GenAI4ELab/papercli-papers huggingface dataset:
Quantifying Memorization Across Neural Language Models
Authors: Nicholas Carlini; Daphne Ippolito; Matthew Jagielski; Katherine Lee; Florian Tramer; Chiyuan Zhang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user data), degrades utility (repeated easy-to-memorize text is often low quality), and hurts fairness (some texts are memorized over others).
We describe three log-linear relationships that quantify the degree to which LMs emit memorized training data. Memorization significantly grows as we increase (1) the capacity of a model, (2) the number of times an example has been duplicated, and (3) the number of tokens of context used to prompt the model. Surprisingly, we find the situation becomes complicated when generalizing these results across model families. On the whole, we find that memorization in LMs is more prevalent than previously believed and will likely get worse as models continues to scale, at least without active mitigations.
Long-horizon video prediction using a dynamic latent hierarchy
Authors: Alexey Zakharov; Qinghai Guo; Zafeirios Fountas
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions. Though plagued with noise and stochasticity, videos consist of features that are organised in a spatiotemporal hierarchy, different features possessing different temporal dynamics. In this paper, we introduce Dynamic Latent Hierarchy (DLH) -- a deep hierarchical latent model that represents videos as a hierarchy of latent states that evolve over separate and fluid timescales. Each latent state is a mixture distribution with two components, representing the immediate past and the predicted future, causing the model to learn transitions only between sufficiently dissimilar states, while clustering temporally persistent states closer together. Using this unique property, DLH naturally discovers the spatiotemporal structure of a dataset and learns disentangled representations across its hierarchy. We hypothesise that this simplifies the task of modeling temporal dynamics of a video, improves the learning of long-term dependencies, and reduces error accumulation. As evidence, we demonstrate that DLH outperforms state-of-the-art benchmarks in video prediction, is able to better represent stochasticity, as well as to dynamically adjust its hierarchical and temporal structure. Our paper shows, among other things, how progress in representation learning can translate into progress in prediction tasks.
Does Federated Learning Really Need Backpropagation?
Authors: Haozhe Feng; Tianyu Pang; Chao Du; Wei Chen; Shuicheng YAN; Min Lin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated learning (FL) provides general principles for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these clients are typically edge devices and not fully trusted, executing backpropagation on them incurs computational and storage overhead as well as white-box vulnerability. In light of this, we develop backpropagation-free federated learning, dubbed BAFFLE, in which backpropagation is replaced by multiple forward processes to estimate gradients. BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server. In experiments, we demonstrate that BAFFLE-trained models can achieve empirically comparable performance to conventional FL models.
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization
Authors: Stamatios Lefkimmiatis; Iaroslav Sergeevich Koshelev
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this work we introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which are parameterized with weighted extensions of the $\ell_p^p$-vector and $\mathcal{S}_p^p$ Schatten-matrix quasi-norms for $0\!<p\!\le1$, respectively. Our proposed algorithm generalizes the Iteratively Reweighted Least Squares (IRLS) method, used for signal recovery under $\ell_1$ and nuclear-norm constrained minimization. Further, we interpret our overall minimization approach as a recurrent network that we then employ to deal with inverse low-level computer vision problems. Thanks to the convergence guarantees that our IRLS strategy offers, we are able to train the derived reconstruction networks using a memory-efficient implicit back-propagation scheme, which does not pose any restrictions on their effective depth. To assess our networks' performance, we compare them against other existing reconstruction methods on several inverse problems, namely image deblurring, super-resolution, demosaicking and sparse recovery. Our reconstruction results are shown to be very competitive and in many cases outperform those of existing unrolled networks, whose number of parameters is orders of magnitude higher than that of our learned models.
