pwc-archive/papers-with-abstracts huggingface dataset:
Towards Efficient On-Chip Training of Quantum Neural Networks
Authors: Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, Song Han
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification

Quantum Neural Network (QNN) is drawing increasingly more research interest thanks to its potential to achieve quantum advantage on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable QNN learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain QNN gradients is parameter shift whose cost scales linearly with the number of qubits. This work presents the first experimental demonstration of practical on-chip QNN training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrade the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments on 5 classification tasks with 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% \qnn accuracy improvements over no pruning. Overall, we successfully obtain comparable accuracy with noise-free simulation but have much better training scalability. We also open-source our PyTorch library for on-chip \qnn training with parameters shift and easy deployment at this link: https://anonymous.4open.science/r/iclr-on-chip-qnn-572E .
Restricted Category Removal from Model Representations using Limited Data
Authors: Pratik Mazumder, Pravendra Singh, Mohammed Asad Karim
Date: 2021-09-29 00:00:00

Deep learning models are trained on multiple categories jointly to solve several real-world problems. However, there can be cases where some of the classes may become restricted in the future and need to be excluded after the model has already been trained on them (Class-level Privacy). It can be due to privacy, ethical or legal concerns. A naive solution is to simply train the model from scratch on the complete training data while leaving out the training samples from the restricted classes (FDR - full data retraining). But this can be a very time-consuming process. Further, this approach will not work well if we no longer have access to the complete training data and instead only have access to very few training data. The objective of this work is to remove the information about the restricted classes from the network representations of all layers using limited data without affecting the prediction power of the model for the remaining classes. Simply fine-tuning the model on the limited available training data for the remaining classes will not be able to sufficiently remove the restricted class information, and aggressive fine-tuning on the limited data may also lead to overfitting. We propose a novel solution to achieve this objective that is significantly faster ($\sim200\times$ on ImageNet) than the naive solution. Specifically, we propose a novel technique for identifying the model parameters that are mainly relevant to the restricted classes. We also propose a novel technique that uses the limited training data of the restricted classes to remove the restricted class information from these parameters and uses the limited training data of the remaining classes to reuse these parameters for the remaining classes. The model obtained through our approach behaves as if it was never trained on the restricted classes and performs similar to FDR (which needs the complete training data). We also propose several baseline approaches and compare our approach with them in order to demonstrate its efficacy.
Deep Semi-Supervised 3D Shape Reconstruction by Solving a Poisson Equation with Spectral Methods
Authors: Diego Patino, Carlos Esteves, Kostas Daniilidis
Date: 2021-09-29 00:00:00
Tasks: 3D Shape Reconstruction

In this paper we propose a deep learning method for unsupervised 3D implicit shape reconstruction from point clouds. Our goal is to approximate 3D shapes as the iso-surface of a scalar field that is the solution of a Poisson partial differential equation. We propose neural network architecture that learns the distance field in the Fourier domain, and solve the PDE by using spectral differentiation through two novel loss functions. Our experiments show that our architecture can efficiently learn the Fourier coefficients while accurately estimating the target distance field. We train our models without any ground truth mesh, scalar distance field values, or surface normals.
Towards Achieving Adversarial Robustness Beyond Perceptual Limits
Authors: Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan, Venkatesh Babu Radhakrishnan
Date: 2021-09-29 00:00:00
Proceeding: ICML Workshop AML 2021 7
Tasks: Adversarial Defense, Adversarial Robustness

