GenAI4ELab/papercli-papers huggingface dataset:
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
Authors: Tennison Liu; Alex Chan; Boris van Breugel; Mihaela van der Schaar
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused on the protection of single attributes in the simpler setting where both attributes and target outcomes are binary. However, the practical application in many a real-world problem entails the simultaneous protection of multiple sensitive attributes, which are often not simply binary, but continuous or categorical. To address this more challenging task, we introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. This leads to two practical tools: first, the FairCOCCO Score, a normalized metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularization term that can be incorporated into arbitrary learning objectives to obtain fair predictors. These contributions address crucial gaps in the algorithmic fairness literature, and we empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
In Search of Smooth Minima for Purifying Backdoor in Deep Neural Networks
Authors: Nazmul Karim; Abdullah Al Arafat; Umar Khalid; Zhishan Guo; Nazanin Rahnavard
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The success of a deep neural network (DNN) heavily relies on the details of the
training scheme; e.g., training data, architectures, hyper-parameters, etc. Recent
backdoor attacks suggest that an adversary can take advantage of such training
details and compromise the integrity of DNN. Our studies show that a backdoor
model is usually optimized to a bad local minima, i.e., sharper minima as compared
to a benign model. Intuitively, backdoor can be purified by re-optimizing the
model to a smoother minima through fine-tuning with a few clean validation data.
However, fine-tuning all DNN parameters often requires huge computational costs as
well as sub-par clean test performance. To address this concern, we propose a novel
backdoor purification technique—N atural G radient Fine-tuning (NGF)—which
focuses on removing backdoor by fine-tuning only one layer. Specifically, NGF
utilizes a loss surface geometry-aware optimizer that can successfully overcome
the challenge of reaching a smooth minima under one-layer optimization scenario.
To enhance the generalization performance of our proposed method, we introduce
a clean data distribution-aware regularizer based on the knowledge of loss surface
curvature matrix, i.e., Fisher Information Matrix. To validate the effectiveness of
our method, we conduct extensive experimentation with four different datasets—
CIFAR10, GTSRB, Tiny-ImageNet, and ImageNet; as well as 11 recent backdoor
attacks, e.g., Blend, Dynamic, Clean Label, etc. NGF achieves state-of-the-art
performance in most of these benchmarks.
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
Authors: Adria Marcos Morales; Matan Leibovich; Sreyas Mohan; Joshua Lawrence Vincent; Piyush Haluai; MAI TAN; Peter Crozier; Carlos Fernandez-Granda
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Unsupervised denoising is a crucial challenge in real-world imaging applications. Unsupervised deep-learning methods have demonstrated impressive performance on benchmarks based on synthetic noise. However, no metrics are available to evaluate these methods in an unsupervised fashion. This is highly problematic for the many practical applications where ground-truth clean images are not available. In this work, we propose two novel metrics: the unsupervised mean squared error (MSE) and the unsupervised peak signal-to-noise ratio (PSNR), which are computed using only noisy data. We provide a theoretical analysis of these metrics, showing that they are asymptotically consistent estimators of the supervised MSE and PSNR. Controlled numerical experiments with synthetic noise confirm that they provide accurate approximations in practice. We validate our approach on real-world data from two imaging modalities:  videos in raw format and transmission electron microscopy. Our results demonstrate that the proposed metrics enable unsupervised evaluation of denoising methods based exclusively on noisy data.
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Authors: Mansheej Paul; Feng Chen; Brett W. Larsen; Jonathan Frankle; Surya Ganguli; Gintare Karolina Dziugaite
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: As neural networks get larger and costlier, it is important to find sparse networks that require less compute and memory but can be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matching subnetworks, known as winning tickets. IMP iterates through cycles of training, pruning a fraction of smallest magnitude weights, rewinding unpruned weights back to an early training point, and repeating. Despite its simplicity, the principles underlying when and how IMP finds winning tickets remain elusive. In particular, what useful information does an IMP mask found at the end of training convey to a rewound network near the beginning of training? How does SGD allow the network to extract this information? And why is iterative pruning needed, i.e. why can't we prune to very high sparsities in one shot? We investigate these questions through the lens of the geometry of the error landscape. First, we find that—at higher sparsities—pairs of pruned networks at successive pruning iterations are connected by a linear path with zero error barrier if and only if they are matching. This indicates that masks found at the end of training convey to the rewind point the identity of an axial subspace that intersects a desired linearly connected mode of a matching sublevel set. Second, we show SGD can exploit this information due to a strong form of robustness: it can return to this mode despite strong perturbations early in training. Third, we show how the flatness of the error landscape at the end of training limits the fraction of weights that can be pruned at each iteration of IMP. This analysis yields a new quantitative link between IMP performance and the Hessian eigenspectrum. Finally, we show that the role of retraining in IMP is to find a network with new small weights to prune. Overall, these results make progress toward demystifying the existence of winning tickets by revealing the fundamental role of error landscape geometry in the algorithms used to find them.
