GenAI4ELab/papercli-papers huggingface dataset:
Illusory Adversarial Attacks on Sequential Decision-Makers and Countermeasures
Authors: Tim Franzmeyer; Stephen Marcus McAleer; Joao F. Henriques; Philip Torr; Jakob Nicolaus Foerster; Adel Bibi; Christian Schroeder de Witt
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Autonomous decision-making agents deployed in the real world need to be robust against possible adversarial attacks on sensory inputs. Existing work on adversarial attacks focuses on the notion of perceptual invariance popular in computer vision. We observe that such attacks can often be detected by victim agents, since they result in action-observation sequences that are not consistent with the dynamics of the environment. Furthermore, real-world agents, such as physical robots, commonly operate under human supervisors who are not susceptible to such attacks. We propose to instead focus on attacks that are statistically undetectable. Specifically, we propose illusory attacks, a novel class of adversarial attack that is consistent with the environment dynamics. We introduce a novel algorithm that can learn illusory attacks end-to-end. We empirically verify that our algorithm generates attacks that, in contrast to current methods, are undetectable to both AI
agents with an environment dynamics model, as well as to humans. Furthermore, we show that existing robustification approaches are relatively ineffective against illusory attacks. Our findings highlight the need to ensure that real-world AI, and human-AI, systems are designed to make it difficult to corrupt sensory observations in ways that are consistent with the environment dynamics.
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Poisson Processes
Authors: Simon Luo; Feng Zhou; lamiae azizi; Mahito Sugiyama
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in Poisson processes using projections into lower-dimensional space. Our model combines the techniques in information geometry to model higher-order interactions on a statistical manifold and in generalized additive models to use lower-dimensional projections to overcome the effects from the curse of dimensionality. Our approach solves a convex optimization problem by minimizing the KL divergence from a sample distribution in lower-dimensional projections to the distribution modeled by an intensity function in the Poisson process. Our empirical results show that our model is able to use samples observed in the lower dimensional space to estimate the higher-order intensity function with extremely sparse observations.
Differentially Private Dataset Condensation
Authors: Tianhang Zheng; Baochun Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent work in ICML'22 builds a theoretical connection between dataset condensation (DC) and differential privacy (DP) and claims that DC can provide privacy protection for free. However, the connection is problematic because of two controversial assumptions. In this paper, we revisit the ICML'22 work and elucidate the issues in the two controversial assumptions. To correctly connect DC and DP, we propose two differentially private dataset condensation (DPDC) algorithms---LDPDC and NDPDC. Through extensive evaluations on multiple datasets, we demonstrate that LDPDC has comparable performance to recent DP generative methods despite its simplicity. NDPDC provides acceptable DP guarantees with a mild utility loss, compared to the state-of-the-art DC method. Additionally, NDPDC allows a flexible trade-off between the synthetic data utility and DP budget.
Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer
Authors: Ronghang Zhu; Ronghang Zhu; Xiang Yu; Sheng Li
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper targets at a new and challenging setting of knowledge transfer from multiple source domains to a single target domain, where target data is few shot or even one shot with label. Traditional domain generalization or adaptation methods cannot directly work since there is no sufficient target domain distribution serving as the transfer object. The multi-source setting further prevents the transfer task as excessive domain gap introduced from all the source domains. To tackle this problem, we newly propose a progressive mix-up (P-Mixup) mechanism to introduce an intermediate mix-up domain, pushing both the source domains and the few-shot target domain aligned to this mix-up domain. Further by enforcing the mix-up domain to progressively move towards the source domains, we achieve the domain transfer from multi-source domains to the single one-shot target domain. Our P-Mixup is different from traditional mix-up that ours is with a progressive and adaptive mix-up ratio, following the curriculum learning spirit to better align the source and target domains. Moreover, our P-Mixup combines both pixel-level and feature-level mix-up to better enrich the data diversity. Experiments on two benchmarks show that our P-Mixup significantly outperforms the state-of-the-art methods, i.e., 6.0\% and 6.8\% improvements on Office-Home and DomainNet.
