pwc-archive/papers-with-abstracts huggingface dataset:
Neural Pooling for Graph Neural Networks
Authors: Sai Sree Harsha, Deepak Mishra
Date: 2021-01-01 00:00:00
Tasks: Classification, General Classification, Graph Classification

Tasks such as graph classification, require graph pooling to learn graph-level representations from constituent node representations. In this work, we propose two novel methods using fully connected neural network layers for graph pooling, namely Neural Pooling Method 1 and 2. Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs. In addition, compared to existing graph pooling methods, our proposed methods are able to capture information from all nodes, collect second-order statistics, and leverage the ability of neural networks to learn relationships among node representations, making them more powerful. We perform experiments on graph classification tasks in the bio-informatics and social network domains to determine the effectiveness of our proposed methods. Experimental results show that our methods lead to an absolute increase of upto 1.2% in classification accuracy over previous works and a general decrease in standard deviation across multiple runs indicating greater reliability. Experimental results also indicate that this improvement in performance is consistent across several datasets.
Robust early-learning: Hindering the memorization of noisy labels
Authors: Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, ZongYuan Ge, Yi Chang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Image Classification, Learning with noisy labels, Memorization

The \textit{memorization effects} of deep networks show that they will first memorize training data with clean labels and then those with noisy labels. The \textit{early stopping} method therefore can be exploited for learning with noisy labels. However, the side effect brought by noisy labels will influence the memorization of clean labels before early stopping. In this paper, motivated by the \textit{lottery ticket hypothesis} which shows that only partial parameters are important for generalization, we find that only partial parameters are important for fitting clean labels and generalize well, which we term as \textit{critical parameters}; while the other parameters tend to fit noisy labels and cannot generalize well, which we term as \textit{non-critical parameters}. Based on this, we propose \textit{robust early-learning} to reduce the side effect of noisy labels before early stopping and thus enhance the memorization of clean labels. Specifically, in each iteration, we divide all parameters into the critical and non-critical ones, and then perform different update rules for different types of parameters. Extensive experiments on benchmark-simulated and real-world label-noise datasets demonstrate the superiority of the proposed method over the state-of-the-art label-noise learning methods.
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning
Authors: Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Dumitru Erhan, Harini Kannan, Chelsea Finn, Sergey Levine
Date: 2021-01-01 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to predict the environment dynamics and expected reward from interaction and use this predictive model to plan and perform the task. However, MBRL methods vary in their fundamental design choices, and it there is no strong consensus in the literature on how these design decisions affect performance. In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. A big exception to this finding is that predicting future observations (i.e., images) leads to significant task performance improvement compared to only predicting rewards. We also empirically find that image prediction accuracy, somewhat surprisingly, correlates more strongly with downstream task performance than reward prediction accuracy. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks (that require exploration) will perform the same as the best-performing models when trained on the same training data. Simultaneously, in the absence of exploration, models that fit the data better usually perform better on the down-stream task as well, but surprisingly, these are often not the same models that perform the best when learning and exploring from scratch. These findings suggest that performance and exploration place important and potentially contradictory requirements on the model.
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation
Authors: Yingjie Fei, Zhuoran Yang, Zhaoran Wang
Date: 2021-01-01 00:00:00
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning (RL)

We study risk-sensitive reinforcement learning with the entropic risk measure and function approximation. We consider the finite-horizon episodic MDP setting, and propose a meta algorithm based on value iteration. We then derive two algorithms for linear and general function approximation, namely RSVI.L and RSVI.G, respectively, as special instances of the meta algorithm. We illustrate that the success of RSVI.L depends crucially on carefully designed feature mapping and regularization that adapt to risk sensitivity. In addition, both RSVI.L and RSVI.G maintain risk-sensitive optimism that facilitates efficient exploration. On the analytic side, we provide regret analysis for the algorithms by developing a meta analytic framework, at the core of which is a risk-sensitive optimism condition. We show that any instance of the meta algorithm that satisfies the condition yields a meta regret bound. We further verify the condition for RSVI.L and RSVI.G under respective function approximation settings to obtain concrete regret bounds that scale sublinearly in the number of episodes.
Auto-view contrastive learning for few-shot image recognition
Authors: Xu Luo, Yuxuan Chen, Liangjian Wen, Lili Pan, Zenglin Xu
Date: 2021-01-01 00:00:00
Tasks: Contrastive Learning, Few-Shot Learning, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Meta-Learning

