mtybilly/Instruct2DS huggingface dataset:
Agent Planning with World Knowledge Model
Authors: Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen Shuofei Qiao
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three real-world simulated datasets with Mistral-7B, Gemma-7B, and Llama-3-8B demonstrate that our method can achieve superior performance compared to various strong baselines. Besides, we analyze to illustrate that our WKM can effectively alleviate the blind trial-and-error and hallucinatory action issues, providing strong support for the agent's understanding of the world. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development.
Calibrating Reasoning in Language Models with Internal Consistency
Authors: Jizhou Guo, Tong Yu, Shuai Li Zhihui Xie
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks, aided by techniques like chain-of-thought prompting that elicits verbalized reasoning. However, LLMs often generate text with obvious mistakes and contradictions, raising doubts about their ability to robustly process and utilize generated rationales. In this work, we investigate reasoning in LLMs through the lens of internal representations, focusing on how these representations are influenced by generated rationales. Our preliminary analysis reveals that while generated rationales improve answer accuracy, inconsistencies emerge between the model’s internal representations in middle layers and those in final layers, potentially undermining the reliability of their reasoning processes. To address this, we propose internal consistency as a measure of the model’s confidence by examining the agreement of latent predictions decoded from intermediate layers. Extensive empirical studies across different models and datasets demonstrate that internal consistency effectively distinguishes between correct and incorrect reasoning paths. Motivated by this, we propose a new approach to calibrate reasoning by up-weighting reasoning paths with high internal consistency, resulting in a significant boost in reasoning performance. Further analysis uncovers distinct patterns in attention and feed-forward modules across layers, providing insights into the emergence of internal inconsistency. In summary, our results demonstrate the potential of using internal representations for self-evaluation of LLMs.
Graph Coarsening with Message-Passing Guarantees
Authors: Nicolas Keriven Antonin Joly
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and memory footprint. For instance, in graph machine learning, training Graph Neural Networks (GNNs) on coarsened graphs leads to drastic savings in time and memory. However, GNNs rely on the Message-Passing (MP) paradigm, and classical spectral preservation guarantees for graph coarsening do not directly lead to theoretical guarantees when performing naive message-passing on the coarsened graph.In this work, we propose a new message-passing operation specific to coarsened graphs, which exhibit theoretical guarantees on the preservation of the propagated signal. Interestingly, and in a sharp departure from previous proposals, this operation on coarsened graphs is oriented, even when the original graph is undirected. We conduct node classification tasks on synthetic and real data and observe improved results compared to performing naive message-passing on the coarsened graph.
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack
Authors: Siyuan Liang, Baoyuan Wu Mingli Zhu
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Deep neural networks face persistent challenges in defending against backdoor attacks, leading to an ongoing battle between attacks and defenses. While existing backdoor defense strategies have shown promising performance on reducing attack success rates, can we confidently claim that the backdoor threat has truly been eliminated from the model? To address it, we re-investigate the characteristics of the backdoored models after defense (denoted as defense models). Surprisingly, we find that the original backdoors still exist in defense models derived from existing post-training defense strategies, and the backdoor existence is measured by a novel metric called backdoor existence coefficient. It implies that the backdoors just lie dormant rather than being eliminated. To further verify this finding, we empirically show that these dormant backdoors can be easily re-activated during inference stage, by manipulating the original trigger with well-designed tiny perturbation using universal adversarial attack. More practically, we extend our backdoor re-activation to black-box scenario, where the defense model can only be queried by the adversary during inference stage, and develop two effective methods, i.e., query-based and transfer-based backdoor re-activation attacks. The effectiveness of the proposed methods are verified on both image classification and multimodal contrastive learning (i.e., CLIP) tasks. In conclusion, this work uncovers a critical vulnerability that has never been explored in existing defense strategies, emphasizing the urgency of designing more robust and advanced backdoor defense mechanisms in the future.
Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks
Authors: Sidak Pal Singh, Aurelien Lucchi Jim Zhao
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: The Gauss-Newton (GN) matrix plays an important role in machine learning, most evident in its use as a preconditioning matrix for a wide family of popular adaptive methods to speed up optimization. Besides, it can  also provide key insights into the optimization landscape of neural networks. In the context of deep neural networks, understanding the GN matrix involves studying the interaction between different weight matrices as well as the dependencies introduced by the data, thus rendering its analysis challenging.In this work, we take a first step towards theoretically characterizing the conditioning of the GN matrix in neural networks. We establish tight bounds on the condition number of the GN in deep linear networks of arbitrary depth and width, which we also extend to two-layer ReLU networks.We expand the analysis to further architectural components, such as residual connections and convolutional layers. Finally, we empirically validate the bounds and uncover valuable insights into the influence of the analyzed architectural components.
