GenAI4ELab/papercli-papers huggingface dataset:
Where to Go Next for Recommender Systems? ID- vs. Modality-based recommender models revisited
Authors: Zheng Yuan; Fajie Yuan; Yu Song; Youhua Li; Fei Yang; Yunzhu Pan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recommender models that utilize unique identities (IDs for short) to represent distinct users and items have been the state-of-the-arts and dominating the recommender system (RS) literature for over a decade. In parallel, the pre-trained modality encoders, such as BERT and ResNet, are becoming increasingly powerful in modeling raw modality features, e.g., text and images. In light of this, a natural question arises: whether the modality (a.k.a, content) only based recommender models (MoRec) can exceed or be on par with the ID-only based models (IDRec) when item modality features are available? In fact, this question had been answered once a decade ago, when IDRec beat MoRec with strong advantages in terms of both recommendation accuracy and efficiency.
We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommender paradigm, MoRec or IDRec, performs best in various practical scenarios, including regular, cold and new item scenarios?  does this hold for items with different modality features? (ii) will MoRec benefit from the latest technical advances in corresponding communities, for example, natural language processing and computer vision? (iii) what is an effective way to leverage item modality representations, freezing them or adapting them by fine-tuning on new data? (iv) are there any other factors that affect the efficacy of MoRec. To answer these questions, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide empirical evidence that MoRec with standard end-to-end training is highly competitive and even exceeds IDRec in some cases. Many of our observations imply that the dominance of IDRec in terms of recommendation accuracy does not hold well when items' raw modality features are available. We promise to release all related codes & datasets upon acceptance.
Mirror Training for Input Convex Neural Network
Authors: Jiaqi Wu; Yang Weng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract:     The input convex neural network (ICNN) aims to learn a convex function from the input to the output by using non-decreasing convex activation functions and non-negativity constraints on the weight parameters of some layers. However, in practice, it loses some representation power because of these non-negativity parameters of the hidden units, even though the design of the ``passthrough'' layer can partially address this problem. To solve issues caused by these non-negativity constraints, we use a duplication input pair trick, i.e., the negation of the original input as part of the new input in our structure. This new method will preserve the convexity of the function from the original input to the output and tackle the representation problem in training. Additionally, we design a mirror unit to address this problem further, making the network Mirror ICNN. Moreover, we propose a recurrent input convex neural network (RICNN) structure to deal with the time-series problems. The recurrent unit of the structure can be ICNN or any other convex variant of ICNN. This structure can maintain convexity by constraining the mapping from the hidden output at time step $t$ to the input of the next time step $t+1$. The experiments can support our design, including the simple numerical curve fitting, power system hosting capacity dataset regression, and the MNIST dataset classification.
Speeding up Policy Optimization with Vanishing Hypothesis and Variable Mini-Batch Size
Authors: Tamás Tardi; Gergo Bogacsovics; Andras Hajdu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Reinforcement learning-based algorithms have been used extensively in recent years due to their flexible nature, good performance, and the increasing number of said algorithms. However, the largest drawback of these techniques remains unsolved, that is, it usually takes a long time for the agents to learn how to solve a given problem. In this work, we outline a novel method that can be used to drastically reduce the training time of current state-of-the-art algorithms like Proximal Policy Optimization (PPO). We evaluate the performance of this approach in a unique environment where we use reinforcement learning to help with a practical astronomical problem: where to place a fixed number of observatory stations in the Solar System to observe space objects (e.g. asteroids) as permanently as possible. That is, the reward in this scenario corresponds to the total coverage of the trajectories of these objects. We apply noisy evaluation for calculating the reward to speed up the training, which technique has already been efficiently applied in stochastic optimization. Namely, we allow the incorporation of some additional noise in the reward function in the form of a hypothesis term and a varying mini-batch size. However, in order to follow the theoretical guidelines, both of them are forced to vanish during training to let the noise converge to zero. Our experimental results show that using this approach we can reduce the training time remarkably, even by 75%.
