nils-herrmann/clean_scirepeval huggingface dataset:
Group Sparse CNNs for Question Sentence Classification with Answer Sets
Authors: Mingbo Ma, Liang Huang, Bing Xiang, Bowen Zhou

Classifying question sentences into their corresponding categories is an important task with wide applications, for example in many websites' FAQ sections. 
However, traditional question classification techniques do not fully utilize the well-prepared answer data which has great potential for improving question sentence representations which could lead to better classification performance. In order to encode answer information into question representation, we first introduce novel group sparse autoencoders which could utilize the group information in the answer set to refine question representation. We then propose a new group sparse convolutional neural network which could naturally learn the question representation with respect to their corresponding answers by implanting the group sparse autoencoders into the traditional convolutional neural network. The proposed model show significant improvements over strong baselines on four datasets.
ChemistryQA: A Complex Question Answering Dataset from Chemistry
Authors: Zhuoyu Wei, Wei Ji, Xiubo Geng, Yining Chen, Baihua Chen, Tao Qin, Daxin Jiang

Many Question Answering (QA) tasks have been studied in NLP and employed to evaluate the progress of machine intelligence. One kind of QA tasks, such as Machine Reading Comprehension QA,  is well solved by end-to-end neural networks; another kind of QA tasks, such as Knowledge Base QA,  needs to be translated to a formatted representations and then solved by a well-designed solver. We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations. To further stimulate the research of QA and development of QA techniques, in this work, we create a new and complex QA dataset, ChemistryQA,  based on real-world chemical calculation questions. To answer chemical questions, machines need to understand questions, apply chemistry and Math knowledge, and do calculation and reasoning. To help researchers ramp up, we build two baselines: the first one is BERT-based sequence to sequence model, and the second one is an extraction system plus a graph search based solver. These two methods achieved 0.164 and 0.169 accuracy on the development set, respectively, which clearly demonstrate that new techniques are needed for complex QA tasks. ChemistryQA dataset will be available for public download once the paper is published.
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Authors: Jinghui Chen, Jinfeng Yi, Quanquan Gu

Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. In both cases, optimization-based attack algorithms can achieve relatively low distortions and high attack success rates. However, they usually suffer from poor time and query complexities, thereby limiting their practical usefulness. In this work, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on the non-convex Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an $O(1/\sqrt{T})$ convergence rate. The empirical results of attacking Inception V3 model and ResNet V2 model on the ImageNet dataset also verify the efficiency and effectiveness of the proposed algorithms. More specific, our proposed algorithms attain the highest attack success rate in both white-box and black-box attacks among all baselines, and are more time and query efficient than the state-of-the-art.
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion
Authors: Sijie Cheng, Jingwen Wu, Yanghua Xiao, Yang Liu, Yang Liu

Today data is often scattered among billions of resource-constrained edge devices with security and privacy constraints. Federated Learning (FL) has emerged as a viable solution to learn a global model while keeping data private, but the model complexity of FL is impeded by the computation resources of edge nodes. In this work, we investigate a novel paradigm to take advantage of a powerful server model to break through model capacity in FL. By selectively learning from multiple teacher clients and itself, a server model develops in-depth knowledge and transfers its knowledge back to clients in return to boost their respective performance. Our proposed framework achieves superior performance on both server and client models and provides several advantages in a unified framework, including flexibility for heterogeneous client architectures, robustness to poisoning attacks, and communication efficiency between clients and server. By bridging FL effectively with larger server model training, our proposed paradigm paves ways for robust and continual knowledge accumulation from distributed and private data.
The Needle in the haystack: Out-distribution aware Self-training in an Open-World Setting
Authors: Maximilian Augustin, Matthias Hein

Traditional semi-supervised learning (SSL) has focused on the closed world assumption where all unlabeled samples are task-related. In practice, this assumption is often violated when leveraging data from very large image databases that contain mostly non-task-relevant samples. While standard self-training and other established methods fail in this open-world setting, we demonstrate that our out-distribution-aware self-learning (ODST) with a careful sample selection strategy can leverage unlabeled datasets with millions of samples, more than 1600 times larger than the labeled datasets, and which contain only about $2\%$ task-relevant inputs. Standard and open world SSL techniques degrade in performance when the ratio of task-relevant sample decreases and show a significant distribution shift which is problematic regarding AI safety while ODST outperforms them with respect to test performance, corruption robustness and out-of-distribution detection.
Scalable Neural Methods for Reasoning With a Symbolic Knowledge   Base
Authors: William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler

We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB.  This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs. The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations.  The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.
Offline-Online Reinforcement Learning: Extending Batch and Online RL
Authors: Maryam Hashemzadeh, Wesley Chung, Martha White

