GenAI4ELab/papercli-papers huggingface dataset:
Mirage: Model-agnostic Graph Distillation for Graph Classification
Authors: Mridul Gupta; Sahil Manchanda; HARIPRASAD KODAMANA; Sayan Ranu
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an effort in that direction with the aim to construct a smaller synthetic training set from the original training data without significantly compromising model performance. While initial efforts are promising, this work is motivated by two key observations: (1) Existing graph distillation algorithms themselves rely on training with the full dataset, which undermines the very premise of graph distillation. (2) The distillation process is specific to the target GNN architecture and hyper-parameters and thus not robust to changes in the modeling pipeline. We circumvent these limitations by designing a distillation algorithm called MIRAGE for graph classification. MIRAGE is built on the insight that a message-passing GNN decomposes the input graph into a multiset of computation trees. Furthermore, the frequency distribution of computation trees is often skewed in nature, enabling us to condense this data into a concise distilled summary. By compressing the computation data itself, as opposed to emulating gradient flows on the original training set—a prevalent approach to date—MIRAGE transforms into an unsupervised and architecture-agnostic distillation algorithm. Extensive benchmarking on real-world datasets underscores MIRAGE’s superiority, showcasing enhanced generalization accuracy, data compression, and distillation efficiency when compared to state-of-the-art baselines.
On the Learnability of Watermarks for Language Models
Authors: Chenchen Gu; Xiang Lisa Li; Percy Liang; Tatsunori Hashimoto
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Watermarking of language model outputs enables statistical detection of model-generated text, which can mitigate harms and misuses of language models. Existing watermarking strategies operate by altering the decoder of an existing language model. In this paper, we ask whether language models can directly learn to generate watermarked text, which would have significant implications for the real-world deployment of watermarks. First, learned watermarks could be used to build open models that naturally generate watermarked text, enabling watermarking for open models, where users can control the decoding procedure. Second, if watermarking is used to determine the provenance of generated text, an adversary can hurt the reputation of a victim model by spoofing its watermark and generating damaging watermarked text. To investigate the learnability of watermarks, we propose watermark distillation, which trains a student model to behave like a teacher model that uses decoding-based watermarking. We test our approach on three decoding-based watermarking strategies and various hyperparameter settings, finding that models can learn to generate watermarked text with high detectability. We also find limitations to learnability, including the loss of watermarking capabilities under fine-tuning on normal text and high sample complexity when learning low-distortion watermarks.
Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision
Authors: Nan Chen; Zemin Liu; Bryan Hooi; Bingsheng He; Rizal Fathony; Jun Hu; Jia Chen
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Graph Anomaly Detection (GAD) has surfaced as a significant field of research, predominantly due to its substantial influence in production environments. Although existing approaches for node anomaly detection have shown effectiveness, they have yet to fully address two major challenges: operating in settings with limited supervision and managing class imbalance effectively. In response to these challenges, we propose a novel model, ConsisGAD, which is tailored for GAD in scenarios characterized by limited supervision and is anchored in the principles of consistency training. Under limited supervision, ConsisGAD effectively leverages the abundance of unlabeled data for consistency training by incorporating a novel learnable data augmentation mechanism, thereby introducing controlled noise into the dataset. Moreover, ConsisGAD takes advantage of the variance in homophily distribution between normal and anomalous nodes to craft a simplified GNN backbone, enhancing its capability to distinguish effectively between these two classes. Comprehensive experiments on several benchmark datasets validate the superior performance of ConsisGAD in comparison to state-of-the-art baselines. Our code is available at https://github.com/Xtra-Computing/ConsisGAD.
Bellman Optimal Stepsize Straightening of Flow-Matching Models
Authors: Bao Nguyen; Binh Nguyen; Viet Anh Nguyen
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these models, especially during the finetuning process and sampling processes, pose significant challenges for low-resource scenarios. This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint. First, this technique involves a dynamic programming algorithm that optimizes the stepsizes of the pretrained network. Then, it refines the velocity network to match the optimal step sizes, aiming to straighten the generation paths. Extensive experimental evaluations across image generation tasks demonstrate the efficacy of BOSS in terms of both resource utilization and image quality. Our results reveal that BOSS achieves substantial gains in efficiency while maintaining competitive sample quality, effectively bridging the gap between low-resource constraints and the demanding requirements of flow-matching generative models. Our paper also fortifies the responsible development of artificial intelligence, offering a more sustainable generative model that reduces computational costs and environmental footprints. Our code can be found at https://github.com/nguyenngocbaocmt02/BOSS.
