pwc-archive/papers-with-abstracts huggingface dataset:
$G^3$: Representation Learning and Generation for Geometric Graphs
Authors: Han Huang, Stefan C Schonsheck, Rongjie Lai, Jie Chen
Date: 2021-09-29 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Property Prediction, Representation Learning

A geometric graph is a graph equipped with geometric information (i.e., node coordinates). A notable example is molecular graphs, where the combinatorial bonding is supplement with atomic coordinates that determine the three-dimensional structure. This work proposes a generative model for geometric graphs, capitalizing on the complementary information of structure and geometry to learn the underlying distribution. The proposed model, Geometric Graph Generator (G$^3$), orchestrates graph neural networks and point cloud models in a nontrivial manner under an autoencoding framework. Additionally, we augment this framework with a normalizing flow so that one can effectively sample from the otherwise intractable latent space. G$^3$ can be used in computer-aided drug discovery, where seeking novel and optimal molecular structures is critical. As a representation learning approach, the interaction of the graph structure and the geometric point cloud also improve significantly the performance of downstream tasks, such as molecular property prediction. We conduct a comprehensive set of experiments to demonstrate that G$^3$ learns more accurately the distribution of given molecules and helps identify novel molecules with better properties of interest.
MOBA: Multi-teacher Model Based Reinforcement Learning
Authors: Jikun Kang, Xi Chen, Ju Wang, Chengming Hu, Xue Liu, Gregory Dudek
Date: 2021-09-29 00:00:00
Tasks: Decision Making, Knowledge Distillation, model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Although reinforcement learning (RL) shines at solving decision-making problems, it not only requires collecting a large amount of environment data but also is time-consuming for training and interaction, making it hard to apply to real applications. To reduce the time-cost and improve the data efficiency, model-based reinforcement learning uses a learned system model to predict system dynamics (i.e. states or rewards) and makes a plan accordingly, thus avoiding the frequent environment interaction. Model-based methods suffer from the model-bias problem, where certain spaces of model are inaccurate, resulting in policy learning variations and system performance degradation.
We propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to solve the model-bias problem. Specifically, different teachers search different spaces and learn various instances of a system. By distilling and transferring the teacher knowledge to a student, the student model is able to learn a generalized dynamic model that covers the state space. Moreover, to overcome the instability of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. We evaluate MOBA in high-dimensional control locomotion tasks. Results show that, compared with SOTA model-free methods, our method can improve the data efficiency and system performance by up to 75% and 10%, respectively. Moreover, our method outperforms other SOTA model-based approaches by up to 63.2% when exposed to high-range model-bias environments.
SGORNN: Combining Scalar Gates and Orthogonal Constraints in Recurrent Networks
Authors: William Keith Taylor-Melanson, Martha Dais Ferreira, Stan Matwin
Date: 2021-09-29 00:00:00
Tasks: Language Modelling

Recurrent Neural Network (RNN) models have been applied in different domains, producing high accuracies on time-dependent data. However, RNNs have long suffered from exploding gradients during training, mainly due to their recurrent process. In this context, we propose a variant of the scalar gated FastRNN architecture, called Scalar Gated Orthogonal Recurrent Neural Networks (SGORNN). SGORNN utilizes orthogonal linear transformations at the recurrent step. In our experiments, SGORNN forms its recurrent weights through a strategy inspired by Volume Preserving RNNs (VPRNN), though our architecture allows the use of any orthogonal constraint mechanism. We present a simple constraint on the scalar gates of SGORNN, which is easily enforced at training time to provide a theoretical generalization ability for SGORNN similar to that of FastRNN. Our constraint is further motivated by success in experimental settings. Next, we provide bounds on the gradients of SGORNN, to show the impossibility of (exponentially) exploding gradients. Our experimental results on the addition problem confirm that our combination of orthogonal and scalar gated RNNs are able to outperform both predecessor models on long sequences using only a single RNN cell. We further evaluate SGORNN on the HAR-2 classification task, where it improves slightly upon the accuracy of both FastRNN and VPRNN using far fewer parameters than FastRNN. Finally, we evaluate SGORNN on the Penn Treebank word-level language modelling task, where it again outperforms its predecessor architectures. Overall, this architecture shows higher representation capacity than VPRNN, suffers from less overfitting than the other two models in our experiments, benefits from a decrease in parameter count, and alleviates exploding gradients when compared with FastRNN on the addition problem.
Linear Convergence of SGD on Overparametrized Shallow Neural Networks
Authors: Paul Rolland, Ali Ramezani-Kebrya, ChaeHwan Song, Fabian Latorre, Volkan Cevher
Date: 2021-09-29 00:00:00