Batch RL has seen a surge in popularity and is applicable in many practical scenarios where past data is available. Unfortunately, the performance of batch RL agents is limited in both theory and practice without strong assumptions on the data-collection process e.g. sufficient coverage or a good policy. To enable better performance, we investigate the offline-online setting: The agent has access to a batch of data to train on but is also allowed to learn during the evaluation phase in an online manner. This is an extension to batch RL, allowing the agent to adapt to new situations without having to precommit to a policy. In our experiments, we find that agents trained in an offline-online manner can outperform agents trained only offline or online, sometimes by a large margin, for different dataset sizes and data-collection policies. Furthermore, we investigate the use of optimism vs. pessimism for value functions in the offline-online setting due to their use in batch and online RL.
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Authors: Aleksandr Beznosikov, Peter Richtárik, Michael Diskin, Max Ryabinin, Alexander Gasnikov

Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across these and other applications, it is necessary to rely on parallel and distributed computing. However, in distributed training, communication among the compute nodes is a key bottleneck during training, and this problem is exacerbated for high dimensional and over-parameterized models models. Due to these considerations, it is important to equip existing methods with strategies that would allow to reduce the volume of transmitted information during training while obtaining a model of comparable quality. In this paper, we present the first theoretically grounded distributed methods for solving variational inequalities and saddle point problems using compressed communication: MASHA1 and MASHA2. Our theory and methods allow for the use of both unbiased (such as Rand$k$; MASHA1) and contractive (such as Top$k$; MASHA2) compressors. We empirically validate our conclusions using two experimental setups: a standard bilinear min-max problem, and large-scale distributed adversarial training of transformers.
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Authors: Wenhan Xiong, Xiang Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Scott Yih, Sebastian Riedel, Douwe Kiela, Barlas Oguz

We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Authors: Pan Xu, Felicia Gao, Quanquan Gu

Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/\epsilon^{3/2})$\footnote{$O(\cdot)$ notation hides constant factors.} episodes to find an $\epsilon$-approximate stationary point of the nonconcave performance function $J(\boldsymbol{\theta})$ (i.e., $\boldsymbol{\theta}$ such that $\|\nabla J(\boldsymbol{\theta})\|_2^2\leq\epsilon$). This sample complexity improves the existing result $O(1/\epsilon^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/\epsilon^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.
Offline Deep models calibration with bayesian neural networks
Authors: Juan Maroñas, Roberto Paredes, Daniel Ramos

We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep
neural networks. We show that, even with the most basic amortized approximate
posterior distribution, and fast fully connected neural network for the likelihood,
the Bayesian framework clearly outperforms other simple maximum likelihood
based solutions that have recently shown very good performance, as temperature
scaling. As an example, we reduce the Expected Calibration
Error (ECE) from 0.52 to 0.24 on CIFAR-10 and from 4.28 to 2.456 on CIFAR-100
on two Wide ResNet with 96.13% and 80.39% accuracy respectively, which are
among the best results published for this task. We demonstrate our robustness and
performance with experiments on a wide set of state-of-the-art computer vision
models. Moreover, our approach acts off-line, and thus can be applied to any
probabilistic model regardless of the limitations that the model may present during
training. This make it suitable to calibrate systems that make use of pre-trained
deep neural networks that are expensive to train for a specific task, or to directly
train a calibrated deep convolutional model with Monte Carlo Dropout approximations, among others. However,
our method is still complementary with any Bayesian Neural Network for further
improvement.
Privacy-preserving Task-Agnostic Vision Transformer for Image Processing
Authors: Boah Kim, Jeongsol Kim, Jong Chul Ye

Distributed collaborative learning approaches such as federated and split learning have attracted significant attention lately due to their ability to train neural networks using data from multiple sources without sharing data. However, they are not usually suitable in applications where each client carries out different tasks with its own data. Inspired by the recent success of Vision Transformer (ViT), here we present a new distributed learning framework for image processing applications, allowing clients to learn multiple tasks with their private data. The key idea arises from a novel task-agnostic Vision Transformer that is introduced to learn the global attention independent of specific tasks. Specifically, by connecting task-specific heads and tails at client sides to a task-agnostic Transformer body at a server side, each client learns a translation from its own task to a common representation, while the Transformer body learns global attention between the features embedded in the common representation. To enable decomposition between the task-specific and common representation, we propose an alternating training strategy in which task-specific learning for the heads and tails is run on the clients by fixing the Transformer, which alternates with task-agnostic learning for the Transformer on the server by freezing the heads and tails. Experimental results on multi-task learning for various image processing show that our method synergistically improves the performance of the task-specific network of each client while maintaining privacy.
Breaking the Expressive Bottlenecks of Graph Neural Networks
Authors: Mingqi Yang, Yanming Shen, Heng Qi, Baocai Yin

Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures. There were also improvements proposed in analogy to $k$-WL test ($k>1$). However, the aggregators in these GNNs are far from injective as required by the WL test, and suffer from weak distinguishing strength, making it become expressive bottlenecks. In this paper, we improve the expressiveness by exploring powerful aggregators. We reformulate aggregation with the corresponding aggregation coefficient matrix, and then systematically analyze the requirements of the aggregation coefficient matrix for building more powerful aggregators and even injective aggregators. It can also be viewed as the strategy for preserving the rank of hidden features, and implies that basic aggregators correspond to a special case of low-rank transformations. We also show the necessity of applying nonlinear units ahead of aggregation, which is different from most aggregation-based GNNs. Based on our theoretical analysis, we develop two GNN layers, ExpandingConv and CombConv. Experimental results show that our models significantly boost performance, especially for large and densely connected graphs.
DyRep: Learning Representations over Dynamic Graphs
Authors: Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha

Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundamental questions arise in learning over dynamic graphs: (i) How to elegantly model dynamical processes over graphs? (ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations? We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes). Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes. This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics. Our unified framework is trained using an efficient unsupervised procedure and has capability to generalize over unseen nodes. We demonstrate that DyRep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework.
LSH Microbatches for Stochastic Gradients:  Value in Rearrangement
Authors: Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias

Metric embeddings are   immensely useful representations of associations between entities   (images, users, search queries, words, and more).  Embeddings are learned by  optimizing a loss objective of the general form of a sum over example associations. Typically, the optimization uses stochastic gradient updates over minibatches of examples that are arranged  independently at random. In this work, we propose the use of {\em structured arrangements} through randomized {\em microbatches} of examples that are more likely to include similar ones. We make a principled argument for the properties of our arrangements  that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal  distribution of training examples.  Finally, we observe experimentally that our structured arrangements accelerate training by 3-20\%. Structured arrangements emerge as a powerful and novel performance knob for SGD that is independent and complementary to other SGD  hyperparameters and thus is a candidate for wide deployment.
Interpretable Meta-Reinforcement Learning with Actor-Critic Method
Authors: Xingyuan Liang, Xu-Ying Liu

Meta-reinforcement learning (meta-RL) algorithms have successfully trained agent systems to perform well on different tasks within only few updates. However, in gradient-based meta-RL algorithms, the Q-function at adaptation step is mainly estimated by the return of few trajectories, which can lead to high variance in Q-value and biased meta-gradient estimation, and the adaptation uses a large number of batched trajectories. To address these challenges, we propose a new meta-RL algorithm that can reduce the variance and bias of the meta-gradient estimation and perform few-shot task data sampling, which makes the meta-policy more interpretable. We reformulate the meta-RL objective, and introduce contextual Q-function as a meta-policy critic during task adaptation step and learn the Q-function under a soft actor-critic (SAC) framework. The experimental results on 2D navigation task and meta-RL benchmarks show that our approach can learn an more interpretable meta-policy to explore unknown environment and the performance are comparable to previous gradient-based algorithms.
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning
Authors: Xiaoshuang Li, Junchen Jin, Xiao Wang, Fei-Yue Wang

Although Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies by directly interacting with simulation environments, how to combine DRL with supervised learning and leverage additional knowledge to assist the DRL agent effectively still remains difficult. This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance. Specifically, the DQfDD-BC model leverages historical demonstrations to pre-train a supervised BC model and consistently update it by learning the dynamically updated demonstrations. Then the DQfDD-BC model manages the sample complexity by exploiting both the historical and generated demonstrations. An expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach adapts to different performance levels of demonstrations, and meanwhile, accelerates the learning processes. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms with the utilization of a BC model contribute to improving the learning convergence performance compared with the origin DQfD model.
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Authors: Minhao Cheng, Simranjit Singh, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh

We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access:  the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. Several algorithms have been proposed for this problem but they typically require huge amount (>20,000) of queries for attacking one example. Among them, one of the state-of-the-art approaches (Cheng et al., 2019) showed that hard-label attack can be modeled as an optimization problem where the objective function can be evaluated by binary search with additional model queries, thereby a zeroth order optimization algorithm can be applied. In this paper, we adopt the same optimization formulation but  propose to directly estimate the sign of gradient at any direction instead of the gradient itself, which enjoys the benefit of single query. 
Using this single query oracle for retrieving sign of directional derivative, we develop a novel query-efficient Sign-OPT approach for hard-label black-box attack. We provide a convergence analysis of the new algorithm and conduct experiments on several models on MNIST, CIFAR-10 and ImageNet. 
We find that Sign-OPT attack consistently requires 5X to 10X fewer queries when compared to the current state-of-the-art approaches, and usually converges to an adversarial example with smaller perturbation.