nils-herrmann/clean_scirepeval huggingface dataset:
Localized Randomized Smoothing for Collective Robustness Certification
Authors: Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann

Models for image segmentation, node classification and many other tasks map a single input to multiple labels. By perturbing this single shared input (e.g. the image) an adversary can manipulate several predictions (e.g. misclassify several pixels). A recent collective robustness certificate provides strong guarantees on the number of predictions that are simultaneously robust. This method is however limited to strictily models, where each prediction is associated with a small receptive field. We propose a more general collective certificate for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e.g. based on their proximity in the image). The certificate is based on our novel localized randomized smoothing approach, where the random perturbation strength for different input regions is proportional to their importance for the outputs. The resulting locally smoothed model yields strong collective guarantees while maintaining high prediction quality on both image segmentation and node classification tasks.
Counting Substructures with Higher-Order Graph Neural Networks:  Possibility and Impossibility Results
Authors: Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka

While message passing Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in their expressive power. In response, several higher-order GNNs have been proposed that substantially increase the expressive power, albeit at a large computational cost. Motivated by this gap, we explore alternative strategies and lower bounds. In particular, we analyze a new recursive pooling technique of local neighborhoods that allows different tradeoffs of computational cost and expressive power. First, we prove that this model can count subgraphs of size $k$, and thereby overcomes a known limitation of low-order GNNs. Second, we show how recursive pooling can exploit sparsity to reduce the computational complexity compared to the existing higher-order GNNs. More generally, we provide a (near) matching information-theoretic lower bound for counting subgraphs with graph representations that pool over representations of derived (sub-)graphs. We also discuss lower bounds on time complexity.
AIR-Net: Adaptive and Implicit Regularization Neural Network for matrix completion
Authors: Zhemin Li, Hongxia Wang

Conventionally, the matrix completion (MC) model aims to recover a matrix from partially observed elements. Accurate recovery necessarily requires a regularization encoding priors of the unknown matrix/signal properly. However, encoding the priors accurately for the complex natural signal is difficult, and even then, the model might not generalize well outside the particular matrix type. This work combines adaptive and implicit low-rank regularization that captures the prior dynamically according to the current recovered matrix. Furthermore, we aim to answer the question: how does adaptive regularization affect implicit regularization? We utilize neural networks to represent Adaptive and Implicit Regularization and named the proposed model \textit{AIR-Net}. Theoretical analyses show that the adaptive part of the AIR-Net enhances implicit regularization. In addition, the adaptive regularizer vanishes at the end, thus can avoid saturation issues. Numerical experiments for various data demonstrate the effectiveness of AIR-Net, especially when the locations of missing elements are not randomly chosen. With complete flexibility to select neural networks for matrix representation, AIR-Net can be extended to solve more general inverse problems.
Empirical confidence estimates for classification by deep neural networks
Authors: Chris Finlay, Adam M. Oberman

How well can we estimate the probability that the classification predicted by a deep neural network is correct (or in the Top 5)?  It is well-known that the softmax values of the network are not estimates of the probabilities of class labels.  However, there is a misconception that these values are not informative.  We define the notion of implied loss and prove that if an uncertainty measure is an implied loss, then low uncertainty means high probability of correct (or Top-k) classification on the test set.   We demonstrate empirically that these values can be used to measure the confidence that the classification is correct.  Our method is simple to use on existing networks: we proposed confidence measures for Top-k which can be evaluated by binning values on the test set.
Self-Organizing Intelligent Matter:  A blueprint for an AI generating algorithm
Authors: Karol Gregor, Frederic Besse

We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through physics-like rules contained in the environment. We discuss how an evolutionary process can lead to the emergence of different organisms made of many such atomic elements which can coexist and thrive in the environment. We discuss how this forms the basis of a general AI generating algorithm. We provide a simplified implementation of such system and discuss what advances need to be made to scale it up further.
On the Latent Holes 🧀 of VAEs for Text Generation
Authors: Ruizhe Li, Xutan Peng, Chenghua Lin