Despite the non-convex landscape, first-order methods can be shown to reach global minima when training overparameterized neural networks, where the number of parameters far exceed the number of training data. In this work, we prove linear convergence of stochastic gradient descent when training a two-layer neural network with smooth activations. While the existing theory either requires a high degree of overparameterization or non-standard initialization and training strategies, e.g., training only a single layer, we show that a subquadratic scaling on the width is sufficient under standard initialization and training both layers simultaneously if the minibatch size is sufficiently large and it also grows with the number of training examples. Via the batch size, our results interpolate between the state-of-the-art subquadratic results for gradient descent and the quadratic results in the worst case.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Authors: Changhee Lee, Fergus Imrie, Mihaela van der Schaar
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: feature selection, Self-Supervised Learning

Discovering relevant input features for predicting a target variable is a key scientific question. However, in many domains, such as medicine and biology, feature selection is confounded by a scarcity of labeled samples coupled with significant correlations among features. In this paper, we propose a novel deep learning approach to feature selection that addresses both challenges simultaneously. First, we pre-train the network using unlabeled samples within a self-supervised learning framework via solving pretext tasks that require the network to learn informative representations from partial feature sets. Then, we fine-tune the pre-trained network to discover relevant features using labeled samples. During both training phases, we explicitly account for the correlation structure of the input features by generating correlated gate vectors from a multivariate Bernoulli distribution. Experiments on multiple real-world datasets including clinical and omics demonstrate that our model discovers relevant features that provide superior prediction performance compared to the state-of-the-art benchmarks, in practical scenarios where there is often limited labeled data and high correlations among features.
Code Editing from Few Exemplars by Adaptive Multi-Extent Composition
Authors: Peizhao Li, Xuchao Zhang, Ziyu Yao, Wei Cheng, Haifeng Chen, Hongfu Liu
Date: 2021-09-29 00:00:00

This paper considers the computer source code editing with few exemplars. The editing exemplar, containing the original and modified support code snippets, showcases a certain editorial style and implies the edit intention for a query code snippet. To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet. Our learning approach combines edit representations extracted from editing exemplars and compositionally generalizes them to the query code snippet editing via multi-extent similarities ensemble. Specifically, we parse the code snippets using language-specific grammar into abstract syntax trees. We apply the similarities measurement in multiple extents from individual nodes to collective tree representations, and ensemble them through a similarity-ranking error estimator. We evaluate the proposed method on two datasets in C\# and Python languages and respectively show 8.0\% and 10.9\% absolute accuracy improvements compared to baselines.
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions
Authors: Yu Hao, Yi Fang
Date: 2021-09-29 00:00:00
Tasks: Meta-Learning, Point Cloud Registration

Learning robust 3D point cloud registration functions with deep neural networks has emerged as a powerful paradigm in recent years, offering promising performance in producing spatial geometric transformations for each pair of 3D point clouds. However, 3D point cloud registration functions are often generalized from extensive training over a large volume of data to learn the ability to predict the desired geometric transformation to register 3D point clouds. Generalizing across 3D point cloud registration functions requires robust learning of priors over the respective function space and enables consistent registration in presence of significant 3D structure variations. In this paper, we proposed to formalize the learning of a 3D point cloud registration function space as a meta-learning problem, aiming to predict a 3D registration model that can be quickly adapted to new point clouds with no or limited training data. Specifically, we define each task as the learning of the 3D registration function which takes points in 3D space as input and predicts the geometric transformation that aligns the source point cloud with the target one. Also, we introduce an auxiliary deep neural network named 3D registration meta-learner that is trained to predict the prior over the respective 3D registration function space. After training, the 3D registration meta-learner, which is trained with the distribution of 3D registration function space, is able to uniquely parameterize the 3D registration function with optimal initialization to rapidly adapt to new registration tasks. We tested our model on the synthesized dataset ModelNet and FlyingThings3D, as well as real-world dataset KITTI. Experimental results demonstrate that 3D Meta-Registration achieves superior performance over other previous techniques (e.g. FlowNet3D).
A new look at fairness in stochastic multi-armed bandit problems
Authors: Guanhua Fang, Ping Li, Gennady Samorodnitsky
Date: 2021-09-29 00:00:00
Tasks: Fairness

