GenAI4ELab/papercli-papers huggingface dataset:
Client-agnostic Learning and Zero-shot Adaptation for Federated Domain Generalization
Authors: Seunghan Yang; Seokeon Choi; Hyunsin Park; Sungha Choi; Simyung Chang; Sungrack Yun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated domain generalization (federated DG) aims to learn a client-agnostic global model from various distributed source domains and generalize the model to new clients in completely unseen domains. The main challenges of federated DG are the difficulty of building the global model with local client models from different domains while keeping data private and low generalizability to test clients, where data distribution deviates from those of training clients. To solve these challenges, we present two strategies: (1) client-agnostic learning with mixed instance-global statistics and (2) zero-shot adaptation with estimated statistics. In client-agnostic learning, we first augment local features by using data distribution of other clients via global statistics in the global model's batch normalization layers. This approach allows the generation of diverse domains by mixing local and global feature statistics while keeping data private. Local models then learn client-invariant representations by applying our client-agnostic objectives with the augmented data. Next, we propose a zero-shot adapter to help the learned global model to directly bridge a large domain gap between seen and unseen clients. At inference time, the adapter mixes instance statistics of a test input with global statistics that are vulnerable to distribution shift. With the aid of the adapter, the global model improves generalizability further by reflecting test distribution. We comprehensively evaluate our methods on several benchmarks in federated DG.
Distilling Cognitive Backdoor Patterns within an Image
Authors: Hanxun Huang; Xingjun Ma; Sarah Monazam Erfani; James Bailey
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the ``minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to the same model output (i.e., logits or deep features). The extracted pattern can help understand the cognitive mechanism of a model on clean vs. backdoor images and is thus called a \emph{Cognitive Pattern} (CP). Using CD and the distilled CPs, we uncover an interesting phenomenon of backdoor attacks: despite the various forms and sizes of trigger patterns used by different attacks, the CPs of backdoor samples are all surprisingly and suspiciously small. 
One thus can leverage the learned mask to detect and remove backdoor examples from poisoned training datasets. 
We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks.
We also show that CD can potentially be applied to help detect potential biases from face datasets.
Code is available at https://github.com/HanxunH/CognitiveDistillation.
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Authors: Jiachen Hu; Han Zhong; Chi Jin; Liwei Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract:     Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest.
IEDR: A Context-aware Intrinsic and Extrinsic Disentangled Recommender System
Authors: Yixin Su; Wei Jiang; Yunxiang Zhao; Fangquan Lin; Cheng Yang; Sarah Monazam Erfani; Junhao Gan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Intrinsic and extrinsic factors jointly affect users' decisions in item selection (e.g., click, purchase). Intrinsic factors reveal users' real interests and are invariant in different contexts (e.g., time, weather), whereas extrinsic factors can change w.r.t. different contexts. Analyzing these two factors is an essential yet challenging task in recommender systems. However, in existing studies, factor analysis is either largely neglected, or designed for a specific context (e.g., the time context in sequential recommendation), which limits the applicability of such models. In this paper, we propose a generic model, IEDR, to learn intrinsic and extrinsic factors from various contexts for recommendation. IEDR contains two key components: a contrastive learning component, and a disentangling component. The two components collaboratively enable our model to learn context-invariant intrinsic factors and context-based extrinsic factors from all available contexts. Experimental results on real-world datasets demonstrate the effectiveness of our model in factor learning and impart a significant improvement in recommendation accuracy over the state-of-the-art methods.