In this paper, we provide the first focused study on the discontinuities (aka. holes) in the latent space of Variational Auto-Encoders (VAEs), a phenomenon which has been shown to have a detrimental effect on model capacity. When investigating la- tent holes, existing works are exclusively centred around the encoder network and they merely explore the existence of holes. We tackle these limitations by proposing a highly efficient Tree-based Decoder-Centric (TDC) algorithm for latent hole identification, with a focal point on the text domain. In contrast to past studies, our approach pays attention to the decoder network, as a decoder has a direct impact on the model’s output quality. Furthermore, we provide, for the first time, in-depth empirical analysis of the latent hole phenomenon, investigating several important aspects such as how the holes impact VAE algorithms’ performance on text generation, and how the holes are distributed in the latent space.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Authors: Ruize Wang, Duyu Tang, Nan Duan, zhongyu wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, they may suffer from catastrophic forgetting.  To address this, we propose K-Adapter, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion. Taking RoBERTa as the pre-trained model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus different adapters are efficiently trained in a distributed way. We inject two kinds of knowledge, including factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata, and linguistic knowledge obtained from dependency parsing. Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing and question answering demonstrate that each adapter improves the performance, and the combination of both adapters brings further improvements. Probing experiments further indicate that K-Adapter captures richer factual and commonsense knowledge than RoBERTa.
UserBERT: Self-supervised User Representation Learning
Authors: Tianyu Li, Ali Cevahir, Derek Cho, Hao Gong, DuyKhuong Nguyen, Bjorn Stenger

This paper extends the BERT model to user data for pretraining user representations in a self-supervised way. By viewing actions (e.g., purchases and clicks) in behavior sequences (i.e., usage history) in an analogous way to words in sentences, we propose methods for the tokenization, the generation of input representation vectors and a novel pretext task to enable the pretraining model to learn from its own input, omitting the burden of collecting additional data. Further, our model adopts a unified structure to simultaneously learn from long-term and short-term user behavior as well as user profiles. Extensive experiments demonstrate that the learned representations  result in significant improvements when transferred to three different real-world tasks, particularly in comparison with task-specific modeling and representations obtained from multi-task learning.
Collaborate to Defend Against Adversarial Attacks
Authors: Sen Cui, Jingfeng Zhang, Jian Liang, Masashi Sugiyama, Changshui Zhang

Adversarially robust learning methods require invariant predictions to a small neighborhood of its natural inputs, thus often encountering insufficient model capacity. Learning multiple models in an ensemble can mitigate this insufficiency, further improving both generalization and robustness. However, an ensemble still wastes the limited capacity of multiple models. To optimally utilizing the limited capacity, this paper proposes to learn a collaboration among multiple sub-models. Compared with the ensemble, the collaboration enables the possibility of correct predictions even if there exists a single correct sub-model. Besides, learning a collaboration could enable every sub-model to fit its own vulnerability area and reserve the rest of the sub-models to fit other vulnerability areas. To implement the idea, we propose a collaboration framework---CDA$^2$ the abbreviation for Collaborate to Defend against Adversarial Attacks. CDA$^2$ could effectively minimize the vulnerability overlap of all sub-models and then choose a representative sub-model to make correct predictions. Empirical experiments verify that CDA$^2$ outperforms various ensemble methods against black-box and white-box adversarial attacks.
BasisNet: Two-stage Model Synthesis for Efficient Inference
Authors: Mingda Zhang, Andrey Zhmoginov, Andrew G. Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka

We present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach uses a lightweight model to preview an image and generate input-dependent combination coefficients, which are later used to control the synthesis of a specialist model for making more accurate final prediction. The two-stage model synthesis strategy can be used with any network architectures and both stages can be jointly trained end to end. We validated BasisNet on ImageNet classification with MobileNets as backbone, and demonstrated clear advantage on accuracy-efficiency trade-off over strong baselines such as EfficientNet (Tan & Le, 2019), FBNetV3 (Dai et al., 2020) and OFA (Cai et al., 2019). Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M Multiply-Add operations (MAdds), halving the computational cost of previous state-of-the-art without sacrificing accuracy. Besides, since the first-stage lightweight model can independently make predictions, inference can be terminated early if the prediction is sufficiently confident. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0%.
Transferable Recognition-Aware Image Processing
Authors: Zhuang Liu, Tinghui Zhou, Hung-Ju Wang, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell

Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Existing image processing methods only optimize for better human perception, yet the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we propose simple approaches to improve machine interpretability of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate transforming model. Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks and training datasets. This makes the solutions applicable even when we do not have the knowledge of future recognition models, e.g., if we upload processed images to the Internet. We conduct experiments on multiple image processing tasks, with ImageNet classification and PASCAL VOC detection as recognition tasks. With our simple methods, substantial accuracy gain can be achieved with strong transferability and minimal image quality loss. Through a user study we further show that the accuracy gain can transfer to a black-box, third-party cloud model. Finally, we try to explain this transferability phenomenon by demonstrating the similarities of different models' decision boundaries. Code is available at https://github.com/anonymous20202020/Transferable_RA .
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments
Authors: Jingpu Shi, Dong Wang, Gino Tesei, Beau Norgeot

A causal effect can be defined as the comparison of outcomes from two or more alternative treatments. Knowing this treatment effect is critically important in healthcare because it makes it possible to identify the best treatment for a person when more than one option exists. In the past decade, there has been exponentially growing interest in the use of observational data collected as a part of routine healthcare practice to determine the effect of a treatment with causal inference models. Validation of these models, however, has been a challenge because the ground truth is unknown: only one treatment-outcome pair for each person can be observed. There have been multiple efforts to fill this void using synthetic data where the ground truth can be generated. However, to date, these datasets have been severely limited in their utility either by being modeled after small non-representative patient populations, being dissimilar to real target populations, or only providing known effects for two cohorts (treated vs control). In this work, we produced a large-scale and realistic synthetic dataset that supports multiple hypertension treatments, by modeling after a nationwide cohort of more than 250,000 hypertension patients' multi-year history of diagnoses, medications, and laboratory values. We designed a data generation process by combining an adapted ADS-GAN model for fictitious patient information generation and a neural network for treatment outcome generation. Wasserstein distance of 0.35 demonstrates that our synthetic data follows a nearly identical joint distribution to the patient cohort used to generate the data. Our dataset provides ground truth effects for about 30 hypertension treatments on blood pressure outcomes. Patient privacy was a primary concern for this study; the $\epsilon$-identifiability metric, which estimates the probability of actual patients being identified, is 0.008%, ensuring that our synthetic data cannot be used to identify any actual patients. Using our dataset, we tested the bias in causal effect estimation of three well-established models: propensity sore stratification, doubly robust approach (DR) with logistic regression, DR with random forest (RF) classification. Interestingly, we found that while the RF DR outperformed the logistic DR as expected, the best performance actually came from  propensity score stratification, despite the theoretical strength of statistical properties of the DR family of models. We believe this dataset will facilitate the additional development, evaluation, and comparison of real-world causal models. The approach we used can be readily extended to other types of diseases in the clinical domain, and to datasets in other domains as well.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Authors: Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen

Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance,  practical gaps exist when deploying DRL in real-world scenarios. One main barrier is the over-fitting issue that leads to poor generalizability of the policy learned by DRL. In particular, for offline DRL with observational data, model selection is a challenging task as there is no ground truth available for performance demonstration, in contrast with the online setting with simulated environments. In this work, we propose a pessimistic model selection (PMS) approach for offline DRL with a theoretical guarantee, which features a tuning-free framework for finding the best policy among a set of candidate models. Two refined approaches are also proposed to address the potential bias of DRL model in identifying the optimal policy. Numerical studies demonstrated the superior performance of our approach over existing methods.
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Authors: Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud

To address the resilience of distributed learning, the ``Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. While this model helped generate several fundamental results, it has however sometimes been considered unrealistic, when the workers are mostly trustworthy machines. In this paper, we show a surprising equivalence between this model and data poisoning, a threat considered much more realistic. More specifically, we prove that any gradient attack can be reduced to data poisoning in a personalized federated learning system that provides PAC guarantees (which we show are both desirable and realistic in various personalized federated learning contexts such as linear regression and classification). Maybe most importantly, we derive a simple and practical attack that may be constructed against classical personalized federated learning models, and we show both theoretically and empirically the effectiveness of this attack.
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Authors: Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

Currently, progressively larger deep neural networks are trained on ever growing data corpora. In result, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. 
%These challenges become even more pressing, as the number of computation nodes increases. 
To mitigate this problem we propose Sparse Binary Compression (SBC), a compression framework that allows for a drastic reduction of communication cost for distributed training. SBC combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits. By doing so, our method also allows us to smoothly trade-off gradient sparsity and temporal sparsity to adapt to the requirements of the learning task. 
%We use tools from information theory to reason why SBC can achieve the striking compression rates observed in the experiments.
Our experiments show, that SBC can reduce the upstream communication on a variety of convolutional and recurrent neural network architectures by more than four orders of magnitude without significantly harming the convergence speed in terms of forward-backward passes. For instance, we can train ResNet50 on ImageNet in the same number of iterations to the baseline accuracy, using $\times 3531$ less bits or train it to a $1\%$ lower accuracy using $\times 37208$ less bits. In the latter case, the total upstream communication required is cut from 125 terabytes to 3.35 gigabytes for every participating client. Our method also achieves state-of-the-art compression rates in a Federated Learning setting with 400 clients.
Count-Based Exploration with the Successor Representation
Authors: Marlos C. Machado, Marc G. Bellemare, Michael Bowling

The problem of exploration in reinforcement learning is well-understood in the tabular case and many sample-efficient algorithms are known. Nevertheless, it is often unclear how the algorithms in the tabular setting can be extended to tasks with large state-spaces where generalization is required. Recent promising developments generally depend on problem-specific density models or handcrafted features. In this paper we introduce a simple approach for exploration that allows us to develop theoretically justified algorithms in the tabular case but that also give us intuitions for new algorithms applicable to settings where function approximation is required. Our approach and its underlying theory is based on the substochastic successor representation, a concept we develop here. While the traditional successor representation is a representation that defines state generalization by the similarity of successor states, the substochastic successor representation is also able to implicitly count the number of times each state (or feature) has been observed. This extension connects two until now disjoint areas of research. We show in traditional tabular domains (RiverSwim and SixArms) that our algorithm empirically performs as well as other sample-efficient algorithms. We then describe a deep reinforcement learning algorithm inspired by these ideas and show that it matches the performance of recent pseudo-count-based methods in hard exploration Atari 2600 games.
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents
Authors: Ammar Fayad, Majd Ibrahim

Discovering successful coordinated behaviors is a central challenge in Multi-Agent Reinforcement Learning (MARL) since it requires exploring a joint action space that grows exponentially with the number of agents. In this paper, we propose a mechanism for achieving sufficient exploration and coordination in a team of agents. Specifically, agents are rewarded for contributing to a more diversified team behavior by employing proper intrinsic motivation functions. To learn meaningful coordination protocols, we structure agents’ interactions by introducing a novel framework, where at each timestep, an agent simulates counterfactual rollouts of its policy and, through a sequence of computations, assesses the gap between other agents’ current behaviors and their targets. Actions that minimize the gap are considered highly influential and are rewarded. We evaluate our approach on a set of challenging tasks with sparse rewards and partial observability that require learning complex cooperative strategies under a proper exploration scheme, such as the StarCraft Multi-Agent Challenge. Our methods show significantly improved performances over different baselines across all tasks.