GenAI4ELab/papercli-papers huggingface dataset:
Human alignment of neural network representations
Authors: Lukas Muttenthaler; Jonas Dippel; Lorenz Linhardt; Robert A. Vandermeulen; Simon Kornblith
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Today’s computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give rise to human vision. In this paper, we investigate the factors that affect the alignment between the representations learned by neural networks and human mental representations inferred from behavioral responses. We find that model scale and architecture have essentially no effect on the alignment with human behavioral responses, whereas the training dataset and objective function both have a much larger impact. These findings are consistent across three datasets of human similarity judgments collected using two different tasks. Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets. In addition, we find that some human concepts such as food and animals are well-represented by neural networks whereas others such as royal or sports-related objects are not. Overall, although models trained on larger, more diverse datasets achieve better alignment with humans than models trained on ImageNet alone, our results indicate that scaling alone is unlikely to be sufficient to train neural networks with conceptual representations that match those used by humans.
MoCa: Cognitive Scaffolding for Language Models in Causal and Moral Judgment Tasks
Authors: Allen Nie; Yuhui Zhang; Atharva Amdekar; Christopher J Piech; Tatsunori Hashimoto; Tobias Gerstenberg
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something bad happened, we naturally ask: who did what, and why? A rich literature in cognitive science has studied people's causal and moral intuitions. These works have revealed a number of factors that systematically influence people's judgments, such as the presence of norms, and whether or not the protagonist in a scenario was aware of their action's potential consequences. Here, we investigate whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with those of human participants. We find that without any annotations, LLMs and human participants are not well aligned (17\%-39\% agreement). However, LLMs can accurately annotate what relevant factors are present in a scenario with simple expert-written instructions. We demonstrate how these annotations can be used to bring LLMs in closer alignment with people (36.3\%-47.2\% agreement). These results show how insights from cognitive science can help scaffold language models to more closely match human intuitions in challenging commonsense evaluation tasks.
QuAFL: Federated Averaging Made Asynchronous and Communication-Efficient
Authors: Hossein Zakerinia; Shayan Talaei; Giorgi Nadiradze; Dan Alistarh
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated Learning (FL) is an emerging paradigm to enable the large-scale distributed training of machine learning models, while still providing privacy guarantees. 
  In this work, we address two of the main practical challenges when scaling federated optimization to large node counts: the need for tight synchronization between the central authority and individual computing nodes, and the large communication cost of transmissions between the central server and clients. 
  Specifically, we present a new variant of the classic federated averaging (FedAvg) algorithm, which supports both asynchronous communication and communication compression. We provide a new analysis technique showing that, in spite of these system relaxations, our algorithm can provide similar convergence to FedAvg in some parameter regimes.
  On the experimental side, we show that our algorithm ensures fast  convergence for standard federated tasks.
Interpretability with full complexity by constraining feature information
Authors: Kieran A Murphy; Danielle Bassett
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, sacrificing model complexity in order to render more comprehensible the effects of those features on the model's output. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. We use the Distributed Information Bottleneck to optimally compress each feature so as to maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature---at every stage of approximation---allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets.
Memory Efficient Dynamic Sparse Training
Authors: Mike Heddes; Narayan Srinivasa
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The excessive memory and energy consumption of modern Artificial Neural Networks (ANNs) is posing limitations on the machines that can run these models. Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training. A growing body of work is now focusing on providing the benefits of model sparsification also during training. While these methods improve the energy efficiency during training, the algorithms yielding the most accurate models still have a peak memory usage on the same order as the dense model. We propose a Dynamic Sparse Training (DST) algorithm that reduces the peak memory usage during training while preserving the energy advantages of sparsely trained models. We evaluate our algorithm on CIFAR-10/100 using ResNet-56 and VGG-16 and compare it against a range of sparsification methods. The benefits of our method are twofold: first, it allows for a given model to be trained to an accuracy on par with the dense model while requiring significantly less memory and energy; second, the savings in memory and energy can be allocated towards training an even larger sparse model on the same machine, generally improving the accuracy of the model.
Towards the Detection of Diffusion Model Deepfakes
Authors: Jonas Ricker; Simon Damm; Thorsten Holz; Asja Fischer
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Diffusion models (DMs) have recently emerged as a promising method in image synthesis. They have surpassed generative adversarial networks (GANs) in both diversity and quality, and have achieved impressive results in text-to-image modeling. However, to date, only little attention has been paid to the detection of DM-generated images, which is critical to prevent adverse impacts on our society. While prior works have shown that GAN-generated images can be reliably detected using automated methods, it is unclear whether the same methods are effective against DMs. In this work, we address this challenge and take a first look at detecting DM-generated images. We approach the problem from two different angles: First, we evaluate the performance of state-of-the-art detectors on a variety of DMs. Second, we analyze DM-generated images in the frequency domain and study different factors that influence the spectral properties of these images. Most importantly, we demonstrate that GANs and DMs produce images with different characteristics, which requires adaptation of existing classifiers to ensure reliable detection. We believe this work provides the foundation and starting point for further research to detect DM deepfakes effectively.