Few-shot learning aims to recognize new classes with few annotated instances within each category. Recently, metric-based meta-learning approaches have shown the superior performance in tackling few-shot learning problems. Despite their success, existing metric-based few-shot approaches often fail to push the fine-grained sub-categories apart in the embedding space given no fine-grained labels. This may result in poor generalization to fine-grained sub-categories, and thus affects model interpretation. To alleviate this problem, we introduce contrastive loss into few-shot classification for learning latent fine-grained structure in the embedding space. Furthermore, to overcome the drawbacks of random image transformation used in current contrastive learning in producing noisy and inaccurate image pairs (i.e., views), we develop a learning-to-learn algorithm to automatically generate different views of the same image. Extensive experiments on standard few-shot learning benchmarks and few-shot fine-grained image classification demonstrate the superiority of our method.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
Authors: Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez
Date: 2021-01-01 00:00:00
Tasks: Attribute, counterfactual, Decision Making

Explainability of black-box predictive models  has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail. Counterfactual methods, on the other hand, indicate how to perturb the input to change the model prediction, providing details about the model's decision-making. Unfortunately, current counterfactual methods make ambiguous interpretations as they combine multiple biases of the model and the data in a single counterfactual interpretation of the model's decision. Moreover, these methods tend to generate trivial counterfactuals about the model's decision, as they often suggest to exaggerate or remove the presence of the attribute being classified. Trivial counterfactuals are usually not valuable, since the information they provide is often already known to the system's designer. In this work, we propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. We will make the code publicly available on github.
Learning to Generate the Unknowns for Open-set Domain Adaptation
Authors: Mahsa Baktashmotlagh, Tianle Chen, Mathieu Salzmann
Date: 2021-01-01 00:00:00
Tasks: Domain Adaptation

In many situations, the data one has access to at test time follows a different distribution from the training data. Over the years, this problem has been tackled by closed-set domain adaptation techniques. Recently, open-set domain adaptation has emerged to address the more realistic scenario where additional unknown classes are present in the target data. In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes. Here, we propose a simpler and more effective solution consisting of complementing the source data distribution and making it comparable to the target one by enabling the model to generate source samples corresponding to the unknown target classes. To this end, we attach a generative model to a standard domain adaptation network and augment the source data with the generated samples before matching the source distribution to the target one, thus avoiding negative transfer between the domains. We formulate this as a general module that can be incorporated into any existing closed-set approach and show that this strategy allows us to outperform the state of the art on standard open-set domain adaptation benchmark datasets.
XT2: Training an X-to-Text Typing Interface with Online Learning from Implicit Feedback
Authors: Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca Dragan, Sergey Levine
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1

We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that capture eye gaze. Standard methods train a model on a fixed dataset of user inputs, then deploy a static interface that does not learn from its mistakes; in part, because extracting an error signal from user behavior can be challenging. We investigate a simple idea that would enable such interfaces to improve over time, with minimal additional effort from the user: online learning from implicit user feedback on the accuracy of the interface's actions. In the typing domain, we leverage backspaces as implicit feedback that the interface did not perform the desired action. We propose an algorithm called x-to-text (XT2) that trains a predictive model of this implicit feedback signal, and uses this model to fine-tune any existing, default interface for translating user input into actions that select words or characters. We evaluate XT2 through a small-scale online user study with 12 participants who type sentences by gazing at their desired words, and a large-scale observational study on handwriting samples from 60 users. The results show that XT2 learns to outperform a non-adaptive default interface, stimulates user co-adaptation to the interface, personalizes the interface to individual users, and can leverage offline data collected from the default interface to improve its initial performance and accelerate online learning.
SEMANTIC APPROACH TO AGENT ROUTING USING A HYBRID ATTRIBUTE-BASED RECOMMENDER SYSTEM
Authors: Anwitha Paruchuri
Date: 2021-01-01 00:00:00
Tasks: Attribute, Management, Recommendation Systems