We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes fairness into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level. In this paper, we present a new insight in MAB with fairness and formulate the problem in the penalization framework, where rigorous penalized regret can be well defined and more sophisticated regret analysis is possible.  Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including asymptotic fairness, nearly optimal regret, better tradeoff between reward and fairness. Both gap-dependent and gap-independent upper bounds have been established. Lower bounds are also given to illustrate the tightness of our theoretical analysis. Numerous experimental results corroborate the theory and show the superiority of our method over other existing methods.
Surprise Minimizing Multi-Agent Learning with Energy-based Models
Authors: Karush Suri
Date: 2021-09-29 00:00:00
Tasks: Multi-agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success by virtue of collaboration across agents. Recent work, on the other hand, introduces surprise which quantifies the degree of change in an agent's environment. Surprise-based learning has received significant attention in the case of single-agent entropic settings but remains an open problem for fast-paced dynamics in multi-agent scenarios. A potential alternative to address surprise may be realized through the lens of free-energy minimization. We explore surprise minimization in multi-agent learning by utilizing the free energy across all agents in a multi-agent system. A temporal Energy-Based Model (EBM) represents an estimate of surprise which is minimized over the joint agent distribution. Our formulation of the EBM is theoretically akin to the minimum conjugate entropy objective and highlights suitable convergence towards minimum surprising states. We further validate our theoretical claims in an empirical study of multi-agent tasks demanding collaboration in the presence of fast-paced dynamics.
Understanding Overfitting in Reweighting Algorithms for Worst-group Performance
Authors: Runtian Zhai, Chen Dan, J Zico Kolter, Pradeep Kumar Ravikumar
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Fairness

Prior work has proposed various reweighting algorithms to improve the worst-group performance of machine learning models for fairness. However, Sagawa et al. (2020) empirically found that these algorithms overfit easily in practice under the overparameterized setting, where the number of model parameters is much greater than the number of samples. In this work, we provide a theoretical backing to the empirical results above, and prove the pessimistic result that reweighting algorithms always overfit. Specifically we prove that with reweighting, an overparameterized model always converges to the same ERM interpolator that fits all training samples, and consequently its worst-group test performance will drop to the same level as ERM in the long run. That is, we cannot hope for reweighting algorithms to converge to a different interpolator than ERM with potentially better worst-group performance. Then, we analyze whether adding regularization helps fix the issue, and we prove that for regularization to work, it must be large enough to prevent the model from achieving small training error. Our results suggest that large regularization (or early stopping) and data augmentation are necessary for reweighting algorithms to achieve high worst-group test performance.
Mimicking Randomized Controlled Trials to Learn End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation
Authors: Gino Tesei, Stefanos Giampanis, Beau Norgeot
Date: 2021-09-29 00:00:00
Tasks: Representation Learning

A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. The gold standard for causal effect measurements is Randomized Controlled Trials (RCTs), in which a target population is explicitly defined and each study sample is randomly assigned to either the treatment or control cohorts. The great potential to derive actionable insights from causal relationships has led to a growing body of machine-learning research applying causal effect estimators to Real World Data (RWD) in the fields of healthcare, education, and economics. The primary difference between causal effect studies utilizing RWD and RCTs is that for RWD the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism. This can lead to massive differences in covariate distributions between control and treatment samples, making a comparison of causal effects confounded and unreliable. Classical approaches have sought to solve this problem piece meal, first by estimating treatment assignment and then treatment effect separately. Recent work extended part of these approaches to a new family of representation-learning based algorithms, revealing that the lower bound of the expected treatment effect estimation error is determined by two factors: the standard generalization-error of the representation and the distance between the treated and control distributions induced by the representation. Here we argue that to achieve minimal dissimilarity in learning such distributions, as it happens for RCTs, a specific auto-balancing self-supervised objective should be used. Experiments on real and simulated data revealed that our approach consistently produces less biased errors than previously published state-of-art methods. We demonstrate that our reduction in error can be directly attributed to the ability to learn representations that explicitly reduce such dissimilarity. Additionally, we show that error improvements between our approach and previously published state-of-art methods widen as a function of sample dissimilarity between treated and untreated covariate distributions. Thus, by learning representations that induce distributions analogous to RCTs, we provide empirical evidence to support the error bound dissimilarity hypothesis as well as providing a new state-of-the-art model for causal effect estimation.
Automated Channel Pruning with Learned Importance
Authors: Łukasz Treszczotko, Pawel Kubik
Date: 2021-09-29 00:00:00
Tasks: Denoising, GPU, Image Denoising, Knowledge Distillation, Network Pruning