Neural Attention Memory
Authors: Hyoungwook Nam; Seung Byum Seo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Scaled dot-product attention has become the essence of state-of-the-art deep neural networks for various machine learning tasks. Though its ubiquitous accomplishments, it is inefficient for long sequence tasks and problematic for tasks requiring memory states such as compositional generalization. We propose a novel perspective of the attention mechanism by reinventing it as a memory architecture for neural networks, namely Neural Attention Memory (NAM). NAM follows the same query-key-value structure by constructing a memory matrix while reducing its computational complexity from quadratic to linear to the sequence length. NAM writes a memory matrix via the sum of outer products of value and unit key vectors, and reads it by multiplying the matrix with a unit query vector. Indeed, we show that our normalized outer-product attention mechanism is mathematically equivalent to the conventional attention mechanism. Then, we evaluate a NAM-based Transformer on long-range arena tasks and demonstrate its efficiency and efficacy. Finally, we propose two NAM-based memory-augmented neural networks, namely Long Short-Term Attention Memory (LSAM) and NAM Turing Machine (NAM-TM), and test their compositional generalization capability using four different tasks. LSAM replaces LSTM's long-term cell state with NAM memory matrix and NAM-TM implements a Turing tape data structure using NAM read/write primitives. The experimental results show that the proposed models outperform traditional Transformer and LSTM, as well as DNC. NAM opens up possibilities in diverse machine learning research problems, including hierarchical data modeling, efficient edge inference, and few-shot learning.
Inducing Gaussian Process Networks
Authors: Alessandro Tibo; Thomas Dyhre Nielsen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can also be non-trivial to determine, providing an additional obstacle to the learning task. Over the last decade, these challenges have resulted in significant advances being made in terms of scalability and expressivity, exemplified by, e.g., the use of inducing points and neural network kernel approximations.

In this paper, we propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains while also facilitating scalable gradient-based learning methods. 

We consider both regression and (binary) classification tasks and report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods. We also demonstrate how IGNs can be used to effectively model complex domains using neural network architectures.
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
Authors: Da-Wei Zhou; Qi-Wei Wang; Han-Jia Ye; De-Chuan Zhan
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist forgetting, while recent works find that storing models from history can substantially boost the performance. However, the stored models are not counted into the memory budget, which implicitly results in unfair comparisons. We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets. As a result, we need to holistically evaluate different CIL methods at different memory scales and simultaneously consider accuracy and memory size for measurement. On the other hand, we dive deeply into the construction of the memory buffer for memory efficiency. By analyzing the effect of different layers in the network, we find that shallow and deep layers have different characteristics in CIL. Motivated by this, we propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends specialized layers based on the shared generalized representations, efficiently extracting diverse representations with modest cost and maintaining representative exemplars. Extensive experiments on benchmark datasets validate MEMO's competitive performance. Code is available at: https://github.com/wangkiw/ICLR23-MEMO
Long-Tailed Learning Requires Feature Learning
Authors: Thomas Laurent; James von Brecht; Xavier Bresson
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose a simple data model inspired from natural data such as text or images, and use it to study the importance of learning features in order to achieve good generalization. Our data model follows a long-tailed distribution in the sense that some rare and uncommon subcategories have few representatives in the training set. In this context we provide evidence that a learner succeeds if and only if it identifies the correct features, and moreover derive non-asymptotic generalization error bounds that precisely quantify the penalty that one must pay for not learning features.
Augmentative Topology Agents For Open-Ended Learning
Authors: Muhammad Umair Nasir; Michael Beukman; Steven James; Christopher Wesley Cleghorn
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this work, we tackle the problem of Open-Ended Learning by a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous open-ended approaches that optimize agents using a fixed neural network topology, we hypothesize that generalization can be improved by allowing agents' controllers to become more complex as they encounter more difficult environments.  Our method, Augmentative Topology EPOET (ATEP), extends the Enhanced Paired Open-Ended Trailblazer (EPOET) algorithm by allowing agents to evolve their own neural network structures over time, adding complexity and capacity as necessary. Empirical results demonstrate that ATEP results in general agents capable of solving more environments than a fixed-topology baseline. We also investigate mechanisms for transferring agents between environments and find that a species-based approach further improves the performance and generalization of agents.