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Authors: Anirudh Buvanesh; Rahul Chand; Jatin Prakash; Bhawna Paliwal; Mudit Dhawan; Neelabh Madan; Deepesh Hada; Vidit Jain; SONU MEHTA; Yashoteja Prabhu; Manish Gupta; Ramachandran Ramjee; Manik Varma
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Extreme Classification (XC) architectures, which utilize a massive One-vs-All (OvA) classifier layer at the output, have demonstrated remarkable performance on problems with large label sets. Nonetheless, these architectures falter on tail labels with few representative samples. This phenomenon has been attributed to factors such as classifier over-fitting and missing label bias, and solutions involving regularization and loss re-calibration have been developed. This paper explores the impact of label variance - a previously unexamined factor - on the tail performance in extreme classifiers. It also develops a method to systematically reduce label variance in XC by transferring the knowledge from a specialized tail-robust teacher model to the OvA classifiers. For this purpose, it proposes a principled knowledge distillation framework, LEVER, which enhances the tail performance in extreme classifiers with formal guarantees on generalization. Comprehensive experiments are conducted on a diverse set of XC datasets, demonstrating that LEVER can enhance tail performance by around 5\% and 6\% points in PSP and coverage metrics, respectively, when integrated with leading extreme classifiers. Moreover, it establishes a new state-of-the-art when added to the top-performing Renee classifier. Extensive ablations and analyses substantiate the efficacy of our design choices. Another significant contribution is the release of two new XC datasets that are different from and more challenging than the available benchmark datasets, thereby encouraging more rigorous algorithmic evaluation in the future. Code for LEVER is available at: aka.ms/lever.
Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching
Authors: Aleksandar Makelov; Georg Lange; Atticus Geiger; Neel Nanda
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Mechanistic interpretability aims to attribute high-level model behaviors to specific, interpretable learned features. It is hypothesized that these features manifest as directions or low-dimensional subspaces within activation space. Accordingly, recent studies have explored the identification and manipulation of such subspaces to reverse-engineer computations, employing methods such as activation patching. In this work, we demonstrate that naïve approaches to subspace interventions can give rise to interpretability illusions.

Specifically, even if patching along a subspace has the intended end-to-end causal effect on model behavior, this effect may be achieved by activating \emph{a dormant parallel pathway} using a component that is \textit{causally disconnected} from the model output.
We demonstrate this in a mathematical example, realize the example empirically in two different settings (the Indirect Object Identification (IOI) task and factual recall), and argue that activating dormant pathways ought to be prevalent in practice.
In the context of factual recall, we further show that the illusion is related to rank-1 fact editing, providing a mechanistic explanation for previous work observing an inconsistency between fact editing performance and fact localisation.

However, this does not imply that activation patching of subspaces is intrinsically unfit for interpretability.
To contextualize our findings, we also show what a success case looks like in a task (IOI) where prior manual circuit analysis allows an understanding of the location of the ground truth feature. We explore the additional evidence needed to argue that a patched subspace is faithful.
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
Authors: Juno Kim; Kakei Yamamoto; Kazusato Oko; Zhuoran Yang; Taiji Suzuki
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We propose \emph{mean-field Langevin averaged gradient} (MFL-AG), a single-loop algorithm that implements gradient descent ascent in the distribution spaces with a novel weighted averaging, and establish average-iterate convergence to the mixed Nash equilibrium. We also study both time and particle discretization regimes and prove a new uniform-in-time propagation of chaos result which accounts for the dependency of the particle interactions on all previous distributions. Furthermore, we propose \emph{mean-field Langevin anchored best response} (MFL-ABR), a symmetric double-loop algorithm based on best response dynamics with linear last-iterate convergence. Finally, we study applications to zero-sum Markov games and conduct simulations demonstrating long-term optimality.