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Authors: Samuel Lavoie; Christos Tsirigotis; Max Schwarzer; Ankit Vani; Michael Noukhovitch; Kenji Kawaguchi; Aaron Courville
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into $L$ simplices of $V$ dimensions each using a \texttt{softmax} operation. This procedure conditions the representation onto a constrained space during pretraining and imparts an inductive bias for group sparsity. For downstream classification, we formally prove that the SEM representation leads to better generalization than an unnormalized representation.
Furthermore, we empirically demonstrate that SSL methods trained with SEMs have improved generalization on natural image datasets such as CIFAR-100 and ImageNet. Finally, when used in a downstream classification task, we show that SEM features exhibit emergent semantic coherence where small groups of learned features are distinctly predictive of semantically-relevant classes.
Non-Parametric State-Space Models: Identifiability, Estimation and Forecasting
Authors: Chenghao Liu; Weiran Yao; Steven Hoi; Kun Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: State-space models (SSMs) provide a standard methodology for time series analysis and prediction. While recent works utilize nonlinear functions to parameterize the transition and emission processes to enhance their expressivity, the form of additive noise still limits their applicability in real-world scenarios. In this work, we propose a general formulation of SSMs with a completely non-parametric transition model and a flexible emission model which can account for sensor distortion. Besides, to deal with more general scenarios (e.g., non-stationary time series), we add a higher level model to capture time-varying characteristics of the process.
Interestingly, we find that even though the proposed model is remarkably flexible, the latent processes are generally identifiable. Given this, we further propose the corresponding estimation procedure and make use of it for the forecasting task. Our model can recover the latent processes and their relations from observed sequential data. Accordingly, the proposed procedure can also be viewed as a method for causal representation learning. We argue that forecasting can benefit from causal representation learning, since the estimated latent variables are generally identifiable. Empirical comparisons on various datasets validate that our model could not only reliably identify the latent processes from the observed data, but also consistently outperform baselines in the forecasting task.
Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement
Authors: Yoonho Lee; Huaxiu Yao; Chelsea Finn
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Real-world machine learning problems often exhibit shifts between the source and target distributions, in which source data does not fully convey the desired behavior on target inputs. Different functions that achieve near-perfect source accuracy can make differing predictions on test inputs, and such ambiguity makes robustness to distribution shifts challenging. We propose DivDis, a simple two-stage framework for identifying and resolving ambiguity in data. DivDis first learns a diverse set of hypotheses that achieve low source loss but make differing predictions on target inputs. We then disambiguate by selecting one of the discovered functions using additional information, for example, a small number of target labels. Our experimental evaluation shows improved performance in subpopulation shift and domain generalization settings, demonstrating that DivDis can scalably adapt to distribution shifts in image and text classification benchmarks.
Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics
Authors: Julius Adebayo; Melissa Hall; Bowen Yu; Bobbie Chern
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Errors in labels obtained via human annotation adversely affect a trained model's performance. Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's group-based disparity metrics\footnote{Group-based disparity metrics like subgroup calibration, false positive rate, false negative rate, equalized odds, and equal opportunity are more often known, colloquially, as \textit{fairness metrics} in the literature. We use the term group-based disparity metrics in this work.}. Here we study the effect of label error on a model's group-based disparity metrics like group calibration. We empirically characterize how varying levels of label error, in both training and test data, affect these disparity metrics. We find that group calibration and other metrics are sensitive to train-time and test-time label error---particularly for minority groups. For the same level of label error, the percentage change in group calibration error for the minority group is on average 1.5 times larger than the change for the majority group. Towards mitigating the impact of training-time label error, we present an approach to estimate how changing a single training input's label affects a model's group disparity metric on a test set. We empirically assess the proposed approach on a variety of datasets and find a 10-40\% improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric. The proposed approach can help surface training inputs that may need to be corrected for improving a model's group-based disparity metrics.
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
Authors: Tong Yang; Michael Jordan; Tatjana Chavdarova
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we generalize ADMM to derive a first-order method for cVIs, that we refer to as ADMM-based interior-point method for constrained VIs (ACVI). We provide convergence guarantees for ACVI in two general classes of problems: (i) when the operator is $\xi$-monotone, and (ii) when it is monotone, some constraints are active and the game is not purely rotational. When the operator is in addition L-Lipschitz for the latter case, we match known lower bounds on rates for the gap function of $\mathcal{O}(1/\sqrt{K})$ and $\mathcal{O}(1/K)$ for the last and average iterate, respectively. To the best of our knowledge, this is the first presentation of a first-order interior-point method for the general cVI problem that has a global convergence guarantee. Moreover, unlike previous work in this setting, ACVI provides a means to solve cVIs when the constraints are nontrivial. Empirical analyses demonstrate clear advantages of ACVI over common first-order methods. In particular, (i) cyclical behavior is notably reduced as our methods approach the solution from the analytic center, and (ii) unlike projection-based methods that zigzag when near a constraint, ACVI efficiently handles the constraints.