Bandwith Enables Generalization in Quantum Kernel Models
Authors: Abdulkadir Canatar; Evan Peters; Cengiz Pehlevan; Stefan M. Wild; Ruslan Shaydulin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been shown to provide an exponential speedup on a learning version of the discrete logarithm problem. Understanding the generalization of quantum models is essential to realizing similar speedups on practically interesting problems. Recent results demonstrate that generalization is hindered by the exponential size of the quantum feature space. Although these results suggest that quantum models cannot generalize when the number of qubits is large, in this paper we show that these results rely on overly restrictive assumptions. We consider a wider class of models by varying a hyperparameter that we call quantum kernel bandwidth. We analyze the large-qubit limit and provide explicit formulas for the generalization of a quantum model that can be solved in closed form. Specifically, we show that changing the value of bandwidth can take a model from provably not being able to generalize on any target function to good generalization for well-aligned targets. Our analysis shows how the bandwidth controls the spectrum of the kernel integral operator, and thereby the inductive bias of the model. We demonstrate empirically that our theory correctly predicts how varying the bandwidth affects generalization of quantum models on challenging datasets, including those far outside our theoretical assumptions. We discuss the implications of our results for quantum advantage in machine learning.
Verifying the Union of Manifolds Hypothesis for Image Data
Authors: Bradley CA Brown; Anthony L. Caterini; Brendan Leigh Ross; Jesse C Cresswell; Gabriel Loaiza-Ganem
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifold of low intrinsic dimension. In this paper, we argue that this hypothesis does not properly capture the low-dimensional structure typically present in image data. Assuming that data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation. To address this deficiency, we consider the union of manifolds hypothesis, which states that data lies on a disjoint union of manifolds of varying intrinsic dimensions. We empirically verify this hypothesis on commonly-used image datasets, finding that indeed, observed data lies on a disconnected set and that intrinsic dimension is not constant. We also provide insights into the implications of the union of manifolds hypothesis in deep learning, both supervised and unsupervised, showing that designing models with an inductive bias for this structure improves performance across classification and generative modelling tasks. Our code is available at https://github.com/layer6ai-labs/UoMH.
Treatment Effect Estimation with Collider Bias and Confounding Bias
Authors: Baohong Li; Kun Kuang; Ruoxuan Xiong; Fei Wu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: To answer causal questions from observational data, it is important to consider the mechanisms that determine which data values are observed and which are missing. Prior work has considered the treatment assignment mechanism and proposed methods to remove the confounding bias from the common causes of treatment and outcome. However, there are other issues in sample selection, commonly overlooked in prior work, that can bias the treatment effect estimation, such as the issue of censored outcome as a form of collider bias. In this paper, we propose the novel Selection Controlled CounterFactual Regression (SC-CFR) to simultaneously address confounding and collider bias. Specifically, we first calculate the magnitude of the collider bias of different instances by estimating the selection model and then add a control term to remove the collider bias while learning a balanced representation to remove the confounding bias when estimating the outcome model. Our method is shown to provide unbiased treatment effect estimates from observational data with confounding and collider bias. Extensive empirical results on both synthetic and real-world datasets show that our method consistently outperforms benchmarks when both types of biases exist.
FedGSNR: Accelerating Federated Learning on Non-IID Data via Maximum Gradient Signal to Noise Ratio
Authors: Qi Tan; Yi Zhao; Ke Xu; Qi Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning (FL) allows participants jointly training a model without direct data sharing. In such a process, participants rather than the central server perform local updates of stochastic gradient descent (SGD) and the central server aggregates the gradients from the participants to update the global model. However, the non-iid training data in participants significantly impact global model convergence.Most of existing studies addressed this issue by utilizing variance reduction or regularization. However, these studies focusing on specific datasets lack theoretical guarantee for efficient model training. In this paper, we provide a novel perspective on the non-iid issue by optimizing Gradient Signal to Noise Ratio (GSNR) during model training. In each participant, we decompose local gradients calculated on the non-iid training data into the signal and noise components and then speed up the model convergence by maximizing GSNR. We prove that GSNR can be maximized by using the optimal number of local updates. Subsequently, we develop FedGSNR to compute the optimal number of local updates for each participant, which can be applied to existing gradient calculation algorithms to accelerate the global model convergence. Moreover, according to the positive correlation between GSNR and the quality of shared information, FedGSNR allows the server to accurately evaluate contributions of different participants (i.e., the quality of local datasets) by utilizing GSNR. Extensive experimental evaluations demonstrate that FedGSNR achieves on average a 1.69× speedup with comparable accuracy.