Demonstration-Regularized RL
Authors: Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Menard
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we study the demonstration-regularized reinforcement learning framework that leverages the expert demonstrations by $\mathrm{KL}$-regularization for a policy learned by behavior cloning. Our findings reveal that using $N^{\mathrm{E}}$ expert demonstrations enables the identification of an optimal policy at a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{Poly}(S,A,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in finite and $\widetilde{\mathcal{O}}(\mathrm{Poly}(d,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in linear Markov decision processes, where $\varepsilon$is the target precision, $H$ the horizon, $A$ the number of action, $S$ the number of states in the finite case and $d$ the dimension of the feature space in the linear case. As a by-product, we provide tight convergence guarantees for the behavior cloning procedure under general assumptions on the policy classes. Additionally, we establish that demonstration-regularized methods are provably efficient for reinforcement learning from human feedback (RLHF). In this respect, we provide theoretical evidence showing the benefits of KL-regularization for RLHF  in tabular and linear MDPs. 
Interestingly, we avoid pessimism injection by employing computationally feasible regularization to handle reward estimation uncertainty, thus setting our approach apart from the prior works.
Multilingual Jailbreak Challenges in Large Language Models
Authors: Yue Deng; Wenxuan Zhang; Sinno Jialin Pan; Lidong Bing
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to exhibit undesirable behavior. Although several preventive measures have been developed to mitigate the potential risks associated with LLMs, they have primarily focused on English. In this study, we reveal the presence of multilingual jailbreak challenges within LLMs and consider two potential risky scenarios: unintentional and intentional. The unintentional scenario involves users querying LLMs using non-English prompts and inadvertently bypassing the safety mechanisms, while the intentional scenario concerns malicious users combining malicious instructions with multilingual prompts to deliberately attack LLMs. The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases. Specifically, low-resource languages exhibit about three times the likelihood of encountering harmful content compared to high-resource languages, with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts can exacerbate the negative impact of malicious instructions, with astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for GPT-4. To handle such a challenge in the multilingual context, we propose a novel \textsc{Self-Defense} framework that automatically generates multilingual training data for safety fine-tuning. Experimental results show that ChatGPT fine-tuned with such data can achieve a substantial reduction in unsafe content generation.  Data is available at \url{https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs}.
Generalized Policy Iteration using Tensor Approximation for Hybrid Control
Authors: Suhan Shetty; Teng Xue; Sylvain Calinon
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Control of dynamic systems involving hybrid actions is a challenging task in robotics.  To address this, we present a novel algorithm called Generalized Policy Iteration using Tensor Train (TTPI) that belongs to the class of Approximate Dynamic Programming (ADP). We use a low-rank tensor approximation technique called Tensor Train (TT) to approximate the state-value and advantage function which enables us to efficiently handle hybrid systems. We demonstrate the superiority of our approach over previous baselines for some benchmark problems with hybrid action spaces. Additionally, the robustness and generalization of the policy for hybrid systems are showcased through a real-world robotics experiment involving a non-prehensile manipulation task which is considered to be a highly challenging control problem.
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
Authors: Juno Kim; Jaehyuk Kwon; Mincheol Cho; Hyunjong Lee; Joong-Ho Won
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The variational autoencoder (VAE) typically employs a standard normal prior as a regularizer for the probabilistic latent encoder. However, the Gaussian tail often decays too quickly to effectively accommodate the encoded points, failing to preserve crucial structures hidden in the data. In this paper, we explore the use of heavy-tailed models to combat over-regularization. Drawing upon insights from information geometry, we propose $t^3$VAE, a modified VAE framework that incorporates Student's t-distributions for the prior, encoder, and decoder. This results in a joint model distribution of a power form which we argue can better fit real-world datasets. We derive a new objective by reformulating the evidence lower bound as joint optimization of KL divergence between two statistical manifolds and replacing with $\gamma$-power divergence, a natural alternative for power families. $t^3$VAE demonstrates superior generation of low-density regions when trained on heavy-tailed synthetic data. Furthermore, we show that $t^3$VAE significantly outperforms other models on CelebA and imbalanced CIFAR-100 datasets.