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
Authors: Andrew Lowy; Meisam Razaviyayn
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper studies federated learning (FL)—especially cross-silo FL—with data from people who do not trust the server or other silos. In this setting, each silo (e.g. hospital) has data from different people (e.g. patients) and must maintain the privacy of each person’s data (e.g. medical record), even if the server or other silos act as adversarial eavesdroppers. This requirement motivates the study of Inter-Silo Record-Level Differential Privacy (ISRL-DP), which requires silo $i$’s communications to satisfy record/item-level differential privacy (DP). ISRL-DP ensures that the data of each person (e.g. patient) in silo $i$ (e.g. hospital $i$) cannot be leaked. ISRL-DP is different from well-studied privacy notions. Central and user-level DP assume that people trust the server/other silos. On the other end of the spectrum, local DP assumes that people do not trust anyone at all (even their own silo). Sitting between central and local DP, ISRL-DP makes the realistic assumption (in cross-silo FL) that people trust their own silo, but not the server or other silos. In this work, we provide tight (up to logarithms) upper and lower bounds for ISRL-DP FL with convex/strongly convex loss functions and homogeneous (i.i.d.) silo data. Remarkably, we show that similar bounds are attainable for smooth losses with arbitrary heterogeneous silo data distributions, via an accelerated ISRL-DP algorithm. We also provide tight upper and lower bounds for ISRL-DP federated empirical risk minimization, and use acceleration to attain the optimal bounds in fewer rounds of communication than the state-of-the-art. Finally, with a secure “shuffler” to anonymize silo messages (but without a trusted server), our algorithm attains the optimal central DP rates under more practical trust assumptions. Numerical experiments show favorable privacy-accuracy tradeoffs for our algorithm in classification and regression tasks.
Topology Matters in Fair Graph Learning: a Theoretical Pilot Study
Authors: Zhimeng Jiang; Xiaotian Han; Chao Fan; Zirui Liu; Xiao Huang; Na Zou; Ali Mostafavi; Xia Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent advances in fair graph learning observe that graph neural networks (GNNs) further amplify prediction bias compared with multilayer perception (MLP), while the reason behind this is unknown. In this paper, we conduct a theoretical analysis of the bias amplification mechanism in GNNs. This is a challenging task since GNNs are difficult to be interpreted, and real-world networks are complex. To bridge the gap, we theoretically and experimentally demonstrate that aggregation operation in representative GNNs accumulates bias in node representation due to topology bias induced by graph topology. We provide a sufficient condition identifying the statistical information of graph data, so that graph aggregation enhances prediction bias in GNNs. 
 Motivated by this data-centric finding, we propose a fair graph refinement algorithm, named \textit{FairGR}, to rewire graph topology to reduce sensitive homophily coefficient while preserving useful graph topology. Experiments on node classification tasks demonstrate that \textit{FairGR} can mitigate the prediction bias with comparable performance on three real-world datasets. Additionally, \textit{FairGR} is compatible with many state-of-the-art methods, such as adding regularization, adversarial debiasing, and Fair mixup via refining graph topology. Therefore, \textit{FairGR} is a plug-in fairness method and can be adapted to improve existing fair graph learning strategies.
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Authors: Peng XU; Lin Zhang; Xuanzhou Liu; Jiaqi Sun; Yue Zhao; Haiqin Yang; Bei Yu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs,  has achieved significant performance  over manually designed GNN architectures. However, these methods inherit issues from the conventional NAS methods, such as high computational cost and optimization difficulty. More importantly, previous NAS methods have ignored the uniqueness of GNNs, where the non-linearity has limited effect. Based on this, we are the first to theoretically prove that a GNN fixed with random weights can obtain optimal outputs under mild conditions. With the randomly-initialized weights, we can then seek the optimal architecture parameters via the sparse coding objective and derive a novel NAS-GNNs method, namely neural architecture coding (NAC).  Consequently, our NAC holds a no-update scheme on GNNs and can efficiently compute in linear time. Empirical evaluations on multiple GNN benchmark datasets demonstrate that our approach leads to state-of-the-art performance, which is up to $200\times$ faster and $18.8\%$ more accurate than the strong baselines.
Neural Design for Genetic Perturbation Experiments
Authors: Aldo Pacchiano; Drausin Wulsin; Robert A Barton; Luis Voloch
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genetic edits (perturbations) or combinations thereof is infeasible due to cost and experimental limitations. This work provides a theoretically sound framework for iteratively exploring the space of perturbations in pooled batches in order to maximize a target phenotype under an experimental budget. Inspired by this application domain, we study the problem of batch query bandit optimization and introduce the Optimistic Arm Elimination ($\mathrm{OAE}$) principle designed to find an almost optimal arm under different functional relationships between the queries (arms) and the outputs (rewards). We analyze the convergence properties of $\mathrm{OAE}$ by relating it to the Eluder dimension of the algorithm's function class and validate that $\mathrm{OAE}$ outperforms other strategies in finding optimal actions in experiments on simulated problems, public datasets well-studied in bandit contexts, and in genetic perturbation datasets when the regression model is a deep neural network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in the GeneDisco experimental planning challenge.