Multitask Reinforcement Learning by Optimizing Neural Pathways
Authors: Samin Yeasar Arnob; Riyasat Ohib; Amy Zhang; Sergey Plis; Doina Precup
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Reinforcement learning (RL) algorithms have achieved great success in learning specific tasks, as evidenced by examples such as AlphaGo or fusion control. However, it is still difficult for an RL agent to learn how to solve multiple tasks. In this paper, we propose a novel multitask learning framework, in which multiple specialized pathways through a single network are trained simultaneously, with each pathway focusing on a single task. We show that this approach achieves competitive performance with existing multitask RL methods, while using only 5% of the number of neurons per task. We demonstrate empirically the success of our approach on several continuous control tasks, in both online and offline training.
ProSampler: Improving Contrastive Learning by Better Mini-batch Sampling
Authors: Zhen Yang; Tinglin Huang; Ming Ding; Zhitao Ying; Yukuo Cen; Yangliao Geng; Yuxiao Dong; Jie Tang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In-batch contrastive learning has emerged as a state-of-the-art self-supervised learning solution, with the philosophy of bringing semantically similar instances closer while pushing dissimilar instances apart within a mini-batch. However, the in-batch negative sharing strategy is limited by the batch size and falls short of prioritizing the informative negatives (i.e., hard negatives) globally. In this paper, we propose to sample mini-batches with hard negatives on a proximity graph in which the instances (nodes) are connected according to the similarity measurement.  Sampling on the proximity graph can better exploit the hard negatives globally by bridging in similar instances from the entire dataset. The proposed method can flexibly explore the negatives by modulating two parameters, and we show that such flexibility is the key to better exploit hard negative globally. We evaluate the proposed method on three representative contrastive learning algorithms, each of which corresponds to one modality: image, text, and graph. Besides, we also apply it to the variants of the InfoNCE objective to verify its generality. The results show that our method can consistently boost the performance of contrastive methods, with a relative improvement of 2.5% for SimCLR on ImageNet-100, 1.4% for SimCSE on the standard STS task, and 1.2% for GraphCL on the COLLAB dataset.
MEGAN: Multi Explanation Graph Attention Network
Authors: Jonas Teufel; Luca Torresi; Patrick Nicholas Reiser; Pascal Friederich
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Explainable artificial intelligence (XAI) methods are expected to improve trust during human-AI interactions, provide tools for model analysis and extend human understanding of complex problems. Attention-based models are an important subclass of XAI methods, partly due to their full differentiability and the potential to improve explanations by means of explanation-supervised training. We propose the novel multi-explanation graph attention network (MEGAN). Our graph regression and classification model features multiple explanation channels, which can be chosen independently of the task specifications. We first validate our model on a synthetic graph regression dataset, where our model produces single-channel explanations with quality similar to GNNExplainer. Furthermore, we demonstrate the advantages of multi-channel explanations on one synthetic and two real-world datasets: The prediction of water solubility of molecular graphs and sentiment classification of movie reviews. We find that our model produces explanations consistent with human intuition, opening the way to learning from our model in less well-understood tasks.
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Authors: Daniel Palenicek; Michael Lutter; Joao Carvalho; Jan Peters
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving the learned dynamics models? Our paper empirically answers this question for the class of model-based value expansion methods in continuous control problems. Value expansion methods should benefit from increased model accuracy by enabling longer rollout horizons and better value function approximations. Our empirical study, which leverages oracle dynamics models to avoid compounding model errors, shows that (1) longer horizons increase sample efficiency, but the gain in improvement decreases with each additional expansion step, and (2) the increased model accuracy only marginally increases the sample efficiency compared to learned models with identical horizons. Therefore, longer horizons and increased model accuracy yield diminishing returns in terms of sample efficiency. These improvements in sample efficiency are particularly disappointing when compared to model-free value expansion methods. Even though they introduce no computational overhead, we find their performance to be on-par with model-based value expansion methods. Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models. While higher model accuracy is beneficial, our experiments show that even a perfect model will not provide an un-rivaled sample efficiency but that the bottleneck lies elsewhere.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
Authors: Jinhyung Park; Chenfeng Xu; Shijia Yang; Kurt Keutzer; Kris M. Kitani; Masayoshi Tomizuka; Wei Zhan
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multi-frame images are instances of temporal stereo matching, we find that performance is hindered by the interplay between 1) the low granularity of matching resolution and 2) the sub-optimal multi-view setup produced by limited history usage. Our theoretical and empirical analysis demonstrates that the optimal temporal difference between views varies significantly for different pixels and depths, making it necessary to fuse many timesteps over long-term history. Building on our investigation, we propose to generate a cost volume from a long history of image observations, compensating for the coarse but efficient matching resolution with a more optimal multi-view matching setup. Further, we augment the per-frame monocular depth predictions used for long-term, coarse matching with short-term, fine-grained matching and find that long and short term temporal fusion are highly complementary. While maintaining high efficiency, our framework sets new state-of-the-art on nuScenes, achieving first place on the test set and outperforming previous best art by 5.2% mAP and 3.7% NDS on the validation set. Code will be released here: https://github.com/Divadi/SOLOFusion.