Rethinking Identity in Knowledge Graph  Embedding
Authors: Jiayi Li; Jiaqi Sun; Yujiu Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Knowledge Graph Embedding (KGE) is a common method to complete real-world Knowledge Graphs (KGs) by learning the embeddings of entities and relations.
Beyond specific KGE models, previous work proposes a general framework based on group. A group has a special element identity that uniquely corresponds to the relation identity in KGs, which implies that identity should be represented uniquely. However, we find that this uniqueness cannot be modeled by bilinear based models, revealing the crack between the framework and models. To this end, we study the required conditions and propose a solution named Unit Ball Bilinear Model (UniBi). In addition to the theoretical superiority, UniBi is more robust and interpretable. Experiments demonstrate that UniBi models the uniqueness without the cost of performance and verify its a robustness and interpretability.
On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation
Authors: Tiago Pimentel; Clara Isabel Meister; Ryan Cotterell
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: A good automatic evaluation metric for language generation ideally correlates highly with human judgements of text quality.  Yet, there is a dearth of such metrics, which inhibits the rapid and efficient progress of language generators. One exception is  the recently proposed Mauve. In theory, Mauve measures an information-theoretic divergence between two probability distributions over strings: one representing the language generator under evaluation; the other representing the true natural language distribution. Mauve's authors argue that its success comes from the qualitative properties of their proposed divergence.  Yet in practice, as this divergence is uncomputable, Mauve approximates it by measuring the divergence between multinomial distributions over clusters instead, where cluster assignments are attained by grouping strings based on a pretrained language model's embeddings. As we show, however, this is not a tight approximation---in either theory or practice. This begs the question: why does Mauve work so well? In this work, we show that \mauve was right for the wrong reasons, and that its newly proposed divergence is not necessary for its high performance. In fact, classical divergences paired with its proposed cluster-based approximation may actually serve as better evaluation metrics. We finish the paper with a probing analysis; this analysis leads us to conclude that---by encoding syntactic- and coherence-level features of text, while ignoring surface-level features---such cluster-based approximations to string distributions may simply be better for evaluating state-of-the-art language generators.
HyperTime: Implicit Neural Representations for Time Series Generation
Authors: Elizabeth Fons; Alejandro Sztrajman; Yousef El-Laham; Alexandros Iosifidis; Svitlana Vyetrenko
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general approximators has been shown in a wide variety of data sources, with applications on image, sound, and 3D scene representation. However, little attention has been given to leveraging these architectures for the representation and analysis of time series data. In this paper, we propose a new INR architecture for time series (iSIREN) designed to perform an accurate reconstruction of univariate and multivariate data, while also providing an interpretable encoding of the signal. We compare our architecture against SIREN and INRs with different activations, in terms of training convergence, and the reconstruction accuracy of both the signal and its spectral distribution. 
To achieve generalization, we propose a hypernetwork architecture (HyperTime) that leverages iSIRENs to learn a latent representation of an entire time series dataset. In addition to the traditional reconstruction loss, we introduce an FFT-based loss that guides the training by enforcing a good match of the ground truth spectral distribution. We show how these architectures can be used for time series generation, and evaluate our method through fidelity metrics, presenting results that exceed the performance of state-of-the-art techniques. Finally, we propose an alternative hypernetwork architecture (iHyperTime) that incorporates interpretability into the latent representation, enabling the introduction of prior knowledge by imposing constraints into the generation process.
Self-Consistent Learning: Cooperation between Generators and Discriminators
Authors: Tong Wu; Hao Wang; Zhongshen Zeng; Wei Wang; Hai-Tao Zheng; Jiaxing Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Using generated data to improve the performance of downstream discriminative models has recently gained popularity due to the great development of pre-trained language models. In most previous studies, generative models and discriminative models are trained separately and thus could not adapt to any changes in each other. As a result, the generated samples can easily deviate from the real data distribution, while the improvement of the discriminative model quickly reaches saturation. Generative adversarial networks (GANs) train generative models via an adversarial process with discriminative models to achieve joint training. However, the training of standard GANs is notoriously unstable and often falls short of convergence. In this paper, to address these issues, we propose a $\textit{self-consistent learning}$ framework, in which a discriminator and a generator are cooperatively trained in a closed-loop form. The discriminator and the generator enhance each other during multiple rounds of alternating training until a scoring consensus is reached. This framework proves to be easy to train and free from instabilities such as mode collapse and non-convergence. Extensive experiments on sentence semantic matching demonstrate the effectiveness of the proposed framework: the discriminator achieves 10+ AP of improvement on the zero-shot setting and new state-of-the-art performance on the full-data setting.