Exp-$\alpha$: Beyond Proportional Aggregation in Federated Learning
Authors: Junjiao Tian; Xiaoliang Dai; Chih-Yao Ma; Zecheng He; Yen-Cheng Liu; Sayan Ghosh; Peter Vajda; Anqi Wu; Zsolt Kira
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated Learning (FL) is a distributed learning paradigm, which computes gradients of a model locally on different clients and aggregates the updates to construct a new model collectively. Typically, the updates from local clients are aggregated with weights proportional to the size of clients' local datasets. In practice, clients have different local datasets suffering from data heterogeneity, such as imbalance. Although proportional aggregation still theoretically converges to the global optimum, it is provably slower when non-IID data is present (under convexity assumptions), the effect of which is exacerbated in practice. We posit that this analysis ignores convergence rate, which is especially important under such settings in the more realistic non-convex real world.  To account for this, we analyze a generic and time-varying aggregation strategy to reveal a surprising trade-off between convergence rate and convergence error under convexity assumptions. Inspired by the theory, we propose a new aggregation strategy, Exp-$\alpha$, which weights clients differently based on their severity of data heterogeneity. It achieves stronger convergence rates at the theoretical cost of a non-vanishing convergence error. Through a series of controlled experiments, we empirically demonstrate the superior convergence behavior (both in terms of rate and, in practice, even error) of the proposed aggregation on three types of data heterogeneity: imbalance, label-flipping, and domain shift when combined with existing FL algorithms. For example, on our imbalance benchmark, Exp-$\alpha$, combined with FedAvg, achieves a relative $12\%$ increase in convergence rate and a relative $3\%$ reduction in error across four FL communication settings.
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
Authors: Yongyi Yang; Jacob Steinhardt; Wei Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent work has observed an intriguing "Neural Collapse" phenomenon in well-trained neural networks, where the last-layer representations of training samples with the same label collapse into each other. This suggests that the last-layer representations are completely determined by the labels, and do not depend on the intrinsic structure of input distribution. We provide evidence that this is not a complete description, and that the apparent collapse hides important fine-grained structure in the representations. Specifically, even when representations apparently collapse, the small amount of remaining variation can still faithfully and accurately captures the intrinsic structure of input distribution. As an example, if we train on CIFAR-10 using only 5 coarse-grained labels (by combining two classes into one super-class) until convergence, we can reconstruct the original 10-class labels from the learned representations via unsupervised clustering. The reconstructed labels achieve $93\%$ accuracy on the CIFAR-10 test set, nearly matching the normal CIFAR-10 accuracy for the same architecture. Our findings show concretely how the structure of input data can play a significant role in determining the fine-grained structure of neural representations, going beyond what Neural Collapse predicts.
Sequential Attention for Feature Selection
Authors: Taisuke Yasuda; Mohammadhossein Bateni; Lin Chen; Matthew Fahrbach; Gang Fu; Vahab Mirrokni
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, prior methods, including those based on $\ell_1$ regularization, attention, and other techniques, typically select the entire feature subset in one evaluation round, ignoring the residual value of features during selection, i.e., the marginal contribution of a feature given that other features have already been selected. We propose a feature selection algorithm called Sequential Attention that achieves state-of-the-art empirical results for neural networks. This algorithm is based on an efficient one-pass implementation of greedy forward selection and uses attention weights at each step as a proxy for feature importance. We give theoretical insights into our algorithm for linear regression by showing that an adaptation to this setting is equivalent to the classical Orthogonal Matching Pursuit (OMP) algorithm, and thus inherits all of its provable guarantees. Our theoretical and empirical analyses offer new explanations towards the effectiveness of attention and its connections to overparameterization, which may be of independent interest.
DeepPipe: Deep, Modular and Extendable Representations of Machine Learning Pipelines
Authors: Sebastian Pineda Arango; Josif Grabocka
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Finding accurate Machine Learning pipelines is essential in achieving state-of-the-art AI predictive performance. Unfortunately, most existing Pipeline Optimization techniques rely on flavors of Bayesian Optimization that do not explore the deep interaction between pipeline stages/components (e.g. between hyperparameters of the deployed preprocessing algorithm and the hyperparameters of a classifier). In this paper, we are the first to capture the deep interaction between components of a Machine Learning pipeline. We propose embedding pipelines in a deep latent representation through a novel per-component encoder mechanism. Such pipeline embeddings are used with deep kernel Gaussian Process surrogates inside a Bayesian Optimization setup. Through extensive experiments on large-scale meta-datasets, we demonstrate that learning pipeline embeddings with Deep Neural Networks significantly advances the state-of-the-art in Pipeline Optimization.