Synergistic Neuromorphic Federated Learning with ANN-SNN Conversion For Privacy Protection
Authors: Yingni Chen; Shikuang Deng; Yuhang Li; Xin Dong; Shi Gu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated Learning (FL) has been widely explored for the growing public data privacy issues, where only model parameters are communicated instead of private data. However, recent studies debunk the privacy protection of FL, showing that private data can be leaked from the communicated gradients or parameters updates. In this paper, we propose a framework called Synergistic Neuromorphic Federated Learning (SNFL) that enhances privacy during FL. Before uploading the updates of the client model, SNFL first converts clients' Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) via calibration algorithms. In a way that not only loses almost no accuracy but also encrypts the client model's parameters, SNFL manages to obtain a more performant model with high privacy. After aggregation of various SNNs parameters, server distributes the parameters back to clients to continue training under ANN architecture, providing smooth convergence.  The proposed framework is demonstrated to be private, introducing a lightweight overhead as well as yielding prominent performance boosts. Extensive experiments with different kinds of datasets have demonstrated the efficacy and practicability of our method. In most of our experimental IID and not extreme Non-IID scenarios, the SNFL technique has significantly enhanced the model performance.  For instance, SNFL improve the accuracy of FedAvg on Tiny-ImageNet by 13.79%. In the IID situation of tiny-ImageNet, for instance, the SNFL method is 13.79% more accurate than FedAvg.  Also, the original image cannot be reconstructed after 280 iterations of attacks with the SNFL method, whereas it can be reconstructed after just 70 iterations with FedAvg.
Local Coefficient Optimization in Federated Learning
Authors: Congliang Chen; Chi Zhang; Bingzhe Wu; Yatao Bian; Zhi-Quan Luo; Peilin Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning emerges as a promising approach to build a large-scale cooperative learning system among multiple clients without sharing their raw data. However, given a specific global objective, finding the optimal sampling weights for each client remains largely unexplored. This is particularly challenging when clients' data distributions are non-i.i.d. and clients partially participate.

In this paper, we model the above task as a bi-level optimization problem which takes the correlations among different clients into account. We present a double-loop primal-dual-based algorithm to solve the bi-level optimization problem. We further provide rigorous convergence analysis for our algorithm under mild assumptions. Finally, we perform extensive empirical studies under both toy examples and learning models from real datasets to verify the effectiveness of the proposed method.
Hazard Gradient Penalty for Survival Analysis
Authors: Seungjae Jung; Kyung-Min Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Survival analysis appears in various fields such as medicine, economics, engineering, and business.
Recent studies showed that the Ordinary Differential Equation (ODE) modeling framework integrates many existing survival models while the framework is flexible and widely applicable.
However, naively applying the ODE framework to survival analysis problems may model fiercely changing density function with respect to covariates which may worsen the model’s performance.
Though we can apply L1 or L2 regularizers to the ODE model, their effect on the ODE modeling framework is barely known.
In this paper, we propose hazard gradient penalty (HGP) to enhance the performance of a survival analysis model.
Our method imposes constraints on local data points by regularizing the gradient of hazard function with respect to the data point.
Our method applies to any survival analysis model including the ODE modeling framework and is easy to implement.
We theoretically show that our method is related to minimizing the KL divergence between the density function at a data point and that of the neighborhood points. 
Experimental results on three public benchmarks show that our approach outperforms other regularization methods.
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Authors: Yifu Yuan; Jianye HAO; Fei Ni; Yao Mu; YAN ZHENG; Yujing Hu; Jinyi Liu; Yingfeng Chen; Changjie Fan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on the pre-training in a model-free manner while lacking the study of transition dynamics modeling that leaves a large space for the improvement of sample efficiency in downstream tasks. To this end, we propose an Efficient Unsupervised Reinforcement Learning Framework with Multi-choice Dynamics model (EUCLID), which introduces a novel model-fused paradigm to jointly pre-train the dynamics model and unsupervised exploration policy in the pre-training phase, thus better leveraging the environmental samples and improving the downstream task sampling efficiency. However, constructing a generalizable model which captures the local dynamics under different behaviors remains a challenging problem. We introduce the multi-choice dynamics model that covers different local dynamics under different behaviors concurrently, which uses different heads to learn the state transition under different behaviors during unsupervised pre-training and selects the most appropriate head for prediction in the downstream task. Experimental results in the manipulation and locomotion domains demonstrate that EUCLID achieves state-of-the-art performance with high sample efficiency, basically solving the state-based URLB benchmark and reaching a mean normalized score of 104.0±1.2% in downstream tasks with 100k fine-tuning steps, which is equivalent to DDPG’s performance at 2M interactive steps with 20× more data. More visualization videos are released on our homepage.