The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim towards defending attacks constrained within low magnitude $\ell_p$ norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to achieve adversarial robustness within larger bounds, against perturbations that may be perceptible, but do not change human (or Oracle) prediction. The presence of images that flip Oracle predictions and those that do not, makes this a challenging setting for adversarial robustness. We discuss the ideal goals of an adversarial defense algorithm beyond perceptual limits, and further highlight the shortcomings of naively extending existing training algorithms to higher perturbation bounds. In order to overcome these shortcomings, we propose a novel defense, Oracle-Aligned Adversarial Training (OA-AT), to align the predictions of the network with that of an Oracle during adversarial training. The proposed approach achieves state-of-the-art performance at large epsilon bounds (such as an $\ell_\infty$ bound of $16/255$ on CIFAR-10) while outperforming existing defenses (AWP, TRADES and PGD-AT) at standard perturbation bounds ($8/255$) as well.
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
Authors: Hiroki Naganuma, Taiji Suzuki, Rio Yokota, Masahiro Nomura, Kohta Ishikawa, Ikuro Sato
Date: 2021-09-29 00:00:00
Tasks: Hyperparameter Optimization

Generalization measures are intensively studied in the machine learning community for better modeling generalization gaps.  However, establishing a reliable generalization measure for statistical singular models such as deep neural networks (DNNs) is challenging due to the complex nature of the singular models. 
We focus on a classical measure called Takeuchi's Information Criteria (TIC) to investigate allowed conditions in which the criteria can well explain generalization gaps caused by DNNs. In fact, theory indicates the applicability of TIC near the neural tangent kernel (NTK) regime.
Experimentally, we trained more than 5,000 DNN models with 12 DNN architectures including large models (e.g., VGG16) and 4 datasets, and estimated corresponding TICs in order to comprehensively study the relationship between the generalization gap and the TIC estimates. 
We examine several approximation methods to estimate TIC with feasible computational load and investigate the accuracy trade-off. Experimental results indicate that estimated TIC well correlates generalization gaps under the conditions that are close to NTK regime. Outside the NTK regime, such correlation disappears, shown theoretically and empirically. We further demonstrate that TIC can yield better trial pruning ability for hyperparameter optimization over existing methods.
Invariant Learning with Partial Group Labels
Authors: Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister
Date: 2021-09-29 00:00:00

Learning invariant representations is an important requirement in training machine learning models that are driven by spurious correlations in the datasets. These spurious correlations, between input samples and the target labels, wrongly direct the neural network predictions resulting in poor performance on certain groups, especially the minority groups. Robust training against these spurious correlations requires the knowledge of group membership for every sample. Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information. On the other hand, the presence of such data collection efforts result in datasets that contain partially labelled group information. Recent works have tackled the fully unsupervised scenario where no labels for groups are available. Thus, we aim to fill the missing gap in the literature by tackling a more realistic setting that can leverage partially available sensitive or group information during training. First, we construct a constraint set and derive a high probability bound for the group assignment to belong to the set. Second, we propose an algorithm that optimizes for the worst-off group assignments from the constraint set. Through experiments on image and tabular datasets, we show improvements in the minority group’s performance while preserving overall aggregate accuracy across groups.
Rethinking Temperature in Graph Contrastive Learning
Authors: Ziyang Liu, Hao Feng, Chaokun Wang
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Inductive Learning, Self-Supervised Learning

Due to not relying on the rare human-labeled information, self-supervised learning, especially contrastive learning, attracted much attention from researchers. It has begun to show its strong advantages on both IID data (independent and identically distributed data, such as images and texts) and Non-IID data (such as nodes in graphs). Recently, researchers begin to explore the interpretability of contrastive learning and have proposed some metrics for measuring the learned representations' qualities of IID data, such as alignment, uniformity, and semantic closeness. It is very important to understand the relationships among node representations, which is helpful to design algorithms with stronger interpretability. However, few studies focus on evaluating good node representations in graph contrastive learning. In this paper, we investigate and discuss what a good representation should be for a general loss (InfoNCE) in graph contrastive learning. By theoretical analysis, we argue that global uniformity and local separation are both necessary to the learning quality. We find that the two new metrics can be regulated by the temperature coefficient in InfoNCE loss. Based on this characteristic, we develop a simple but effective algorithm GLATE to dynamically adjust the temperature value in the training phase. GLATE outperforms the state-of-the-art graph contrastive learning algorithms 2.8 and 0.9 percent on average under the transductive and inductive learning tasks, respectively. The code is available at: https://github.com/anonymousICLR22/GLATE.
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.
Authors: Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
Date: 2021-09-29 00:00:00
Tasks: continuous-control, Continuous Control, D4RL, Offline RL, reinforcement-learning, Reinforcement Learning (RL)

