pwc-archive/papers-with-abstracts huggingface dataset:
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis
Authors: Md Mahfuzur Rahman Siddiquee, Teresa Wu, Baoxin Li
Date: 2021-09-29 00:00:00
Tasks: Anomaly Detection, Image-to-Image Translation, Medical Image Analysis, Translation

Automated anomaly detection in medical images can significantly reduce human effort in disease diagnosis. Owing to the complexity in modeling anomalies and the high cost of manual annotation by domain experts, a typical technique in the current literature is to employ only data from healthy subjects to derive the model for normal images and then to detect anomalies as outliers to this model. In many real applications, mixed datasets with both normal and potential abnormal images (e.g., images of patients with confirmed diseases) are abundant. This paper poses the research question of how to improve anomaly detection by using an unannotated set of mixed images of both normal and anomalous samples (in addition to a set of normal images from healthy subjects). We propose a novel one-directional image-to-image translation method named A2B-GAN, which learns to translate any images to only normal images (hence “one-directional”). This alleviates the requirement of direct cycle consistency of existing unpaired image-to-image translation methods, which is unattainable with unannotated data. Once the translation is learned, we generate a difference map for any given image by subtracting its translated output. Regions of significant responses in the difference map correspond to potential anomalies (if any). In terms of average AUC, our A2B-GAN outperforms the state-of-the-art methods by 0.1 points (approximately 16.25%) on two medical imaging datasets: COVID-19 detection and Cardiomegaly detection by utilizing an unannotated set mixed with anomalies. Our code is available for public release upon the paper decision.
Reinforcement Learning with Ex-Post Max-Min Fairness
Authors: Wang Chi Cheung, Zi Yi Ewe
Date: 2021-09-29 00:00:00
Tasks: Fairness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider reinforcement learning with vectorial rewards, where the agent receives a vector of $K\geq 2$ different types of rewards at each time step. The agent aims to maximize the minimum total reward among the $K$ reward types. Different from existing works that focus on maximizing the minimum expected total reward, i.e. \emph{ex-ante max-min fairness}, we maximize the expected minimum total reward, i.e. \emph{ex-post max-min fairness}. Through an example and numerical experiments, we show that the optimal policy for the former objective generally does not converge to optimality under the latter, even as the number of time steps $T$ grows. Our main contribution is a novel algorithm, Online-ReOpt, that achieves near-optimality under our objective, assuming an optimization oracle that returns a near-optimal policy given any scalar reward. The expected objective value under Online-ReOpt is shown to converge to the asymptotic optimum as $T$ increases. Finally, we propose offline variants to ease the burden of online computation in Online-ReOpt, and we propose generalizations from the max-min objective to concave utility maximization.
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data
Authors: Gonçalo Leote Cardoso Mestre, Ruxandra Barbulescu, Arlindo L. Oliveira, L. Miguel Silveira
Date: 2021-09-29 00:00:00
Tasks: Benchmarking, regression, Time Series, Time Series Analysis, Time Series Regression

Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking modelling and simulation techniques. In these complex neuronal collections, classical white-box modelling techniques based on intrinsic structural or behavioural information are either unable to capture the profound nonlinearities of the neuronal response to different stimuli or generate extremely complex models, which are computationally intractable. In this paper we investigate whether it is possible to generate lower complexity black-box models that can capture the system dynamics with low error using only measured or simulated input-output information. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their properties and their RMSE, as well as the complexity of the resulting models. 
We show that GRU models with a hidden layer size of 4 units are able to accurately reproduce the system's response to very different stimuli.
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers
Authors: Catherine Cang, Kourosh Hakhamaneshi, Ryan Rudes, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin
Date: 2021-09-29 00:00:00
Tasks: D4RL, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Pre-training deep neural network models using large unlabelled datasets followed by fine-tuning them on small task-specific datasets has emerged as a dominant paradigm in natural language processing (NLP) and computer vision (CV). Despite the widespread success, such a paradigm has remained atypical in reinforcement learning (RL).
In this paper, we investigate how we can leverage large reward-free (i.e. task-agnostic) offline datasets of prior interactions to pre-train agents that can then be fine-tuned using a small reward-annotated dataset. To this end, we present Pre-trained Decision Transformer (PDT), a simple yet powerful algorithm for semi-supervised Offline RL. By masking reward tokens during pre-training, the transformer learns to autoregressivley predict actions based on previous state and action context and effectively extracts behaviors present in the dataset. During fine-tuning, rewards are un-masked and the agent learns the set of skills that should be invoked for the desired behavior as per the reward function. We demonstrate the efficacy of this simple and flexible approach on tasks from the D4RL benchmark with limited reward annotations.
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning
Authors: Yutong Wang, Ke Xue, Chao Qian
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Clustering, continuous-control, Continuous Control, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) has achieved significant successes, which aims to obtain a single policy maximizing the expected cumulative rewards for a given task. However, in many real-world scenarios, e.g., navigating in complex environments and controlling robots, one may need to find a set of policies having both high rewards and diverse behaviors, which can bring better exploration and robust few-shot adaptation. Recently, some methods have been developed by using evolutionary techniques, including iterative reproduction and selection of policies. However, due to the inefficient selection mechanisms, these methods cannot fully guarantee both high quality and diversity. In this paper, we propose EDO-CS, a new Evolutionary Diversity Optimization algorithm with Clustering-based Selection. In each iteration, the policies are divided into several clusters based on their behaviors, and a high-quality policy is selected from each cluster for reproduction. EDO-CS also adaptively balances the importance between quality and diversity in the reproduction process. Experiments on various (i.e., deceptive and multi-modal) continuous control tasks, show the superior performance of EDO-CS over previous methods, i.e., EDO-CS can achieve a set of policies with both high quality and diversity efficiently while previous methods cannot.
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness
Authors: Sho Okumoto, Taiji Suzuki
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: speech-recognition, Speech Recognition

Among a wide range of success of deep learning, convolutional neural networks have been extensively utilized in several tasks such as speech recognition, image processing, and natural language processing, which require inputs with large dimensions.
Several studies have investigated function estimation capability of deep learning, but most of them have assumed that the dimensionality of the input is much smaller than the sample size. 
However, for typical data in applications such as those handled by the convolutional neural networks described above, 
the dimensionality of inputs is relatively high or even infinite. 
In this paper, we investigate the approximation and estimation errors of the (dilated) convolutional neural networks when the input is infinite dimensional. 
Although the approximation and estimation errors of neural networks are affected by the curse of dimensionality in the existing analyses for typical function spaces such as the \Holder and Besov spaces, we show that, by considering anisotropic smoothness, they can alleviate exponential dependency on the dimensionality but they only depend on the smoothness of the target functions. 
Our theoretical analysis supports the great practical success of convolutional networks.  
Furthermore, we show that the dilated convolution is advantageous when the smoothness of the target function has a sparse structure.
Neurally boosted supervised spectral clustering
Authors: Ali Parviz, Ioannis Koutis
Date: 2021-09-29 00:00:00
Tasks: Clustering, Network Embedding, Node Classification

  Network embedding methods compute geometric representations of graphs that render various prediction problems amenable to machine learning techniques. Spectral network embeddings are based on the computation of eigenvectors of a normalized graph Laplacian.   When coupled with standard classifiers, spectral embeddings yield strong baseline performance in node classification tasks. Remarkably, it has been recently shown that these `base' classifications followed by a simple `Correction and Smooth' procedure reach state-of-the-art performance on widely used benchmarks. All these recent works employ classifiers that are agnostic to the nature of the underlying embedding. We present simple neural models that leverage fundamental geometric properties of spectral embeddings and obtains significantly improved classification accuracy over commonly used standard classifiers. Our results are based on a specific variant of spectral clustering that is not well-known, but it is presently the only variant known to have analyzable theoretical properties. We provide a \texttt{PyTorch} implementation of our classifier along with code for the fast computation of spectral embeddings.
Neural Architecture Search via Ensemble-based Knowledge Distillation
Authors: Fanxin Li, Shixiong Zhao, Haowen Pi, Yuhao QING, Yichao Fu, Sen Wang, Heming Cui
Date: 2021-09-29 00:00:00
Tasks: Diversity, Knowledge Distillation, Neural Architecture Search

Neural Architecture Search (NAS) automatically searches for well-performed network architectures from a given search space. The One-shot NAS method improves the training efficiency by sharing weights among the possible architectures in the search space, but unfortunately suffers from insufficient parameterization of each architecture due to interferences from other architectures. Recent works attempt to alleviate the insufficient parameterization problem by knowledge distillation, which let the learning of all architectures (students) be guided by the knowledge (i.e., parameters) from a better-parameterized network (teacher), which can be either a pre-trained one (e.g., ResNet50) or some searched out networks with good accuracy performance up to now. 