Amortized Fourier Neural Operators
Authors: Siqi Kou, Zhongkai Hao, Bokai Lin, Zhijie Deng Zipeng Xiao
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Fourier Neural Operators (FNOs) have shown promise for solving partial differential equations (PDEs).Typically, FNOs employ separate parameters for different frequency modes to specify tunable kernel integrals in Fourier space, which, yet, results in an undesirably large number of parameters when solving high-dimensional PDEs. A workaround is to abandon the frequency modes exceeding a predefined threshold, but this limits the FNOs' ability to represent high-frequency details and poses non-trivial challenges for hyper-parameter specification. To address these, we propose AMortized Fourier Neural Operator (AM-FNO), where an amortized neural parameterization of the kernel function is deployed to accommodate arbitrarily many frequency modes using a fixed number of parameters. We introduce two implementations of AM-FNO, based on the recently developed, appealing Kolmogorov–Arnold Network (KAN) and Multi-Layer Perceptrons (MLPs) equipped with orthogonal embedding functions respectively. We extensively evaluate our method on diverse datasets from various domains and observe up to 31\% average improvement compared to competing neural operator baselines.
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Authors: Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko Romain Ilbert
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimization problem as a regularization technique to enable single-task models to leverage multi-task learning information. We derive a closed-form solution for multi-task optimization in the context of linear models. Our analysis provides valuable insights by linking the multi-task learning performance to various model statistics such as raw data covariances, signal-generating hyperplanes, noise levels, as well as the size and number of datasets. We finally propose a consistent estimation of training and testing errors, thereby offering a robust foundation for hyperparameter optimization in multi-task regression scenarios. Experimental validations on both synthetic and real-world datasets in regression and multivariate time series forecasting demonstrate improvements on univariate models, incorporating our method into the training loss and thus leveraging multivariate information.
Image2Struct: Benchmarking Structure Extraction for Vision-Language Models
Authors: Tony Lee, Chi Heem Wong, Michihiro Yasunaga, Yifan Mai, Percy Liang Josselin Somerville Roberts
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Datasets and Benchmarks Track), 2024

Abstract: We introduce Image2Struct, a benchmark to evaluate vision-language models (VLMs) on extracting structure from images.Our benchmark 1) captures real-world use cases, 2) is fully automatic and does not require human judgment, and 3) is based on a renewable stream of fresh data.In Image2Struct, VLMs are prompted to generate the underlying structure (e.g., LaTeX code or HTML) from an input image (e.g., webpage screenshot).The structure is then rendered to produce an output image (e.g., rendered webpage), which is compared against the input image to produce a similarity score.This round-trip evaluation allows us to quantitatively evaluate VLMs on tasks with multiple valid structures.We create a pipeline that downloads fresh data from active online communities upon execution and evaluates the VLMs without human intervention.We introduce three domains (Webpages, LaTeX, and Musical Scores) and use five image metrics (pixel similarity, cosine similarity between the Inception vectors, learned perceptual image patch similarity, structural similarity index measure, and earth mover similarity) that allow efficient and automatic comparison between pairs of images. We evaluate Image2Struct on 14 prominent VLMs and find that scores vary widely, indicating that Image2Struct can differentiate between the performances of different VLMs.Additionally, the best score varies considerably across domains (e.g., 0.402 on sheet music vs. 0.830 on LaTeX equations), indicating that Image2Struct contains tasks of varying difficulty.For transparency, we release the full results at  https://crfm.stanford.edu/helm/image2struct/v1.0.1/.
Offline Behavior Distillation
Authors: Sen Zhang, Dacheng Tao Shiye Lei
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity $\mathcal{O}(1/(1-\gamma)^2)$. We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity $\mathcal{O}(1/(1-\gamma))$. Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization.