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
Authors: Boah Kim; Yujin Oh; Jong Chul Ye
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are required in supervised methods and confusing background structures make neural networks hard to segment vessels in an unsupervised manner. To address this, here we introduce a novel diffusion adversarial representation learning (DARL) model that leverages a denoising diffusion probabilistic model with adversarial learning, and apply it to vessel segmentation. In particular, for self-supervised vessel segmentation, DARL learns the background signal using a diffusion module, which lets a generation module effectively provide vessel representations. Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information. Once the proposed model is trained, the model generates segmentation masks in a single step and can be applied to general vascular structure segmentation of coronary angiography and retinal images. Experimental results on various datasets show that our method significantly outperforms existing unsupervised and self-supervised vessel segmentation methods.
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
Authors: Chenglin Yang; Siyuan Qiao; Qihang Yu; Xiaoding Yuan; Yukun Zhu; Alan Yuille; Hartwig Adam; Liang-Chieh Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, we replace its multi-layer perceptron with a mobile convolution block, and further reorder it before the self-attention operation. The mobile convolution block not only enhances the network representation capacity, but also produces better downsampled features. Our conceptually simple MOAT networks are surprisingly effective, achieving 89.1% / 81.5% top-1 accuracy on ImageNet-1K / ImageNet-1K-V2 with ImageNet-22K pretraining. Additionally, MOAT can be seamlessly applied to downstream tasks that require large resolution inputs by simply converting the global attention to window attention. Thanks to the mobile convolution that effectively exchanges local information between pixels (and thus cross-windows), MOAT does not need the extra window-shifting mechanism. As a result, on COCO object detection, MOAT achieves 59.2% AP$^{\text{box}}$ with 227M model parameters (single-scale inference, and hard NMS), and on ADE20K semantic segmentation, MOAT attains 57.6% mIoU with 496M model parameters (single-scale inference). Finally, the tiny-MOAT family, obtained by simply reducing the channel sizes, also surprisingly outperforms several mobile-specific transformer-based models on ImageNet. The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community. We hope our simple yet effective MOAT will inspire more seamless integration of convolution and self-attention. Code is publicly available.
Speculative Decoding: Lossless Speedup of Autoregressive Translation
Authors: Heming Xia; Tao Ge; Si-Qing Chen; Furu Wei; Zhifang Sui
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Different from some previous work accelerating autoregressive translation (AT) at the sacrifice of quality, we propose Speculative Decoding (SpecDec) -- a novel decoding paradigm inspired by speculative execution in computer architecture, which combines respective advantages of AT and non-autoregressive translation (NAT) for lossless speedup of translation. At each decoding step, SpecDec first speculatively drafts (i.e. decodes) next $k$ tokens with an NAT model and then verifies them with an AT model, where only the drafted tokens passing the verification are accepted as decoded tokens for guaranteeing its translation result is exactly the same as AT. The collaboration of NAT drafting and AT verification leads to a much higher decoding speed without quality loss due to parallel computing enabled by speculative decoding.