Traditionally contact centers route an issue to an agent based on ticket load or skill of the agent. When a ticket comes into the system, it is either manually analyzed and pushed to an agent or automatically routed to an agent based on some business rules. A Customer Relationship Management (CRM) system often has predefined categories that an issue could belong to. The agents are generally proficient in handling multiple categories, the categories in the CRM system are often related to each other, and a ticket typically contains content across multiple categories. This makes the traditional approach sub-optimal. We propose a Hybrid Recommendation based approach that recommends top N agents for a ticket by jointly modelling on the interactions between the agents and categories as well as on the semantic features of the categories and the agents.
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning
Authors: Chenjia Bai, Lingxiao Wang, Peng Liu, Zhaoran Wang, Jianye Hao, Yingnan Zhao
Date: 2021-01-01 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Efficient Exploration, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimism in the face of uncertainty is a principled approach for provably efficient exploration for reinforcement learning in tabular and linear settings. However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL). To address this problem, we propose an Optimistic Exploration algorithm with Backward Bootstrapped Bonus (OEB3) for DRL by following these two principles. OEB3 is built on bootstrapped deep $Q$-learning, a non-parametric posterior sampling method for temporally-extended exploration. Based on such a temporally-extended exploration, we construct an UCB-bonus indicating the uncertainty of $Q$-functions. The UCB-bonus is further utilized to estimate an optimistic $Q$-value, which encourages the agent to explore the scarcely visited states and actions to reduce uncertainty. In the estimation of $Q$-function, we adopt an episodic backward update strategy to propagate the future uncertainty to the estimated $Q$-function consistently. Extensive evaluations show that OEB3 outperforms several state-of-the-art exploration approaches in Mnist maze and 49 Atari games.
A Simple Sparse Denoising Layer for Robust Deep Learning
Authors: Yueming Lyu, Xingrui Yu, Ivor Tsang
Date: 2021-01-01 00:00:00
Tasks: Deep Learning, Denoising, Dictionary Learning, Reinforcement Learning (RL)

Deep models have achieved great success in many applications. However, vanilla deep models are not well-designed against the input perturbation.   In this work, we take an initial step to designing a simple robust layer as a lightweight plug-in for vanilla deep models.  To achieve this goal, we first propose a fast sparse coding and dictionary learning algorithm for sparse coding problem with an exact $k$-sparse constraint or  $l_0$ norm regularization. Our method comes with a closed-form solution for the sparse coding phase by taking advantage of a novel structured dictionary.   With this handy solution,  we propose a simple sparse denoising layer (SDL) as a lightweight robust plug-in.   Extensive experiments on both classification and reinforcement learning tasks manifest the effectiveness of our methods.
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
Authors: Yong liu, Jiankun Liu, Shuqiang Wang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Generalization Bounds

In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds. Specifically, we first show that the simple combination of divide-and-conquer technique and random features can achieve the same statistical accuracy as the exact KRR in expectation requiring only $\mathcal{O}(|\mathcal{D}|)$ memory and $\mathcal{O}(|\mathcal{D}|^{1.5})$ time. Then, beyond the generalization bounds in expectation that demonstrate the average information for multiple trails, we derive generalization bounds in probability to capture the learning performance for a single trail. Finally, we propose an effective communication strategy to further improve the performance of DKRR-RF, and validate the theoretical bounds via numerical experiments.
Better Together: Resnet-50 accuracy with $13 \times $ fewer parameters and at $3 \times $ speed
Authors: Utkarsh Nath, Shrinu Kushagra
Date: 2021-01-01 00:00:00
Tasks: One-Shot Learning