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
Authors: Zehao Dong; Muhan Zhang; Philip Payne; Michael A Province; Carlos Cruchaga; Tianyu Zhao; Fuhai Li; Yixin Chen
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper proposes to maximize the expressivity of GNNs by graph canonization, then the power of such GNNs is studies from the perspective of model stability. A stable GNN will map similar graphs to close graph representations in the vectorial space, and the stability of GNNs is critical to generalize their performance to unseen graphs. We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs. Then we introduce a notion of universal graph canonization as the general solution to address the trade-off and characterize a widely applicable sufficient condition to solve the universal graph canonization. A comprehensive set of experiments demonstrates the effectiveness of the proposed method. In many popular graph benchmark datasets, graph canonization successfully enhances GNNs and provides highly competitive performance, indicating the capability and great potential of proposed method in general graph representation learning. In graph datasets where the sufficient condition holds, GNNs enhanced by universal graph canonization consistently outperform GNN baselines and successfully improve the SOTA performance up to $31$%, providing the optimal solution to numerous challenging real-world graph analytical tasks like gene network representation learning in bioinformatics.
Gradual Optimization Learning for Conformational Energy Minimization
Authors: Artem Tsypin; Leonid Anatolievich Ugadiarov; Kuzma Khrabrov; Alexander Telepov; Egor Rumiantsev; Alexey Skrynnik; Aleksandr Panov; Dmitry P. Vetrov; Elena Tutubalina; Artur Kadurin
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Molecular conformation optimization is crucial to computer-aided drug discovery and materials design.
Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (oracle) as anti-gradients.
However, this is a computationally expensive approach that requires many interactions with a physical simulator.
One way to accelerate this procedure is to replace the physical simulator with a neural network.
Despite recent progress in neural networks for molecular conformation energy prediction, such models are prone to errors due to distribution shift, leading to inaccurate energy minimization.
We find that the quality of energy minimization with neural networks can be improved by providing optimization trajectories as additional training data.
Still, obtaining complete optimization trajectories demands a lot of additional computations.
To reduce the required additional data, we present the Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks.
The framework consists of an efficient data-collecting scheme and an external optimizer.
The external optimizer utilizes gradients from the energy prediction model to generate optimization trajectories, and the data-collecting scheme selects additional training data to be processed by the physical simulator. 
Our results demonstrate that the neural network trained with GOLF performs \textit{on par} with the oracle on a benchmark of diverse drug-like molecules using significantly less additional data.
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Authors: Qihang Zhou; Guansong Pang; Yu Tian; Shibo He; Jiming Chen
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary
data to detect anomalies without any training sample in a target dataset. It
is a crucial task when training data is not accessible due to various concerns, e.g.,
data privacy, yet it is challenging since the models need to generalize to anomalies
across different domains where the appearance of foreground objects, abnormal
regions, and background features, such as defects/tumors on different products/
organs, can vary significantly. Recently large pre-trained vision-language
models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition
ability in various vision tasks, including anomaly detection. However, their ZSAD
performance is weak since the VLMs focus more on modeling the class semantics
of the foreground objects rather than the abnormality/normality in the images. In
this paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIP
for accurate ZSAD across different domains. The key insight of AnomalyCLIP
is to learn object-agnostic text prompts that capture generic normality and abnormality
in an image regardless of its foreground objects. This allows our model to
focus on the abnormal image regions rather than the object semantics, enabling
generalized normality and abnormality recognition on diverse types of objects.
Large-scale experiments on 17 real-world anomaly detection datasets show that
AnomalyCLIP achieves superior zero-shot performance of detecting and segmenting
anomalies in datasets of highly diverse class semantics from various defect
inspection and medical imaging domains. Code will be made available at https://github.com/zqhang/AnomalyCLIP.
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
Authors: Linan Yue; Qi Liu; Yichao Du; Li Wang; Weibo Gao; Yanqing An
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shortcuts. Then, by introducing the identified shortcuts, we propose two strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods to close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method.