We conduct experiments in 4 standard WMT translation benchmarks and confirm the vanilla SpecDec yields exactly the same results as AT greedy decoding with an around $3\times$ speedup, and that its variant (SpecDec++) with an advanced verification strategy not only outperforms AT greedy decoding, but also further improves the decoding speed, resulting in an around $5\times$ speedup over AT. Moreover, SpecDec can be easily generalized for speeding up other seq2seq tasks like Abstractive Summarization, and benefit more from stronger computing devices, demonstrating its potential to become a de facto decoding standard in the future for efficient and lossless seq2seq generation.
CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos
Authors: Hao-Wen Dong; Naoya Takahashi; Yuki Mitsufuji; Julian McAuley; Taylor Berg-Kirkpatrick
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent years have seen progress beyond domain-specific sound separation for speech or music towards universal sound separation for arbitrary sounds. Prior work on universal sound separation has investigated separating a target sound out of an audio mixture given a text query. Such text-queried sound separation systems provide a natural and scalable interface for specifying arbitrary target sounds. However, supervised text-queried sound separation systems require costly labeled audio-text pairs for training. Moreover, the audio provided in existing datasets is often recorded in a controlled environment, causing a considerable generalization gap to noisy audio in the wild. In this work, we aim to approach text-queried universal sound separation by using only unlabeled data. We propose to leverage the visual modality as a bridge to learn the desired audio-textual correspondence. The proposed CLIPSep model first encodes the input query into a query vector using the contrastive language-image pretraining (CLIP) model, and the query vector is then used to condition an audio separation model to separate out the target sound. While the model is trained on image-audio pairs extracted from unlabeled videos, at test time we can instead query the model with text inputs in a zero-shot setting, thanks to the joint language-image embedding learned by the CLIP model. Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence. To address this problem, we further propose an approach called noise invariant training for training a query-based sound separation model on noisy data. Experimental results show that the proposed models successfully learn text-queried universal sound separation using only noisy unlabeled videos, even achieving competitive performance against a supervised model in some settings.
Explaining Patterns in Data  with  Language Models via Interpretable Autoprompting
Authors: Chandan Singh; John Xavier Morris; Jyoti Aneja; Alexander M Rush; Jianfeng Gao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns in data. Specifically, given a pre-trained LLM and data examples, we introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt iteratively alternates between generating explanations with an LLM and reranking them based on their performance when used as a prompt. Experiments on a wide range of datasets, from synthetic mathematics to natural-language understanding, show that iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Moreover, the prompts produced by iPrompt are simultaneously human-interpretable and highly effective for generalization: on real-world sentiment classification datasets, iPrompt produces prompts that match or even improve upon human-written prompts for GPT-3. Finally, experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
Improving the imputation of missing data with Markov Blanket discovery
Authors: Yang Liu; Anthony Constantinou
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The process of imputation of missing data typically relies on generative and regression models. These approaches often operate on the unrealistic assumption that all of the data features are directly related with one another, and use all of the available features to impute missing values. In this paper, we propose a novel Markov Blanket discovery approach to determine the optimal feature set for a given variable by considering both observed variables and missingness of partially observed variables to account for systematic missingness. We then incorporate this method to the learning process of the state-of-the-art MissForest imputation algorithm, such that it informs MissForest which features to consider to impute missing values, depending on the variable the missing value belongs to. Experiments across different case studies and multiple imputation algorithms show that the proposed solution improves imputation accuracy, both under random and systematic missingness.
Univariate vs Multivariate Time Series Forecasting with Transformers
Authors: William Michael John Murphy; Ke Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Multivariate time series forecasting is a challenging problem and a number of Transformer-based long-term time series forecasting models have been developed to tackle it.  These models, however, are impeded by the additional information available in multivariate forecasting.  In this paper we propose a simple univariate setting as an alternative method for producing multivariate forecasts.  The univariate model is trained on each individual dimension of the time series.  This single model is then used to forecast each dimension of the multivariate forecast in turn.  A comparative study shows that our setting outperforms state-of-the-art Transformers in the multivariate setting in benchmark datasets.  To investigate why, we set three hypotheses and verify them via an empirical study, which leads to a criterion for when our univariate setting is likely to lead to better performance and reveals flaws in the current multivariate Transformers for long-term time series forecasting.