pwc-archive/papers-with-abstracts huggingface dataset:
CARD: Certifiably Robust Machine Learning Pipeline via Domain Knowledge Integration
Authors: Jiawei Zhang, Linyi Li, Bo Li
Date: 2021-09-29 00:00:00
Tasks: BIG-bench Machine Learning

The advent of ubiquitous machine learning (ML) has led to exciting revolution in computing today. However, recent studies have shown that ML, especially deep neural networks (DNNs), are vulnerable to adversarial examples, which are able to mislead DNNs with carefully crafted stealthy perturbations. So far, many defense approaches have been proposed against such adversarial attacks, both empirically and theoretically. Though effective under certain conditions, existing empirical defenses are usually found vulnerable against new attacks; existing certified defenses are only able to certify robustness against limited perturbation radius. As current pure data-driven defenses have reached a bottleneck towards certifiably robust ML, in this paper we propose a certifiably robust ML pipeline CARD, aiming to integrate exogenous information, such as domain knowledge, as logical rules with ML models to improve the certified robustness. Intuitively, domain knowledge (e.g., cat belongs to the animal category) will prevent attacks that violate these knowledge rules, and it is also challenging to construct adaptive attacks satisfying such pre-defined logical relationships. In particular, we express the domain knowledge as first-order logic rules and embed these logic rules in a probabilistic graphical model. We then prove that such a probabilistic graphical model can be mapped to a 1-layer NN for efficient training. We conduct extensive experiments on several high-dimensional datasets and show that our proposed CARD achieves the state-of-the-art certified robustness.
Cross Domain Ensemble Distillation for Domain Generalization
Authors: kyungmoon lee, Sungyeon Kim, Suha Kwak
Date: 2021-09-29 00:00:00
Tasks: Domain Generalization, image-classification, Image Classification

For domain generalization, the task of learning a model that generalizes to unseen target domains utilizing multiple source domains, many approaches explicitly align the distribution of the domains.  However, the optimization for domain alignment has a risk of overfitting since the target domain is not available. To address the issue, this paper proposes a method for domain generalization by employing self-distillation. The proposed method aims to train a model robust to domain shift by allowing meaningful erroneous predictions in multiple domains. Specifically, our method matches the ensemble of predictive distributions of data with the same class label but different domains with each predictive distribution. We also propose a de-stylization method that standardizes feature maps of images to help produce consistent predictions. Image classification experiments on two benchmarks demonstrated that the proposed method greatly improves performance in both single-source and multi-source settings. We also show that the proposed method works effectively in person-reID experiments. In all experiments, our method significantly improves the performance.
Neural network architectures for disentangling the multimodal structure of data ensembles
Authors: M. Alex O. Vasilescu
Date: 2021-09-29 00:00:00
Tasks: Decoder

We introduce neural network architectures that model the mechanism that generates data and address the difficult problem of disentangling the multimodal structure of data ensembles. We provide (i) an autoencoder-decoder architecture that implements the $M$-mode SVD and (ii) a generalized autoencoder that employs a kernel activation and implements the doubly nonlinear Kernel-MPCA.  The neural network projection architecture decomposes an unlabeled data given an estimated forward model and a set of observations that constrain the solution set.
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization
Authors: Peng Lu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Philippe Langlais
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, Natural Language Understanding

Knowledge Distillation (KD) is an algorithm that transfers the knowledge of a trained, typically larger, neural network into another model under training. Although a complete understanding of KD is elusive, a growing body of work has shown that the success of both KD and label smoothing comes from a similar regularization effect of soft targets. In this work, we propose an instance-specific label smoothing technique, Pseudo-KD, which is efficiently learnt from the data. We devise a two-stage optimization problem that leads to a deterministic and interpretable solution for the optimal label smoothing. We show that Pseudo-KD can be equivalent to an efficient variant of self-distillation techniques, without the need to store the parameters or the output of a trained model. Finally, we conduct experiments on multiple image classification (CIFAR-10 and CIFAR-100) and natural language understanding datasets (the GLUE benchmark) across various neural network architectures and demonstrate that our method is competitive against strong baselines.
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Authors: Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Reinforcement Learning (RL), Representation Learning

Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information from raw observations and subsequently plan efficiently. However, such approaches can fail in the presence of temporally correlated noise in the observations, a phenomenon that is common in practice. We initiate the formal study of latent state discovery in the presence of such exogenous noise sources by proposing a new model, the Exogenous Block MDP (EX-BMDP), for rich observation RL. We start by establishing several negative results, by highlighting failure cases of prior representation learning based approaches. Then, we introduce the Predictive Path Elimination (PPE) algorithm, that learns a generalization of inverse dynamics and is provably sample and computationally efficient in EX-BMDPs when the endogenous state dynamics are near deterministic. The sample complexity of PPE depends polynomially on the size of the latent endogenous state space while not directly depending on the size of the observation space, nor the exogenous state space. We provide experiments on challenging exploration problems which show that our approach works empirically.
Efficient Second-Order Optimization for Deep Learning with Kernel Machines
Authors: Yawen Chen, Zeyi Wen, Yile Chen, Jian Chen, Jin Huang
Date: 2021-09-29 00:00:00
Tasks: Deep Learning

Second-order optimization has been recently explored in neural network training. However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training. There have been some attempts to address the issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. To address the issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which projects the optimization problem to a transformed space with the Hessian matrix of kernel machines. Kernel SGD eliminates the recomputation of the Hessian matrix and requires a much smaller memory cost which can be controlled via the mini-batch size. The additional advantage of Kernel SGD is its ability to converge to better solutions according to our theoretical analysis. Kernel SGD is theoretically guaranteed to converge. Experimental results on tabular, image and text data show that Kernel SGD converges up to 30 times faster than the existing second-order optimization techniques, and also shows remarkable performance in generalization.
An Experimental Design Perspective on Exploration in Reinforcement Learning
Authors: Viraj Mehta, Biswajit Paria, Jeff Schneider, Willie Neiswanger, Stefano Ermon
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: continuous-control, Continuous Control, Experimental Design, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an \emph{acquisition function} that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach. We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to $5$ -- $1,000\times$ less data than model-based RL baselines and $10^3$ -- $10^5\times$ less data than model-free RL baselines. We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data.
VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling
Authors: Yang Li, Gang Li, Xin Zhou, Mostafa Dehghani, Alexey A. Gritsenko
Date: 2021-09-29 00:00:00
Tasks: object-detection, Object Detection, Question Answering, Text Generation

User interface modeling is inherently multimodal, which involves several distinct types of data: images, structures and language. The tasks are also diverse, including object detection, language generation and grounding. In this paper, we present VUT, a Versatile UI Transformer that takes multimodal input and simultaneously accomplishes 5 distinct tasks with the same model. Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input. Our model also consists of an auto-regressive Transformer model that encodes the language input and decodes output, for both question-answering and command grounding with respect to the UI. Our experiments show that for most of the tasks, when trained jointly for multi-tasks, VUT has achieved accuracy either on par with or exceeding the accuracy when the model is trained for individual tasks separately.
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
Authors: Liudmila Prokhorenkova, Dmitry Baranchuk, Nikolay Bogachev, Yury Demidovich, Alexander Kolpakov
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Information Retrieval, Retrieval, Word Embeddings

The nearest neighbor search (NNS) problem is widely studied in Euclidean space, and graph-based algorithms are known to outperform other approaches for this task. However, hyperbolic geometry is found to be very useful for data representation in various domains, including natural language processing, computer vision, and information retrieval. In this paper, we show that graph-based approaches are also well suited for hyperbolic geometry. From a theoretical perspective, we rigorously analyze the time and space complexity of graph-based NNS, assuming that an n-element dataset is uniformly distributed within a d-dimensional ball of radius R in the hyperbolic space of curvature -1. Assuming the dense setting (d << log(n)), we derive the time and space complexity of graph-based NNS and compare the obtained results with known guarantees for the Euclidean case. Interestingly, under some assumptions on dimension d and radius R, graph-based NNS has lower time complexity in the hyperbolic space. From a practical perspective, we illustrate this result on word embedding data: we compare graph-based NNS for GloVe and Poincare GloVe word embeddings. It turns out that for the same corpus, graph-based NNS is more efficient in the hyperbolic space. We also demonstrate that graph-based methods outperform other existing baselines on Poincare GloVe word embeddings. Overall, our theoretical and empirical analysis suggests that graph-based NNS can be considered a default approach for similarity search in hyperbolic spaces.
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems
Authors: Shusaku Sone, Atsushi Hashimoto, Jiaxin Ma, rintaro yanagi, Naoya Chiba, Yoshitaka Ushiku
Date: 2021-09-29 00:00:00
Tasks: 3D Point Cloud Matching, Combinatorial Optimization