On Intriguing Layer-Wise Properties of Robust Overfitting in Adversarial Training
Authors: Duke Nguyen; Chaojian Yu; Vinoth Nandakumar; Young Lee; Tongliang Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial training has proven to be one of the most effective methods to defend against adversarial attacks. Nevertheless, robust overfitting is a common obstacle in adversarial training of deep networks. There is a common belief that the features learned by different network layers have different properties, however, existing works generally investigate robust overfitting by considering a DNN as a single unit and hence the impact of different network layers on robust overfitting remains unclear. In this work, we divide a DNN into a series of layers and investigate the effect of different network layers on robust overfitting. We find that different layers exhibit distinct properties towards robust overfitting, and in particular, robust overfitting is mostly related to the optimization of latter parts of the network. Based upon the observed effect, we propose a robust adversarial training (RAT) prototype: in a mini-batch, we optimize the front parts of the network as usual, and adopt additional measures to regularize the optimization of the latter parts. Based on the prototype, we designed two realizations of RAT, and extensive experiments demonstrate that RAT can eliminate robust overfitting and boost adversarial robustness over the standard adversarial training.
How Powerful is Implicit Denoising in Graph Neural Networks
Authors: Songtao Liu; Zhitao Ying; Hanze Dong; Lu Lin; Jinghui Chen; Dinghao Wu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for processing graph-structured data due to their powerful representation learning capabilities. It is generally believed that GNNs can implicitly remove feature noises. However, existing works have not rigorously analyzed the implicit denoising effect in graph neural networks. In this work, we conduct a comprehensive theoretical study and analyze when and why implicit denoising happens in GNNs. Our theoretical analysis suggests that the implicit denoising largely depends on the connectivity and size of the graph, as well as the GNN architectures. Motivated by adversarial machine learning in improving the robustness of neural networks, we propose the adversarial graph signal denoising (AGSD) problem. By solving such a problem, we derive a robust graph convolution, where the smoothness of the node representations and the implicit denoising effect can be enhanced. Extensive empirical evaluations verify our theoretical analyses and the effectiveness of our proposed model.
Discovering Distinctive ``Semantics'' in Super-Resolution Networks
Authors: Yihao Liu; Anran Liu; Jinjin Gu; Zhipeng Zhang; Wenhao Wu; Yu Qiao; Chao Dong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Image super-resolution (SR) is a representative low-level vision problem. Although deep SR networks have achieved extraordinary success, we are still unaware of their working mechanisms. Specifically, whether SR networks can learn semantic information, or just perform complex mapping function? What hinders SR networks from generalizing to real-world data? These questions not only raise our curiosity, but also influence SR network development. In this paper, we make the primary attempt to answer the above fundamental questions. After comprehensively analyzing the feature representations (via dimensionality reduction and visualization), we successfully discover the distinctive ``semantics'' in SR networks, i.e., deep degradation representations (DDR), which relate to image degradation instead of image content. We show that a well-trained deep SR network is naturally a good descriptor of degradation information. Our experiments also reveal two key factors (adversarial learning and global residual) that influence the extraction of such semantics. We further apply DDR in several interesting applications (such as distortion identification, blind SR and generalization evaluation) and achieve promising results, demonstrating the correctness and effectiveness of our findings.