AutoSurvey: Large Language Models Can Automatically Write Surveys
Authors: Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Min Zhang, Qingsong Wen, Wei Ye, Shikun Zhang, Yue Zhang Yidong Wang
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due to the vast volume and complexity of information, prompting the need for efficient survey methods. While large language models (LLMs) offer promise in automating this process, challenges such as context window limitations, parametric knowledge constraints, and the lack of evaluation benchmarks remain. AutoSurvey addresses these challenges through a systematic approach that involves initial retrieval and outline generation, subsection drafting by specialized LLMs, integration and refinement, and rigorous evaluation and iteration. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Authors: Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou Henry Hengyuan Zhao
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. However, current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. In this study, we introduce LOVA3, an innovative framework named ``Learning tO Visual Question Answering, Asking and Assessment,'' designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming at fostering the skills of asking and assessing questions in the context of images. To develop the questioning ability, we compile a comprehensive set of multimodal foundational tasks. For assessment, we introduce a new benchmark called EvalQABench, comprising 64,000 training samples (split evenly between positive and negative samples) and 5,000 testing samples. We posit that enhancing MLLMs with the capabilities to answer, ask, and assess questions will enhance their multimodal comprehension, ultimately improving overall performance. To validate this hypothesis, we train MLLMs using the LOVA3 framework and evaluate them on a range of multimodal datasets and benchmarks. Our results demonstrate consistent performance gains, underscoring the critical role of these additional tasks in fostering comprehensive intelligence in MLLMs.
Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning
Authors: Chen Liu, Ying Wei Xu Yang
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: This paper introduces AMT, an \textbf{A}dversarial \textbf{M}eta-\textbf{T}uning methodology, to boost the robust generalization of pre-trained models in the out-of-domain (OOD) few-shot learning. To address the challenge of transferring knowledge from source domains to unseen target domains, we construct the robust LoRAPool by meta-tuning LoRAs with dual perturbations applied to not only the inputs but also singular values and vectors of the weight matrices at various robustness levels. On top of that, we introduce a simple yet effective test-time merging mechanism to dynamically merge discriminative LoRAs for test-time task customization. Extensive evaluations demonstrate that AMT yields significant improvements, up to 12.92\% in clean generalization and up to 49.72\% in adversarial generalization, over previous state-of-the-art methods across a diverse range of OOD few-shot image classification tasks on three benchmarks, confirming the effectiveness of our approach to boost the robust generalization of pre-trained models. Our code is available at \href{https://github.com/xyang583/AMT}{https://github.com/xyang583/AMT}.
Leveraging Catastrophic Forgetting to Develop Safe Diffusion Models against Malicious Finetuning
Authors: Hongcheng Gao, Zongyu Wu, Taihang Hu, Li Su, Qingming Huang, Liang Li Jiadong Pan
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters.However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods.Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting.The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further.
PowerPM: Foundation Model for Power Systems
Authors: Yupeng Zhang, Jing Zhang, Zhendong Fu, Yin Zhang, Yang Yang Shihao Tu
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: The proliferation of abundant electricity time series (ETS) data presents numerous opportunities for various applications within power systems, including demand-side management, grid stability, and consumer behavior analysis. Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. However, learning a generic representation of ETS data for various applications is challenging due to the inherently complex hierarchical structure of ETS data. Moreover, ETS data exhibits intricate temporal dependencies and is susceptible to the influence of exogenous variables. Furthermore, different instances exhibit diverse electricity consumption behavior. In this paper, we propose a foundation model PowerPM for ETS data, providing a large-scale, off-the-shelf model for power systems. PowerPM consists of a temporal encoder and a hierarchical encoder. The temporal encoder captures temporal dependencies within ETS data, taking into account exogenous variables. The hierarchical encoder models correlations between different levels of hierarchy. Furthermore, PowerPM leverages a novel self-supervised pre-training framework consisting of masked ETS modeling and dual-view contrastive learning. This framework enables PowerPM to capture temporal dependency within ETS windows and aware the discrepancy across ETS windows, providing two different perspectives to learn generic representation. Our experiments span five real-world scenario datasets, including both private and public data. Through pre-training on massive ETS data, PowerPM achieves SOTAperformance on diverse downstream tasks within the private dataset. Notably, when transferred to public datasets, PowerPM retains its edge, showcasing its remarkable generalization ability across various tasks and domains. Moreover, ablation studies and few-shot experiments further substantiate the effectiveness of our model.
Cost-efficient Knowledge-based Question Answering with Large Language Models
Authors: Qinggang Zhang, Chuang Zhou, Hao Chen, Daochen Zha, Xiao Huang Junnan Dong
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving. However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics. It is also laborious for model selection since different models excel in diverse knowledge. To this end, we propose Coke, a novel cost-efficient strategy for KBQA with LLMs, modeled as a tailored multi-armed bandit problem to minimize calls to LLMs within limited budgets. We first formulate the accuracy expectation with a cluster-level Thompson Sampling for either KGMs or LLMs. A context-aware policy is optimized to further distinguish the expert model subject to the question semantics. The overall decision is bounded by the cost regret according to historical expenditure on failures. Extensive experiments showcase the superior performance of Coke, which moves the Pareto frontier with up to 20.89% saving of GPT-4 fees while achieving a 2.74% higher accuracy on the benchmark datasets.