Approximating any Function via Coreset for Radial Basis Functions: Towards Provable Data Subset Selection For Efficient Neural Networks training
Authors: Murad Tukan; Samson Zhou; Alaa Maalouf; Vladimir Braverman; Dan Feldman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Radial basis function neural networks (\emph{RBFNN}) are notoriously known for their capability to approximate any continuous function on a closed bounded set with arbitrary precision given enough hidden neurons. Coreset is a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, etc.). In this paper, we suggest the first coreset construction algorithm for \emph{RBFNNs}, i.e., a small weighted subset which approximates the loss of the input data on any radial basis function network and thus approximates any function defined by an \emph{RBFNN} on the big input data. This is done by constructing coresets for radial basis and Laplacian loss functions. We use our coreset to suggest a provable data subset selection algorithm for training deep neural networks, since our coreset approximates every function, it should approximate the gradient of each weight in a neural network as it is defined as a function on the input. Experimental results on function approximation and dataset subset selection on popular network architectures and data sets are presented, demonstrating the efficacy and accuracy of our coreset construction.
The Dark Side of AutoML: Towards Architectural Backdoor Search
Authors: Ren Pang; Changjiang Li; Zhaohan Xi; Shouling Ji; Ting Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks? Specifically, we present EVAS, a new attack that leverages NAS to find neural architectures with inherent backdoors and exploits such vulnerability using input-aware triggers. Compared with existing attacks, EVAS demonstrates many interesting properties: (i) it does not require polluting training data or perturbing model parameters; (ii) it is agnostic to downstream fine-tuning or even re-training from scratch; (iii) it naturally evades defenses that rely on inspecting model parameters or training data. With extensive evaluation on benchmark datasets, we show that EVAS features high evasiveness, transferability, and robustness, thereby expanding the adversary's design spectrum. We further characterize the mechanisms underlying EVAS, which are possibly explainable by architecture-level ``shortcuts'' that recognize trigger patterns. This work showcases that NAS can be exploited in a harmful way to find architectures with inherent backdoor vulnerability. The code is available at https://github.com/ain-soph/nas_backdoor.
AlphaFold Distillation for Improved Inverse Protein Folding
Authors: Igor Melnyk; Aurelie Lozano; Payel Das; Vijil Chenthamarakshan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Inverse protein folding, i.e., designing sequences that fold into a given three-dimensional structure, is  one of the fundamental design challenges in bio-engineering and drug discovery. Traditionally, inverse folding mainly involves learning from sequences that have an experimentally resolved structure. However, the known structures cover only a tiny space of the  protein sequences, imposing limitations on the model learning. Recently proposed forward folding models, e.g., AlphaFold, offer unprecedented opportunity for accurate estimation of the structure given a protein sequence. Naturally, incorporating a forward folding model as a component of an inverse folding approach offers the potential of significantly improving the inverse folding,  as the folding model can provide a feedback on any generated sequence in the form of the predicted protein structure or a structural confidence metric. However, at present, these forward folding models are still prohibitively slow to be a part of the model optimization loop during training. In this work, we propose to perform knowledge distillation on the folding model's confidence metrics, e.g., pTM or pLDDT scores, to obtain a smaller, faster and end-to-end differentiable distilled model, which then can be included as part of the structure consistency regularized inverse folding model training. Moreover, our regularization technique is general enough and can be applied in other  design tasks, e.g., sequence-based protein infilling. Extensive experiments show a clear benefit of our method over the non-regularized baselines. E.g., in inverse folding design problems we observe up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity, while still preserving structural consistency of the generated sequences.
