techniques that are effective and easy to implement. The underlying idea is to manipulate the connections between tokens in the multi-head attention via masking, where the networks are forced to exploit partial neighbors' information to produce a meaningful representation. The generality and effectiveness of TLM are thoroughly evaluated via extensive experiments on 4 diversified NLP tasks across 18 datasets, including natural language understanding benchmark GLUE, ChineseGLUE, Chinese Grammatical Error Correction, and data-to-text generation. The results indicate that TLM can consistently outperform attention dropout and DropHead, e.g., it increases by 0.5 points relative to DropHead with BERT-large on GLUE. Moreover, TLM can establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). Our code will be publicly available at https://github.com/Young1993/tlm.
Latent class analysis by regularized spectral clustering
Authors: Huan Qing
Date: 2023-10-28 00:00:00
Tasks: Clustering

The latent class model is a powerful tool for identifying latent classes within populations that share common characteristics for categorical data in social, psychological, and behavioral sciences. In this article, we propose two new algorithms to estimate a latent class model for categorical data. Our algorithms are developed by using a newly defined regularized Laplacian matrix calculated from the response matrix. We provide theoretical convergence rates of our algorithms by considering a sparsity parameter and show that our algorithms stably yield consistent latent class analysis under mild conditions. Additionally, we propose a metric to capture the strength of latent class analysis and several procedures designed based on this metric to infer how many latent classes one should use for real-world categorical data. The efficiency and accuracy of our algorithms are verified by extensive simulated experiments, and we further apply our algorithms to real-world categorical data with promising results.
On the Accuracy of Hotelling-Type Asymmetric Tensor Deflation: A Random Tensor Analysis
Authors: Mohamed El Amine Seddik, Maxime Guillaud, Alexis Decurninge, José Henrique de Morais Goulart
Date: 2023-10-28 00:00:00

This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions. Specifically, we consider a low-rank asymmetric tensor model of the form $\sum_{i=1}^r \beta_i{\mathcal{A}}_i + {\mathcal{W}}$ where $\beta_i\geq 0$ and the ${\mathcal{A}}_i$'s are unit-norm rank-one tensors such that $\left| \langle {\mathcal{A}}_i, {\mathcal{A}}_j \rangle \right| \in [0, 1]$ for $i\neq j$ and ${\mathcal{W}}$ is an additive noise term. Assuming that the dominant components are successively estimated from the noisy observation and subsequently subtracted, we leverage recent advances in random tensor theory in the regime of asymptotically large tensor dimensions to analytically characterize the estimated singular values and the alignment of estimated and true singular vectors at each step of the deflation procedure. Furthermore, this result can be used to construct estimators of the signal-to-noise ratios $\beta_i$ and the alignments between the estimated and true rank-1 signal components.
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
Authors: Shengkun Wang, Yangxiao Bai, Kaiqun Fu, Linhan Wang, Chang-Tien Lu, Taoran Ji
Date: 2023-10-28 00:00:00
Tasks: Sentiment Analysis

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Out-of-Distribution Generalization

The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of the energy, but has so far not yet demonstrated significant out-of-distribution generalization. Here we investigate another approach based on the pointwise learning of the Kohn-Sham charge-density. On a new dataset of bulk catalysts with charge densities, we show density models can generalize to new structures with combinations of elements not seen at train time, a form of combinatorial generalization. We show that over 80% of binary and ternary test cases achieve faster convergence than standard baselines in Density Functional Theory, amounting to an average reduction of 13% in the number of iterations required to reach convergence, which may be of independent interest. Our results suggest that density learning is a viable alternative, trading greater inference costs for a step towards combinatorial generalization, a key property for applications.
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose $(1+\epsilon)$-th moment is bounded for some $\epsilon\in (0,1]$. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of $\widetilde{O}(dT^{\frac{1}{1+\epsilon}})$, where $d$ is the dimension of contextual information and $T$ is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only $O(\log T)$ rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when $\epsilon=1$. Numerical experimental results confirm the merits of our algorithms.
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Authors: Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
Date: 2023-10-28 00:00:00
Proceeding: ICLR 2021 1
Tasks: AutoML, Missing Values, Time Series

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models. Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support. In particular, orchestrating a real-world project lifecycle poses challenges in engineering (i.e. hard to build), evaluation (i.e. hard to assess), and efficiency (i.e. hard to optimize). Designed to address these issues simultaneously, Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization. Our ultimate goal lies in facilitating transparent and reproducible experimentation with complex inference workflows, providing integrated pathways for (1) personalized prediction, (2) treatment-effect estimation, and (3) information acquisition. Through illustrative examples on real-world data in outpatient, general wards, and intensive-care settings, we illustrate the applicability of the pipeline paradigm on core tasks in the healthcare journey. To the best of our knowledge, Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
DySurv: dynamic deep learning model for survival analysis with conditional variational inference
Authors: Munib Mesinovic, Peter Watkinson, Tingting Zhu
Date: 2023-10-28 00:00:00
Tasks: Deep Learning, Survival Analysis, Survival Prediction, Time Series, Time-to-Event Prediction, Variational Inference

Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitudinal measurements from electronic health records to estimate the individual risk of death dynamically. DySurv directly estimates the cumulative risk incidence function without making any parametric assumptions on the underlying stochastic process of the time-to-event. We evaluate DySurv on 6 time-to-event benchmark datasets in healthcare, as well as 2 real-world intensive care unit (ICU) electronic health records (EHR) datasets extracted from the eICU Collaborative Research (eICU) and the Medical Information Mart for Intensive Care database (MIMIC-IV). DySurv outperforms other existing statistical and deep learning approaches to time-to-event analysis across concordance and other metrics. It achieves time-dependent concordance of over 60% in the eICU case. It is also over 12% more accurate and 22% more sensitive than in-use ICU scores like Acute Physiology and Chronic Health Evaluation (APACHE) and Sequential Organ Failure Assessment (SOFA) scores. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets. Our interdisciplinary framework successfully incorporates deep learning, survival analysis, and intensive care to create a novel method for time-to-event prediction from longitudinal health records. We test our method on several held-out test sets from a variety of healthcare datasets and compare it to existing in-use clinical risk scoring benchmarks.
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Anomaly Detection

We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data. The proposed algorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a data point along a low-dimensional manifold that approximates the training dataset. Then, EBM is trained to maximize the probability of recovering the original data. The training involves the generation of negative samples via MCMC, as in conventional EBM training, but from a different distribution concentrated near the manifold. The resulting near-manifold negative samples are highly informative, reflecting relevant modes of variation in data. An energy function of MPDR effectively learns accurate boundaries of the training data distribution and excels at detecting out-of-distribution samples. Experimental results show that MPDR exhibits strong performance across various anomaly detection tasks involving diverse data types, such as images, vectors, and acoustic signals.
Maximum Independent Set: Self-Training through Dynamic Programming
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Neural Network

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly constructs two smaller sub-graphs, predicts the one with the larger MIS, and then uses it in the next recursive call. To train our algorithm, we require annotated comparisons of different graphs concerning their MIS size. Annotating the comparisons with the output of our algorithm leads to a self-training process that results in more accurate self-annotation of the comparisons and vice versa. We provide numerical evidence showing the superiority of our method vs prior methods in multiple synthetic and real-world datasets.
Causal discovery in a complex industrial system: A time series benchmark
Authors: Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Date: 2023-10-28 00:00:00
Tasks: Causal Discovery, Time Series

Causal discovery outputs a causal structure, represented by a graph, from observed data. For time series data, there is a variety of methods, however, it is difficult to evaluate these on real data as realistic use cases very rarely come with a known causal graph to which output can be compared. In this paper, we present a dataset from an industrial subsystem at the European Spallation Source along with its causal graph which has been constructed from expert knowledge. This provides a testbed for causal discovery from time series observations of complex systems, and we believe this can help inform the development of causal discovery methodology.
SSL Framework for Causal Inconsistency between Structures and Representations
Authors: Hang Chen, Xinyu Yang, Keqing Du, Wenya Wang
Date: 2023-10-28 00:00:00
Tasks: Causal Discovery, Deep Learning, Philosophy, Representation Learning, Self-Supervised Learning

The cross-pollination between causal discovery and deep learning has led to increasingly extensive interactions. It results in a large number of deep learning data types (such as images, text, etc.) extending into the field of causal discovery, and a multitude of deep learning tasks have begun to utilize causal discovery to explore the internal causal structure and causal representation of data. In this paper, we first identified that a complex data type, ``Indefinite Data", has conflicts between causal relationships expressed by the causal structure and causal representation generated by deep learning models, a phenomenon referred to as causal inconsistency. We thoroughly analyzed related work to explain why only Indefinite Data exhibits causal inconsistency while other data types do not. Furthermore, to alleviate causal inconsistency, we proposed a self-supervised learning (SSL) framework based on intervention, hoping to provide more causal information from different intervention views to promote consistency between structure and representation. Extensive experiments have shown that the SSL framework enhances causal consistency and can further improve causal structure and representation learning performance. Additionally, we extended the SSL framework to three different downstream tasks and LLM instructions. The quantitative results of these applications all reflect the performance improvement brought about by causal consistency.
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Authors: Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen, Guangchun Ruan, Zhe Yang
Date: 2023-10-28 00:00:00

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box approach that combines high accuracy with transparency for wind power forecasting. Specifically, the core is to sum up the feature effects by constructing shape functions, which effectively map the intricate non-linear relationships between wind power output and input features. Furthermore, the forecasting model is enriched by incorporating interaction terms that adeptly capture interdependencies and synergies among the input features. The additive nature of the proposed glass-box approach ensures its interpretability. Simulation results show that the proposed glass-box approach effectively interprets the results of wind power forecasting from both global and instance perspectives. Besides, it outperforms most benchmark models and exhibits comparable performance to the best-performing neural networks. This dual strength of transparency and high accuracy positions the proposed glass-box approach as a compelling choice for reliable wind power forecasting.