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
Authors: Yilmazcan Ozyurt; Stefan Feuerriegel; Ce Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framework, we further capture the variation in the contextual representations between source and target domain via a custom nearest-neighbor contrastive learning. To the best of our knowledge, ours is the first framework to learn domain-invariant, contextual representation for UDA of time series data. We evaluate our framework using a wide range of time series datasets to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA.
Conditional Invariances for Conformer Invariant Protein Representations
Authors: Balasubramaniam Srinivasan; Vassilis N. Ioannidis; Soji Adeshina; Mayank Kakodkar; George Karypis; Bruno Ribeiro
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Representation learning for proteins is an emerging area in geometric deep learning. Recent works have factored in both the relational (atomic bonds) and the geometric aspects (atomic positions) of the task, notably bringing together graph neural networks (GNNs) with neural networks for point clouds. The equivariances and invariances to geometric transformations (group actions such as rotations and translations) so far treats large molecules as rigid structures. However, in many important settings, proteins can co-exist as an ensemble of multiple stable conformations. The conformations of a protein, however, cannot be described as input-independent transformations of the protein: Two proteins may require different sets of transformations in order to describe their set of viable conformations. To address this limitation, we introduce the concept of conditional transformations (CT). CT can capture protein structure, while respecting the restrictions posed by constraints on dihedral (torsion) angles and steric repulsions between atoms. We then introduce a Markov chain Monte Carlo framework to learn representations that are invariant to these conditional transformations. Our results show that endowing existing baseline models with these conditional transformations helps improve their performance without sacrificing computational cost.
Relative Positional Encoding Family via Unitary Transformation
Authors: Zhen Qin; Weixuan Sun; Kaiyue Lu; Hui Deng; Dongxu Li; XiaoDong Han; Yuchao Dai; Lingpeng Kong; Yiran Zhong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Relative position encoding is widely used in vanilla and linear transformers to represent positional information. However, the existing encoding methods of a vanilla transformer are not always directly applicable to a linear transformer, because the latter requires a decomposition of the query and key representations into separate kernel functions. Nevertheless, principles to design encoding methods suitable for linear transformers remain under-studied. In this work, we put together a variety of existing encoding methods under a canonical form and further propose a family of relative positional encodings via unitary transformation. Our formulation leads to a principled framework that can be used to develop new relative positional encoding methods that preserve linear space-time complexity. Equipping with different parameters, the proposed unitary relative positional encoding family (URPE) derives effective encoding for various applications. Experiments show that compared with existing encoding methods, unitary encoding achieves competitive performance on language modeling and various challenging downstream tasks, such as machine translation and text classification. In the meantime, it highlights a general paradigm to design broadly more relative positional encoding methods, applicable inclusively to linear and vanilla transformers.
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Authors: Jun CEN; Di Luan; Shiwei Zhang; Yixuan Pei; Yingya Zhang; Deli Zhao; Shaojie Shen; Qifeng Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the uncertainty distribution of almost all these methods is actually closer to the expectation of UOSR than OSR. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings.
Expected Perturbation Scores for Adversarial Detection
Authors: Shuhai Zhang; Feng Liu; Jiahao Yang; Yifan Yang; Bo Han; Mingkui Tan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in the high-dimension space. Recently, the gradients of log probability density (a.k.a., score) w.r.t. samples is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we can perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two distributions under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPS-AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. To verify the validity of our proposed method, we also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Empirical studies on CIFAR-10 and ImageNet across different network architectures including ResNet, WideResNet, and ViT show the superior adversarial detection performance of EPS-AD compared to existing methods.
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
Authors: Kun Zhou; Xiao Liu; Yeyun Gong; Xin Zhao; Daxin Jiang; Nan Duan; Ji-Rong Wen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Dense retrieval aims to map queries and passages into low-dimensional vector space for efficient similarity measuring, showing promising effectiveness in various large-scale retrieval tasks. Since most existing methods commonly adopt pre-trained Transformers (\eg BERT) for parameter initialization, some work focuses on proposing new pre-training tasks for compressing the useful semantic information from passages into dense vectors, achieving remarkable performances. However, it is still challenging to effectively capture the rich semantic information and relations about passages into the dense vectors via one single particular pre-training task. In this work, we propose a multi-task pre-trained model, MASTER, that unifies and integrates multiple pre-training tasks with different learning objectives under the bottlenecked masked autoencoder architecture. Concretely, MASTER utilizes a multi-decoder architecture to integrate three types of pre-training tasks: corrupted passages recovering, related passage recovering and PLMs outputs recovering. By incorporating a shared deep encoder, we construct a representation bottleneck in our architecture, compressing the abundant semantic information across tasks into dense vectors. The first two types of tasks concentrate on the semantic information of passages and capturing relationships among them within the pre-training corpus. The third can capture the knowledge beyond the corpus from external PLMs (\eg GPT-2). Extensive experiments on several large-scale passage retrieval datasets have shown that our approach outperforms the previous state-of-the-art dense retrieval methods.