Neural network pruning allows for significant reduction of model size and latency. However, most of the current network pruning methods do not consider channel interdependencies and a lot of manual adjustments are required before they can be applied to new network architectures. Moreover, these algorithms are often based on hand-picked, sometimes complicated heuristics and can require thousands of GPU computation hours.  In this paper, we introduce a simple neural network pruning and fine-tuning framework that requires no manual heuristics, is highly efficient to train (2-6 times speed up compared to NAS-based competitors) and produces comparable performance. The framework contains 1) an automatic channel detection algorithm that groups the interdependent blocks of channels; 2) a non-iterative pruning algorithm that learns channel importance directly from feature maps while masking the coupled computational blocks using Gumbel-Softmax sampling and 3) a hierarchical knowledge distillation approach to fine-tune the pruned neural networks. We validate our pipeline on ImageNet classification, human segmentation and image denoising, creating lightweight and low latency models, easy to deploy on mobile devices. Using our pruning algorithm and hierarchical knowledge distillation for fine-tuning we are able to prune EfficientNet B0, EfficientNetV2 B0 and MobileNetV2  to 75% of their original FLOPs with no loss of accuracy on ImageNet. We release a set pruned backbones as Keras models - all of them proved beneficial when deployed in other projects.
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions
Authors: Eoin M. Kenny, Eoin D. Delaney, Mark T. Keane
Date: 2021-09-29 00:00:00
Tasks: Classification, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

There is an increasing body of evidence suggesting that post-hoc explanation-by- example with nearest neighbors is a promising solution for the eXplainable Artificial Intelligence (XAI) problem. However, despite being thoroughly researched for decades, such post-hoc methods have never seriously explored how to enhance these explanations by highlighting specific important "parts" in a classification. Here, we propose the notion of Critical Classification Regions (CCRs) to do this, and several possible methods are experimentally compared to determine the best approach for this explanation strategy. CCRs supplement nearest neighbor examples by highlighting similar important "parts" in the image explanation. Experiments across multiple domains show that CCRs represent key features used by the CNN in both the testing and training data. Finally, a suitably-controlled user study (N=163) on ImageNet, shows CCRs improve people’s assessments of the correctness of a CNN’s predictions for difficult classifications due to ambiguity.
What can multi-cloud configuration learn from AutoML?
Authors: Malgorzata Lazuka, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
Date: 2021-09-29 00:00:00
Tasks: AutoML, Cloud Computing

Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to benefit from such differences will naturally want to solve the multi-cloud configuration problem: given a workload, which cloud provider should be chosen and how should its nodes be configured in order to minimize runtime or cost? In this work, we consider this multi-cloud optimization problem and publish a new offline benchmark dataset, MOCCA, comprising 60 different multi-cloud configuration tasks across 3 public cloud providers, to enable further research in this area. Furthermore, we identify an analogy between multi-cloud configuration and the selection-configuration problems that are commonly studied in the automated machine learning (AutoML) field. Inspired by this connection, we propose an algorithm for solving multi-cloud configuration, CloudBandit (CB). It treats the outer problem of cloud provider selection as a best-arm identification problem, in which each arm pull corresponds to running an arbitrary black-box optimizer on the inner problem of node configuration. Extensive experiments on MOCCA indicate that CB achieves (a) significantly lower regret relative to its component black-box optimizers and (b) competitive or lower regret relative to state-of-the-art AutoML methods, whilst also being cheaper and faster.
SketchODE: Learning neural sketch representation in continuous time
Authors: Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Data Augmentation

Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression. Despite being inherently continuous-time data, existing works have treated these as discrete-time sequences, disregarding their true nature. In this work, we model such data as continuous-time functions and learn compact representations by virtue of Neural Ordinary Differential Equations. To this end, we introduce the first continuous-time Seq2Seq model and demonstrate some remarkable properties that set it apart from traditional discrete-time analogues. We also provide solutions for some practical challenges for such models, including introducing a family of parameterized ODE dynamics & continuous-time data augmentation particularly suitable for the task. Our models are validated on several datasets including VectorMNIST, DiDi and Quick, Draw!.