LoQT: Low-Rank Adapters for Quantized Pretraining
Authors: Mads Toftrup, Michael J. Kastoryano, Serge Belongie, Vésteinn Snæbjarnarson Sebastian Loeschcke
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Despite advances using low-rank adapters and quantization, pretraining of large models on consumer hardware has not been possible without model sharding, offloading during training, or per-layer gradient updates. To address these limitations, we propose Low-Rank Adapters for Quantized Training (LoQT), a method for efficiently training quantized models. LoQT uses gradient-based tensor factorization to initialize low-rank trainable weight matrices that are periodically merged into quantized full-rank weight matrices. Our approach is suitable for both pretraining and fine-tuning models. We demonstrate this for language modeling and downstream task adaptation, finding that LoQT enables efficient training of models up to 7B parameters on a 24GB GPU. We also demonstrate the feasibility of training a 13B model using per-layer gradient updates on the same hardware.
High Rank Path Development: an approach to learning the filtration of stochastic processes
Authors: Hao Ni, Chong Liu Jiajie Tao
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Since the weak convergence for stochastic processes does not account for the growth of information over time which is represented by the underlying filtration, a slightly erroneous stochastic model in weak topology may cause huge loss in multi-periods decision making problems. To address such discontinuities, Aldous introduced the extended weak convergence, which can fully characterise all essential properties, including the filtration, of stochastic processes; however, it was considered to be hard to find efficient numerical implementations. In this paper, we introduce a novel metric called High Rank PCF Distance (HRPCFD) for extended weak convergence based on the high rank path development method from rough path theory, which also defines the characteristic function for measure-valued processes. We then show that such HRPCFD admits many favourable analytic properties which allows us to design an efficient algorithm for training HRPCFD from data and construct the HRPCF-GAN by using HRPCFD as the discriminator for conditional time series generation. Our numerical experiments on both hypothesis testing and generative modelling validate the out-performance of our approach compared with several state-of-the-art methods, highlighting its potential in broad applications of synthetic time series generation and in addressing classic financial and economic challenges, such as optimal stopping or utility maximisation problems. Code is available at https://github.com/DeepIntoStreams/High-Rank-PCF-GAN.git.
Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
Authors: Lianze Shan, Jitao Zhao, Hengrui Zhang, Zhen Wang, Weixiong Zhang Dongxiao He
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrimination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood. In this paper, we revisit these frameworks and reveal a common mechanism—representation scattering—that significantly enhances their performance. Our discovery highlights an essential feature of GCL and unifies these seemingly disparate methods under the concept of representation scattering. To leverage this insight, we introduce Scattering Graph Representation Learning (SGRL), a novel framework that incorporates a new representation scattering mechanism designed to enhance representation diversity through a center-away strategy. Additionally, consider the interconnected nature of graphs, we develop a topology-based constraint  mechanism that integrates graph structural properties with representation scattering to prevent excessive scattering. We extensively evaluate SGRL across various downstream tasks on benchmark datasets, demonstrating its efficacy and superiority over existing GCL methods. Our findings underscore the significance of representation scattering in GCL and provide a structured framework for harnessing this mechanism to advance graph representation learning. The code of SGRL is at https://github.com/hedongxiao-tju/SGRL.
One-Step Diffusion Distillation through Score Implicit Matching
Authors: Zemin Huang, Zhengyang Geng, J. Zico Kolter, Guo-jun Qi Weijian Luo
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples.  This has motivated the community to develop effective methods to distill pre-trained diffusion models into more efficient models, but these methods still typically require few-step inference or perform substantially worse than the underlying model.  In this paper, we present Score Implicit Matching (SIM) a new approach to distilling pre-trained diffusion models into single-step generator models, while maintaining almost the same sample generation ability as the original model as well as being data-free with no need of training samples for distillation.  The method rests upon the fact that, although the traditional score-based loss is intractable to minimize for generator models, under certain conditions we \emph{can} efficiently compute the \emph{gradients} for a wide class of score-based divergences between a diffusion model and a generator. SIM shows strong empirical performances for one-step generators: on the CIFAR10 dataset, it achieves an FID of 2.06 for unconditional generation and 1.96 for class-conditional generation.  Moreover, by applying SIM to a leading transformer-based diffusion model, we distill a single-step generator for text-to-image (T2I) generation that attains an aesthetic score of 6.42 with no performance decline over the original multi-step counterpart, clearly outperforming the other one-step generators including SDXL-TURBO of 5.33, SDXL-LIGHTNING of 5.34 and HYPER-SDXL of 5.85. We will release this industry-ready one-step transformer-based T2I generator along with this paper.