Harnessing Client Drift with Decoupled Gradient Dissimilarity
Authors: Zhenheng TANG; Yonggang Zhang; Shaohuai Shi; Xinmei Tian; Tongliang Liu; Bo Han; Xiaowen Chu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The performance of Federated learning (FL) typically suffers from client drift caused by heterogeneous data, where data distributions vary with clients. Recent studies show that the gradient dissimilarity between clients induced by the data distribution discrepancy causes the client drift. Thus, existing methods mainly focus on correcting the gradients. However, it is challenging to identify which client should (or not) be corrected. This challenge raises a series of questions: will the local training, without gradient correction, contribute to the server model's generalization of other clients' distributions? when the generalization contribution holds? how to address the challenge when it fails? To answer these questions, we analyze the generalization contribution of local training and conclude that the generalization contribution of local training is bounded by the conditional Wasserstein distance between clients' distributions. Thus, the key to promote generalization contribution is to leverage similar conditional distributions for local training. As collecting data distribution can cause privacy leakage, we propose decoupling the deep models, i.e., splitting into high-level models and low-level models, for harnessing client drift. Namely, high-level models are trained on shared feature distributions, causing promoted generalization contribution and alleviated gradient dissimilarity. Experimental results demonstrate that FL with decoupled gradient dissimilarity is robust to data heterogeneity.
Average Sensitivity of Decision Tree Learning
Authors: Satoshi Hara; Yuichi Yoshida
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A decision tree is a fundamental model used in data mining and machine learning. In practice, the training data used to construct a decision tree may change over time or contain noise, and a drastic change in the learned tree structure owing to such data perturbation is unfavorable. For example, in data mining, a change in the tree implies a change in the extracted knowledge, which raises the question of whether the extracted knowledge is truly reliable or is only a noisy artifact. To alleviate this issue, we design decision tree learning algorithms that are stable against insignificant perturbations in the training data. Specifically, we adopt the notion of average sensitivity as a stability measure, and design an algorithm with low average sensitivity that outputs a decision tree whose accuracy is close to the optimal decision tree. The experimental results on real-world datasets demonstrate that the proposed algorithm enables users to select suitable decision trees considering the trade-off between average sensitivity and accuracy.
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Authors: Zhuo Huang; Xiaobo Xia; Li Shen; Bo Han; Mingming Gong; Chen Gong; Tongliang Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation.
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
Authors: Yivan Zhang; Jindong Wang; Xing Xie; Masashi Sugiyama
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning systems may encounter unexpected problems when the data distribution changes in the deployment environment. A major reason is that certain combinations of domains and labels are not observed during training but appear in the test environment. Although various invariance-based algorithms can be applied, we find that the performance gain is often marginal. To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement. The algebraic requirements naturally derive a simple yet effective method, referred to as equivariant disentangled transformation (EDT), which augments the data based on the algebraic structures of labels and makes the transformation satisfy the equivariance and disentanglement requirements. Experimental results demonstrate that invariance may be insufficient, and it is important to exploit the equivariance structure in the combination shift problem.
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games
Authors: Wenhao Zhan; Jason D. Lee; Zhuoran Yang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents. Our goal is to develop a no-regret online learning algorithm that (i) takes actions based on the local information observed by the agent and (ii) is able to find the best policy in hindsight. For such a problem, the nonstationary state transitions due to the varying opponent pose a significant challenge. In light of a recent hardness result (Liu et al., 2022), we focus on the setting where the opponent's previous policies are revealed to the agent for decision making. With such an information structure, we propose a new algorithm, Decentralized Optimistic hypeRpolicy mIrror deScent (DORIS), which achieves $\sqrt{K}$-regret in the context of general function approximation, where $K$ is the number of episodes. Moreover, when all the agents adopt DORIS, we prove that their mixture policy constitutes an approximate coarse correlated equilibrium. In particular, DORIS maintains a hyperpolicy which is a distribution over the policy space. The hyperpolicy is updated via mirror descent, where the update direction is obtained by an optimistic variant of least-squares policy evaluation. Furthermore, to illustrate the power of our method, we apply DORIS to constrained and vector-valued MDPs, which can be formulated as zero-sum Markov games with a fictitious opponent.