Recent research on compressing deep neural networks has focused on reducing the number of parameters. Smaller networks are easier to export and deploy on edge-devices. We introduce Adjoined networks as a training approach that can regularize and compress any CNN-based neural architecture. Our one-shot learning paradigm trains both the original and the smaller networks together. The parameters of the smaller network are shared across both the architectures. We prove strong theoretical guarantees on the regularization behavior of the adjoint training paradigm. We complement our theoretical analysis by an extensive empirical evaluation of both the compression and regularization behavior of adjoint networks. For resnet-50 trained adjointly on Imagenet, we are able to achieve a $13.7 \times$ reduction in the number of parameters and a $3 \times$ improvement in inference time without any significant drop in accuracy. For the same architecture on CIFAR-100, we are able to achieve a $99.7 \times$ reduction in the number of parameters and a $5 \times$ improvement in inference time. On both these datasets, the original network trained in the adjoint fashion gains about $3\%$ in top-1 accuracy as compared to the same network trained in the standard fashion.
What Can Phase Retrieval Tell Us About Private Distributed Learning?
Authors: Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Retrieval

In this work, we examine the security of InstaHide, a scheme recently proposed by \cite{hsla20} for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and randomly flips the sign of each entry of the resulting vector with probability 1/2. A salient question is whether this scheme is secure in any provable sense, perhaps under a plausible complexity-theoretic assumption. 

The answer to this turns out to be quite subtle and closely related to the average-case complexity of a multi-task, missing-data version of the classic problem of phase retrieval that is interesting in its own right. Motivated by this connection, under the standard distributional assumption that the public/private feature vectors are isotropic Gaussian, we design an algorithm that can actually recover a private vector using only the public vectors and a sequence of synthetic vectors generated by InstaHide.
Graph Neural Network Acceleration via Matrix Dimension Reduction
Authors: Shunhua Jiang, Yunze Man, Zhao Song, Danyang Zhuo
Date: 2021-01-01 00:00:00
Tasks: Dimensionality Reduction, Graph Neural Network

Graph Neural Networks (GNNs) have become the de facto method for machine learning on graph data (e.g., social networks, protein structures, code ASTs), but they require significant time and resource to train. One alternative method is Graph Neural Tangent Kernel (GNTK), a kernel method that corresponds to infinitely wide multi-layer GNNs. GNTK's parameters can be solved directly in a single step, avoiding time-consuming gradient descent. Today, GNTK is the state-of-the-art method to achieve high training speed without compromising accuracy. Unfortunately, solving for the kernel and searching for parameters can still take hours to days on real-world graphs. The current computation of GNTK has running time $O(N^4)$, where $N$ is the number of nodes in the graph. This prevents GNTK from scaling to datasets that contain large graphs. Theoretically, we present two techniques to speed up GNTK training while preserving the generalization error: (1) We use a novel matrix decoupling method to reduce matrix dimensions during the kernel solving. This allows us to reduce the dominated computation bottleneck term from $O(N^4)$ to $O(N^3)$. (2)  We apply sketching to further reduce the bottleneck term to $o(N^{\omega})$, where $\omega \approx 2.373$ is the exponent of current matrix multiplication. Experimentally, we demonstrate that our approaches speed up kernel learning by up to $19\times$ on real-world benchmark datasets.
Intra-layer Neural Architecture Search
Authors: Dong Kai Wang, Nam Sung Kim
Date: 2021-01-01 00:00:00
Tasks: Multi-Task Learning, Neural Architecture Search

We propose an efficient neural architecture search (NAS) algorithm with a flexible search space that encompasses layer operations down to individual weights. This work addresses NAS challenges in a search space of weight connections within layers, specifically the large number of architecture variations compared to a high-level search space with predetermined layer types. Our algorithm continuously evolves network architecture by adding new candidate parameters (weights and biases) using a first-order estimation based on their gradients at 0. Training is decoupled into alternating steps: adjusting network weights holding architecture constant, and adjusting network architecture holding weights constant. We explore additional applications by extend this method for multi-task learning with shared parameters. On the CIFAR-10 dataset, our evolved network achieves an accuracy of 97.42\% with 5M parameters, and 93.75\% with 500K parameters. On the ImageNet dataset, we achieve 76.6\% top-1 and 92.5\% top-5 accuracy with a search restriction of 8.5M parameters.