Bias Amplification Improves Worst-Group Accuracy without Group Information
Authors: Gaotang Li; Jiarui Liu; Wei Hu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels. Previous approaches based on worst-group loss minimization (\textit{e.g.} Group-DRO) are effective in improving worse-group accuracy but require expensive group annotations for all the training samples. In this paper, we focus on the more challenging and realistic setting where group annotations are only available on a small validation set or are not available at all. We propose \bam, a novel two-stage training algorithm: in the first stage, the model is trained using a \emph{bias amplification} scheme via introducing a learnable \emph{auxiliary variable} for each training sample together with the adoption of squared loss; in the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset. Empirically, \bam leads to consistent improvement over its counterparts in worst-group accuracy, resulting in state-of-the-art performance in spurious correlation benchmarks in computer vision and natural language processing. Moreover, we find a simple stopping criterion that completely removes the need for group annotations, with little or no loss in worst-group accuracy.
Prompt Tuning with Prompt-aligned Gradient for Vision-Language Models 
Authors: Beier Zhu; Yulei Niu; Yucheng Han; Yue Wu; Hanwang Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a zero-shot classifier by ``prompt'', e.g., using the model provided similarity measure between an image and the prompt sentence ``$\texttt{a photo of a [CLASS]}$'', as the confidence score of predicting the image is ``$\texttt{[CLASS]}$''. Therefore, prompt shows a great potential for fast adapting the VLMs to downstream tasks if we fine-tune the prompt-based similarity measure. However, we find a common failure that improper fine-tuning may not only undermine the prompt's inherent prediction for the task-related classes, but also for other classes in the VLM vocabulary. Existing methods still address this problem by using traditional anti-overfitting techniques such as early stopping and data augmentation, which lack a principled solution specific to prompt. We present Prompt-aligned Gradient, dubbed $\texttt{ProGrad}$, to prevent prompt tuning from forgetting the the general knowledge learned from VLMs. In particular, $\texttt{ProGrad}$ only updates the prompt whose gradient is aligned (or non-conflicting) to the ``general direction'', which is represented as the gradient of the KL loss of the pre-defined prompt prediction. Extensive experiments demonstrate the stronger few-shot generalization ability of $\texttt{ProGrad}$ over state-of-the-art prompt tuning methods. Codes are in Appendix.
Spectral Subgraph Localization
Authors: Judith Hermanns; Amit Boyarski; Petros Petsinis; Alex M. Bronstein; Davide Mottin; Panagiotis Karras
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Several graph mining problems are based on some variant of the subgraph isomorphism problem: Given two graphs, G and Q, does G contain a subgraph isomorphic to Q? As this problem is NP-hard, many methods avoid addressing it explicitly. In this paper, we propose a method that solves the problem by localizing, i.e., finding the position of, Q in G, by means of an alignment among graph spectra. Finding a node correspondence from Q to G thereafter is relegated to a separate task, as an instance of the graph alignment problem. We demonstrate that our spectral approach outperforms a baseline based on the state-of-the-art method for graph alignment in terms of accuracy on real graphs and scales to hundreds of nodes as no other method does.
Test-time Adaptation for Segmentation via Image Synthesis
Authors: Mihir Prabhudesai; Anirudh Goyal; Sujoy Paul; Mehdi S. M. Sajjadi; Sjoerd van Steenkiste; Gaurav Aggarwal; Thomas Kipf; Deepak Pathak; Katerina Fragkiadaki
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We consider the problem of segmenting scenes into constituent objects and their parts. Current supervised visual detectors, though impressive within their training distribution, often fail to segment out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently and have shown promising results towards generalization outside the training distribution for the task of image classification. In our work, we find evidence that these losses can be insufficient for instance segmentation tasks, without also considering architectural inductive biases. For image segmentation, recent slot-centric generative models break such dependence on supervision by attempting to segment scenes into entities in a self-supervised manner by reconstructing pixels. Drawing upon these two lines of work, we propose Generating Fast and Slow Networks (GFS-Nets), a semi-supervised instance segmentation model equipped with a slot-centric image or point-cloud rendering component that is adapted per scene at test time through gradient descent on reconstruction or novel view synthesis objectives. We show that test-time adaptation greatly improves segmentation in out-of-distribution scenes. We evaluate GFS-Nets in several 3D and 2D scene segmentation benchmarks and show substantial out-of-distribution performance improvements against state-of-the-art supervised feed forward detectors and self-supervised domain adaptation models.