Motif-induced Graph Normalization
Authors: Kaixuan Chen; Shunyu Liu; Tongtian Zhu; Tongya Zheng; Haofei Zhang; Jie Song; Mingli Song
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data in the non-Euclidean domain. Despite their success, existing GNNs typically suffer from the insufficient expressive power bottlenecked by Weisfeiler-Lehman (WL) test, and meanwhile are prone to the over-smoothing situation with increasing layer numbers. In this paper, we strive to strengthen the discriminative capabilities of GNNs by devising a dedicated plug-and-play normalization scheme, termed as Motif-induced Normalization (MotifNorm), that explicitly considers the intra-connection information within each node-induced subgraph. To this end, we embed the motif-induced structural weights at the beginning and the end of the standard BatchNorm, as well as incorporate the graph instance-specific statistics for improved distinguishable capabilities. In the meantime, we provide the theoretical analysis to support that, with the proposed elaborated MotifNorm, an arbitrary GNNs is capable of more expressive abilities than the 1-WL test in distinguishing k-regular graphs. Furthermore, the proposed MotifNorm scheme is also exemplified to be able to alleviate the over-smoothing phenomenon. Experimental results on ten popular benchmarks across all the tasks of the graph-, node-, as well as link-level property predictions, demonstrate the effectiveness of the proposed method. Our code is made available in the supplementary material.
TOWARD RELIABLE NEURAL SPECIFICATIONS
Authors: Chuqin Geng; Nham Le; Xiaojie Xu; Zhaoyue Wang; Arie Gurfinkel; Xujie Si
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Having reliable specifications is an unavoidable challenge in achieving verifiable correctness, robustness, and interpretability of AI systems. Existing specifications for neural networks are in the flavor of “data as specification”, that is, the local neighborhood centering around a reference input is considered to be correct (or robust). However, our empirical study shows that such specifications fail to certify any test data points, making it impractical for real-world applications. We propose a new family of specifications called “neural representation as specification”, which uses the intrinsic information of neural networks — neural activation patterns (NAP) rather than input data to specify the correctness and/or robustness of neural network predictions. We present a simple statistical approach to extracting dominant neural activation patterns. We analyze NAPs from a statistical point of view and find that a single NAP can cover a large number of training and testing data points whereas ad hoc data-as-specification can only cover a single training data point and often zero testing data points. To show the effectiveness of discovered NAPs, we formally verify several important properties, such as a particular type of misclassification never happens for a given NAP, and there is no ambiguity among different NAPs. We show that by using NAP, we can verify the prediction of the entire input space, while still recalling 84% of the data. Thus, we argue that using NAPs is a more reliable and extensible specification for neural network verification.
Active Learning based Structural Inference
Authors: Aoran Wang; Jun Pang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we propose an active-learning based framework, Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents' states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with relatively small pool of prior knowledge. Moreover, based on information theory, we propose inter- and out-of-scope message learning pipelines, which are remarkably beneficial to the structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to precisely infer the existence of connections in these systems under either supervised learning or unsupervised learning, with better performance than baseline methods.
VQ-TR: Vector Quantized Attention for Time Series Forecasting
Authors: Kashif Rasul; Umang Gupta; Hena Ghonia; Yuriy Nevmyvaka
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Modern time series datasets can easily contain hundreds or thousands of temporal time points, however, Transformer based models scale poorly to the size of the sequence length constraining their context size in the seq-to-seq setting. In this work, we introduce VQ-TR which maps large sequences to a discrete set of latents representations as part of the Attention module. This allows us to attend over larger context windows with linear complexity with respect to the sequence length. We compare this method with other competitive deep learning and classical univariate probabilistic models and highlight its performance using both probabilistic and point forecasting metrics on a variety of open datasets from different domains.
Domain Generalization with Small Data
Authors: Kecheng Chen; Elena Gal; Hong Yan; Haoliang Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisted of a serial of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the distribution over distributions (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art baseline methods.
Causally Constrained Data Synthesis For Private Data Release
Authors: Varun Chandrasekaran; Darren Edge; Somesh Jha; Lukas Wutschitz; Amit Sharma; Cheng Zhang; Shruti Tople
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Data privacy is critical in many decision-making contexts, such as healthcare and finance. A common mechanism is to create differentially private synthetic data using generative models. Such data generation reflects certain statistical properties of the original data, but often has an unacceptable privacy vs. utility trade-off. Since natural data inherently exhibits causal structure, we propose incorporating \emph{causal information} into the training process to favorably navigate the aforementioned trade-off. Under certain assumptions for linear gaussian models and a broader class of models, we theoretically prove that causally informed generative models provide better differential privacy guarantees than their non-causal counterparts. We evaluate our proposal using variational autoencoders, and demonstrate that the trade-off is mitigated through better utility for comparable privacy.