In order to achieve strong performance in offline reinforcement learning (RL),  it is necessary to act conservatively with respect to confident lower-bounds on anticipated values of actions. Thus, a valuable approach would be to obtain high quality uncertainty estimates on action values. In current supervised learning literature, state-of-the-art approaches to uncertainty estimation and calibration rely on ensembling methods. In this work, we aim to transfer the success of ensembles from supervised learning to the setting of batch RL. We propose, MSG, a model-free dynamic programming based offline RL method that trains an ensemble of independent Q-functions, and updates a policy to act conservatively with respect to the uncertainties derived from the ensemble. Theoretically, by referring to the literature on infinite-width neural networks, we demonstrate the crucial dependence of the quality of uncertainty on the manner in which ensembling is performed, a phenomenon that arises due to the dynamic programming nature of RL and overlooked by existing offline RL methods. Our theoretical predictions are corroborated by pedagogical examples on toy MDPs, as well as empirical comparisons in benchmark continuous control domains. In the more challenging domains of the D4RL offline RL benchmark, MSG significantly surpasses highly well-tuned state-of-the-art methods in batch RL. Motivated by the success of MSG, we investigate whether efficient approximations to ensembles can be as effective. We demonstrate that while efficient variants outperform current state-of-the-art, they do not match MSG with deep ensembles. We hope our work engenders increased focus into deep network uncertainty estimation techniques directed for reinforcement learning.
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate
Authors: Sungyeob Han, Yeongmo Kim, Jungwoo Lee
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, image-classification, Image Classification

One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially.
The memory based continual learning stores a small subset of the data for previous tasks and applies various methods such as quadratic programming and sample selection.
Some memory-based approaches are formulated as a constrained optimization problem and rephrase constraints on the objective for memory as the inequalities on gradients.
However, there have been little theoretical results on the convergence of continual learning.
In this paper, we propose a theoretical convergence analysis of memory-based continual learning with stochastic gradient descent.
The proposed method called nonconvex continual learning (NCCL) adapts the learning rates of both previous and current tasks with the gradients.
The proposed method can achieve the same convergence rate as the SGD method for a single task when the catastrophic forgetting term which we define in the paper is suppressed at each iteration.
It is also shown that memory-based approaches inherently overfit to memory, which degrades the performance on previously learned tasks. Experiments show that the proposed algorithm improves the performance of continual learning over existing methods for several image classification tasks.
Privacy Auditing of Machine Learning using Membership Inference Attacks
Authors: Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Reza Shokri
Date: 2021-09-29 00:00:00
Tasks: BIG-bench Machine Learning

Membership inference attacks determine if a given data point is used for training a target model. Thus, this attack could be used as an auditing tool to quantify the private information that a model leaks about the individual data points in its training set. In the last five years, a variety of membership inference attacks against machine learning models are proposed, where each attack exploits a slightly different clue. Also, the attacks are designed under different implicit assumptions about the uncertainties that an attacker has to resolve. Thus attack success rates do not precisely capture the information leakage of models about their data, as they also reflect other uncertainties that the attack algorithm has (for example, about data distribution or characteristics of the target model). In this paper, we present a framework that can explain the implicit assumptions and also the simplifications made in the prior work. We also derive new attack algorithms from our framework that can achieve a high AUC score while also highlighting the different factors that affect their performance. Thus, our algorithms can be used as a tool to perform an accurate and informed estimation of privacy risk in machine learning models. We provide a thorough empirical evaluation of our attack strategies on various machine learning tasks trained on benchmark datasets.
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Authors: Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Out-of-Distribution Generalization, Representation Learning

Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant relationship with the target. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features and build an invariant predictor. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. We propose invariant Causal Representation Learning (iCaRL), an approach that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation (i.e., a set of latent variables encoding the data) given the target and the environment belongs to general exponential family distributions, i.e., a more flexible conditionally non-factorized prior that can actually capture complicated dependences between the latent variables. Based on this, we show that it is possible to identify the data representation up to simple transformations. We also prove that all direct causes of the target can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods.
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Authors: huan zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J Zico Kolter
Date: 2021-09-29 00:00:00
Tasks: Adversarial Attack, GPU

Strong adversarial attacks are important for evaluating the true robustness of deep neural networks. Most existing attacks find adversarial examples via searching in the input space, e.g., using gradient descent. In this work, we formulate an adversarial attack using a branch-and-bound (BaB) procedure on ReLU neural networks and search adversarial examples in the activation space corresponding to binary variables in a mixed integer programming (MIP) formulation. This attack formulation can be used to tackle hard instances where none of the existing adversarial attacks can succeed. Existing attacks using this formulation rely on generic solvers which cannot exploit the structure of neural networks and also cannot utilize GPU acceleration, so they are mostly limited to small networks and easy problem instances. To improve its scalability and practicability, we propose a top-down beam-search approach to quickly identify the subspace that may contain adversarial examples. The search utilizes the bound propagation based neural network verifiers on GPUs to rapidly evaluate a large number of searching regions, which is not possible in generic MIP solvers. Moreover, we exploit the fact that good candidates of adversarial examples can be easily found via gradient based attacks, and build an adversarial candidates pool to further guide the search in activation space via diving techniques. Additionally, any candidate adversarial examples found during the process are refined using a bottom-up large neighbourhood search (LNS) guided by the candidates pool. Our adversarial attack framework, BaB-Attack, opens up a new opportunity for designing novel adversarial attacks not limited to searching the input space, and enables us to borrow techniques from integer programming theory and neural network verification to build stronger attacks. In experiments, we can successfully generate adversarial examples for hard input instances where existing strong adversarial attacks fail, and outperform off-the-shelf MIP solver based attacks in both success rates and efficiency. Our results further close the gap between the upper bound of robust accuracy obtained by attacks and the lower bound obtained by verification.
Multi-Objective Online Learning
Authors: Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu
Date: 2021-09-29 00:00:00

This paper presents a systematic study of multi-objective online learning. We first formulate the framework of Multi-Objective Online Convex Optimization, which encompasses a novel multi-objective dynamic regret in the unconstrained max-min form. We show that it is equivalent to the regret commonly used in the zero-order multi-objective bandit setting and overcomes the problem that the latter is hard to optimize via first-order gradient-based methods. Then we propose the Online Mirror Multiple Descent algorithm with two variants, which computes the composite gradient using either the vanilla min-norm solver or a newly designed $L_1$-regularized min-norm solver. We further derive regret bounds of both variants and show that the $L_1$-regularized variant enjoys a lower bound. Extensive experiments demonstrate the effectiveness of the proposed algorithm and verify the theoretical advantage of the $L_1$-regularized variant.
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces
Authors: Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Stefano Ermon, Jiaming Song, Krzysztof Janowicz, Ni Lao
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification, Representation Learning, Self-Supervised Learning

Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions. However, most existing approaches do not leverage unlabeled data, which is crucial for use cases with limited labels. Furthermore, the availability of large-scale real-world GPS coordinate data demands representation and prediction at global scales. However, existing location encoding models assume that the input coordinates are in Euclidean space, which can lead to modeling errors due to distortions introduced when mapping coordinates from other manifolds (e.g., spherical surfaces) to Euclidean space. We introduceSphere2Vec, a location encoder, which can directly encode spherical coordinates while preserving spherical distances.Sphere2Vecis trained with a self-supervised learning framework which pre-trains deep location representations from unlabeled geo-tagged images with contrastive losses, and then fine-tunes to perform super-vised geographic object classification tasks.Sphere2Vecachieves the performances of state-of-the-art results on various image classification tasks ranging from species, Point of Interest (POI) facade, to remote sensing. The self-supervised pertaining significantly improves the performance ofSphere2Vecespecially when the labeled data is limited