Assignment, a task to match a limited number of elements, is a fundamental problem in informatics. Traditionally, non-linear assignment is discussed as a combinatorial optimization problem with its calculation complexity. On the other hand, it is often a sub-problem of image processing tasks, such as 3D point cloud matching. This paper proposes WeaveNet, a differentiable solver for diverse non-linear assignment problems. Traditional graph convolutional networks (GCNs) suffer from an over-smoothing problem when characterizing nodes with their relationship. WeaveNet overcomes this problem by forwarding edge-wise features at each layer rather than aggregated node features.
To deal with the exponentially large input space of combinatorial optimization problems, we designed WeaveNet to be highly parameter efficient while characterizing edges through stacked set-encoder with cross-concatenation operations. Experimental results show that WeaveNet approximates two strongly NP-hard variants of stable matching in a comparative performance with the gold standard hand-crafted algorithms under the limited size of problem instances. We have also confirmed that it can boost 3D point cloud matching performance significantly.
HYPOCRITE: Homoglyph Adversarial Examples for Natural Language Web Services in the Physical World
Authors: JINYONG KIM, JEONGHYEON KIM, MOSE GU, SANGHAK OHH, GILTEUN CHOI, JAEHOON JEONG
Date: 2021-09-29 00:00:00

Recently, as Artificial Intelligence (AI) develops, many companies in various industries are trying to use AI by grafting it into their domains.
Also, for these companies, various cloud companies (e.g., Amazon, Google, IBM, and Microsoft) are providing AI services as the form of Machine-Learning-as-a-Service (MLaaS).
However, although these AI services are very advanced and well-made, security vulnerabilities such as adversarial examples still exist, which can interfere with normal AI services.
This paper demonstrates a HYPOCRITE for hypocrisy that generates homoglyph adversarial examples for natural language web services in the physical world. This  hypocrisy can disrupt normal AI services provided by the cloud companies.
The key idea of HYPOCRITE is to replace English characters with other international  characters that look similar to them in order to give the dataset noise to the AI engines.
By using this key idea, parts of text can be appropriately replaced with subtext with malicious meaning through black-box attacks for natural language web services in order to cause misclassification.
In order to show attack potential by HYPOCRITE, this paper implemented a framework that makes homoglyph adversarial examples for natural language web services in the physical world and evaluated the performance under various conditions.
Through extensive experiments, it is shown that HYPOCRITE is more effective than other baseline in terms of both attack success rate and perturbed ratio.
Zero-Shot Reward Specification via Grounded Natural Language
Authors: Parsa Mahmoudieh, Sayna Ebrahimi, Deepak Pathak, Trevor Darrell
Date: 2021-09-29 00:00:00
Tasks: Reinforcement Learning (RL)

Reward signals in reinforcement learning can be expensive signals in many tasks and often require access to direct state. The alternative to reward signals are usually demonstrations or goal images which can be labor intensive to collect. Goal text description is a low effort way of communicating the desired task. Goal text conditioned policies so far though have been trained with reward signals that have access to state or labelled expert demonstrations. We devise a model that leverages CLIP to ground objects in a scene described by the goal text paired with spatial relationship rules to provide an off-the-shelf reward signal on only raw pixels to learn a set of robotic manipulation tasks. We distill the policies learned with this reward signal on several tasks to produce one goal text conditioned policy.
Fixed Neural Network Steganography: Train the images, not the network
Authors: Varsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li, Kilian Q Weinberger
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Decoder, Image Steganography, Steganalysis

Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images. These methods are able to hide large amounts of data, but also incur high decoding error rates (around 20\%). In this paper, we propose a novel algorithm for steganography that takes advantage of the fact that neural networks are sensitive to tiny perturbations. Our method, Fixed Neural Network Steganography (FNNS),  achieves 0\% error reliably for hiding up to 3 bits per pixel (bpp) of secret information in images and yields significantly lower error rates when compared to prior state of the art methods for hiding more than 3 bpp. FNNS also successfully evades existing statistical steganalysis systems and can be modified to evade neural steganalysis systems as well. Recovering every bit correctly, up to 3 bpp, enables novel applications, e.g. those requiring encryption.
We introduce one specific use case for facilitating anonymized and safe image sharing.
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection
Authors: Mohamed Yousef, Tom Bishop, Unmesh Kurup
Date: 2021-09-29 00:00:00
Tasks: Anomaly Detection, Out of Distribution (OOD) Detection, Self-Supervised Anomaly Detection, Self-Supervised Learning, Supervised Anomaly Detection, Unsupervised Anomaly Detection

Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples. Recently, large gains were made on this task for the domain of natural images using self-supervised contrastive feature learning as a first step followed by kNN or traditional one-class classifiers for feature scoring.
Learned representations that are non-uniformly distributed on the unit hypersphere have been shown to be beneficial for this task. We go a step further and investigate how the \emph {geometrical compactness} of the ID feature distribution makes isolating and detecting outliers easier, especially in the realistic situation when ID training data is polluted (i.e. ID data contains some OOD data that is used for learning the feature extractor parameters).

We propose novel architectural modifications to the self-supervised feature learning step, that enable such compact ID distributions to be learned. We show that the proposed modifications can be effectively applied to most existing self-supervised learning objectives with large gains in performance. Furthermore, this improved OOD performance is obtained without resorting to tricks such as using strongly augmented ID images (e.g. by 90 degree rotations) as proxies for the unseen OOD data, which imposes overly prescriptive assumptions about ID data and its invariances.

We perform extensive studies on benchmark datasets for one-class OOD detection and show state-of-the-art performance in the presence of pollution in the ID data, and comparable performance otherwise. We also propose and extensively evaluate a novel feature scoring technique based on the angular Mahalanobis distance, and propose a simple and novel technique for feature ensembling during evaluation that enables a big boost in performance at nearly zero run-time cost compared to the standard use of model ensembling or test time augmentations. Code for all models and experiments will be made open-source.
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach
Authors: Wen Shen, Henger Li, Zizhan Zheng
Date: 2021-09-29 00:00:00
Tasks: Federated Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a model-based multi-agent reinforcement learning attack framework against federated learning systems. Our framework first approximates the distribution of the clients' aggregated data through cooperative multi-agent coordination. It then learns an attack policy through multi-agent reinforcement learning. Depending on the availability of the server's federated learning configurations, we introduce algorithms for both white-box attacks and black-box attacks. Our attack methods are capable of handling scenarios when the clients' data is independent and identically distributed and when the data is independent but not necessarily identically distributed. We further derive an upper bound on the attacker's performance loss due to inaccurate distribution estimation. Experimental results on real-world datasets demonstrate that the proposed attack framework achieves strong performance even if the server deploys advanced defense mechanisms. Our work sheds light on how to attack federated learning systems through multi-agent coordination.
Generalisation in Lifelong Reinforcement Learning through Logical Composition
Authors: Geraud Nangue Tasse, Steven James, Benjamin Rosman
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether a new task can be immediately solved using its existing abilities, or whether a task-specific skill should be learned. In the latter case, the proposed algorithm also enables the agent to learn the new task faster by generating an estimate of the optimal policy. Importantly, we provide two main theoretical results: we give bounds on the performance of the transferred policy on a new task, and we give bounds on the necessary and sufficient number of tasks that need to be learned throughout an agent's lifetime to generalise over a distribution. We verify our approach in a series of experiments, where we perform transfer learning both after learning a set of base tasks, and after learning an arbitrary set of tasks. We also demonstrate that as a side effect of our transfer learning approach, an agent can produce an interpretable Boolean expression of its understanding of the current task. Finally, we demonstrate our approach in the full lifelong setting where an agent receives tasks from an unknown distribution and, starting from zero skills, is able to quickly generalise over the task distribution after learning only a few tasks---which are sub-logarithmic in the size of the task space.