However, all these methods fall short in providing a sufficiently outstanding teacher, as they either depend on a pre-trained network that does not fit the NAS task the best, or the selected fitting teachers are still undertrained and inaccurate. In this paper, we take the first step to propose an ensemble-based  knowledge distillation method for NAS, called EnNAS, which assembles an outstanding teacher by aggregating a set of architectures currently searched out with the most diversity (high diversity brings highly accurate ensembles); by doing so, EnNAS can deliver a high-quality knowledge distillation with outstanding teacher network (i.e., the ensemble network) all the time. Eventually, compared with existing works, on the real-world dataset ImageNet, EnNAS improved the top-1 accuracy of architectures searched out by 1.2% on average and 3.3% at most.
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Authors: Yuning You, Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Uncertainty Quantification, Variational Inference

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions. Meanwhile, another relevant but very different question remains yet open: how to model and quantify the uncertainty of an optimization algorithm (a.k.a., optimizer) itself? To close such a gap, the prerequisite is to consider the optimizers as sampled from a distribution, rather than a few prefabricated and fixed update rules. We first take the novel angle to consider the algorithmic space of optimizers, and provide definitions for the optimizer prior and likelihood, that intrinsically determine the posterior and therefore uncertainty. We then leverage the recent advance of learning to optimize (L2O) for the space parameterization, with the end-to-end training pipeline built via variational inference, referred as uncertainty-aware L2O (UA-L2O). Our study represents the first effort to recognize and quantify the uncertainty of the optimization algorithm. The extensive numerical results show that, UA-L2O achieves superior optimization performances in two out of three test functions, the loss function in data privacy attack, and the energy function in docking four out of five protein pairs, with the most accurate posterior estimation. Besides, the optimized solutions close to the real solution are of a larger population and tighter confidence intervals, demonstrating a better calibration capability. All our codes will be publicly released upon acceptance.
Active Deep Multiple Instance Learning
Authors: Hitesh Sapkota, Qi Yu
Date: 2021-09-29 00:00:00
Tasks: Multiple Instance Learning, Prediction

State-of-the-art multiple instance learning (MIL) models achieve competitive performance at the bag level. However, instance-level prediction, which is essential for many important applications, remains largely unsatisfactory. We propose a novel active deep multiple instance learning (ADMIL) model that samples a small subset of informative instances for annotation, aiming to significantly boost the instance-level prediction. A variance regularized loss function is designed to properly balance the bias and variance of instance-level predictions, aiming to effectively accommodate the highly imbalanced instance distribution in MIL and other fundamental challenges.  Instead of directly minimizing the variance regularized loss that is non-convex, we optimize a distributionally robust bag level likelihood as its convex surrogate. The robust bag likelihood provides a good approximation of the variance based MIL loss with a strong theoretical guarantee. It also automatically balances bias and variance, making it effective to identify the potentially positive instances to support active sampling. The robust bag likelihood can be naturally integrated with a deep architecture to support deep model training using mini-batches of positive-negative bag pairs. Finally, a novel P-F sampling function is developed that combines a probability vector and predicted instance scores, obtained by optimizing the robust bag likelihood. By leveraging the key MIL assumption, the sampling function can explore the most challenging bags and effectively detect their positive instances for annotation, which can significantly improve the instance-level prediction. Experiments conducted over multiple real-world datasets clearly demonstrate the state-of-the-art instance-level prediction achieved by the proposed ADMIL model.
Information-Aware Time Series Meta-Contrastive Learning
Authors: Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Haifeng Chen, Xiang Zhang
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Meta-Learning, Open-Ended Question Answering, Representation Learning, Time Series, Time Series Analysis

Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations. Unlike image and language domains where ``desired'' augmented samples can be generated with the rule of thumb guided by prefabricated human priors, the ad-hoc manual selection of time series augmentations is hindered by their diverse and human-unrecognizable temporal structures. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. In this work, we address the problem by encouraging both high fidelity and variety based upon information theory. A theoretical analysis leads to the criteria for selecting feasible data augmentations. On top of that, we employ the meta-learning mechanism and propose an information-aware approach, InfoTS, that adaptively selects optimal time series augmentations for contrastive representation learning. The meta-learner and the encoder are jointly optimized in an end-to-end manner to avoid sub-optimal solutions. Experiments on various datasets show highly competitive performance with up to 11.4%  reduction in MSE on the forecasting task and up to 2.8% relative improvement in accuracy on the classification task over the leading baselines.
Effect of Pressure for Compositionality on Language Emergence
Authors: Mihira Kasun Vithanage, Rukshan Darshana Wijesinghe, Alex Xavier, Dumindu Tissera, Sanath Jayasena, Subha Fernando
Date: 2021-09-29 00:00:00

Humans can use natural languages compositionally, where complicated concepts are expressed using expressions grounded in simpler concepts. Hence, it has been argued that compositionality increases the ability of generalization. This behavior is acquired during natural language learning. Natural languages contain a large number of compositional phrases that function as examples of how to construct compositional expressions for human learners.  However, in language emergence, neural agents do not have access to such compositional language expressions. It can be circumvent by optimizing a suitably devised metric of compositionality, which does not require supervising examples. In this paper, we present a learning environment where agents are pressured to make their emerging languages compositional by incorporating a metric of topological similarity into the loss function. We observe that when this pressure is carefully adjusted,  agents can achieve higher generalizations. The optimal level of this pressure is highly dependent on the agent architecture, input, and structure of the message space. However, we find no simple correlation between high compositionality and generalization. The advantage offered by compositional pressure is situational. We observe instances where moderately compositional languages are showing generalizing behavior to the extent of some highly compositional ones.
Spatio-temporal Disentangled representation learning for mobility prediction
Authors: Sichen Zhao, Wei Shao, Jeffrey Chan, Flora D. Salim
Date: 2021-09-29 00:00:00
Tasks: Management, Prediction, Representation Learning

Spatio-temporal (ST) prediction task like mobility forecasting is of great significance to traffic management and public safety. 
There is an increasing number of works proposed for mobility forecasting problems recently, and they typically focus on better extraction of the features from the spatial and temporal domains. Although prior works show promising results on more accurate predictions, they still suffer in characterising and separating the dynamic and static components, making it difficult to make further improvements. Disentangled representation learning separates the learnt latent representation into independent variables associated with semantic factors. It offers a better separation of the spatial and temporal features, which could improve the performance of mobility forecasting models. In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting. Our deep generative model learns a latent representation that (i) separates the temporal dynamics of the data from the spatially varying component and generates effective reconstructions; (ii) is able to achieve state-of-the-art performance across multiple spatio-temporal datasets. Moreover, we investigate the effectiveness of our method by eliminating the non-informative features from the learnt representations, and the results show that models can benefit from this operation.
Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks
Authors: Arthur da Cunha, Emanuele Natale, Laurent Viennot
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

The lottery ticket hypothesis states that a randomly-initialized neural network contains a small subnetwork such that, when trained in isolation, can compete with the performance of the original network.
Recent theoretical works proved an even stronger version: every sufficiently overparameterized (dense) neural network contains a subnetwork that, even without training, achieves comparable accuracy to the trained large network.
These works left as an open problem to extend the result to convolutional neural networks (CNNs).
In this work we provide such generalization by showing that, with high probability, it is possible to approximate any CNN by pruning a random CNN whose size is larger by a logarithmic factor.
Loss meta-learning for forecasting
Authors: Alan Collet, Antonio Bazco-Nogueras, Albert Banchs, Marco Fiore
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, regression, Time Series, Time Series Analysis

Meta-learning of loss functions for supervised learning has been used to date for classification tasks, or as a way to enable few-shot learning. In this paper, we show how a fairly simple loss meta-learning approach can substantially improve regression results. Specifically, we target forecasting of time series and explore case studies grounded on real-world data, and show that meta-learned losses can benefit the quality of the prediction both in cases that are apparently naive and in practical scenarios where the performance metric is complex, time-correlated, non-differentiable, or not known a-priori.