Structured Learning of Compositional Sequential Interventions
Authors: Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva Jialin Yu
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as "close schools for a month due to a pandemic" or "promote this podcast to this user during this week", it is unclear which appropriate structural assumptions allow us to generalize behavioral predictions to previously unseen combinations of interventions. Standard black-box approaches mapping sequences of categorical variables to outputs are applicable, but they rely on poorly understood assumptions on how reliable generalization can be obtained, and may underperform under sparse sequences, temporal variability, and large action spaces. To approach that,  we pose an explicit model for composition, that is, how the effect of sequential interventions can be isolated into modules, clarifying which data conditions allow for the identification of their combined effect at different units and time steps. We show the identification properties of our compositional model, inspired by advances in causal matrix factorization methods. Our focus is on predictive models for novel compositions of interventions instead of matrix completion tasks and causal effect estimation. We compare our approach to flexible but generic black-box models to illustrate how structure aids prediction in sparse data conditions.
Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
Authors: Eric Mazumdar Zaiwei Chen
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: We study the convergence behavior of a generalized Frank-Wolfe algorithm in constrained (stochastic) monotone variational inequality (MVI) problems. In recent years, there have been numerous efforts to design algorithms for solving constrained MVI problems due to their connections with optimization, machine learning, and equilibrium computation in games. Most work in this domain has focused on extensions of simultaneous gradient play, with particular emphasis on understanding the convergence properties of extragradient and optimistic gradient methods. In contrast, we examine the performance of an algorithm from another well-known class of optimization algorithms: Frank-Wolfe. We show that a generalized variant of this algorithm achieves a fast $\mathcal{O}(T^{-1/2})$ last-iterate convergence rate in constrained MVI problems. By drawing connections between our generalized Frank-Wolfe algorithm and the well-known smoothed fictitious play (FP) from game theory, we also derive a finite-sample convergence rate for smoothed FP in zero-sum matrix games. Furthermore, we demonstrate that a stochastic variant of the generalized Frank-Wolfe algorithm for MVI problems also converges in a last-iterate sense, albeit at a slower $\mathcal{O}(T^{-1/6})$ convergence rate.
Real-time Stereo-based 3D Object Detection for Streaming Perception
Authors: Zonghua Gu, Gang Chen, Libo Huang, Wei Zhang, Huihui Zhou Changcai Li
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: The ability to promptly respond to environmental changes is crucial for the perception system of autonomous driving. Recently, a new task called streaming perception was proposed. It jointly evaluate the latency and accuracy into a single metric for video online perception. In this work, we introduce StreamDSGN, the first real-time stereo-based 3D object detection framework designed for streaming perception. StreamDSGN is an end-to-end framework that directly predicts the 3D properties of objects in the next moment by leveraging historical information, thereby alleviating the accuracy degradation of streaming perception. Further, StreamDSGN applies three strategies to enhance the perception accuracy: (1) A feature-flow-based fusion method, which generates a pseudo-next feature at the current moment to address the misalignment issue between feature and ground truth. (2) An extra regression loss for explicit supervision of object motion consistency in consecutive frames. (3) A large kernel backbone with a large receptive field for effectively capturing long-range spatial contextual features caused by changes in object positions. Experiments on the KITTI Tracking dataset show that, compared with the strong baseline, StreamDSGN significantly improves the streaming average precision by up to 4.33%. Our code is available at https://github.com/weiyangdaren/streamDSGN-pytorch.
Parallelizing Linear Transformers with the Delta Rule over Sequence Length
Authors: Bailin Wang, Yu Zhang, Yikang Shen, Yoon Kim Songlin Yang
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Transformers with linear attention (i.e., linear transformers) and state-space models have recently been suggested as a viable linear-time alternative to transformers with softmax attention. However, these models still underperform transformers especially on tasks that require in-context retrieval. While more expressive variants of linear transformers which replace the additive update in linear transformers with the delta rule (DeltaNet) have been found to be more effective at associative recall, existing algorithms for training such models do not parallelize over sequence length and are thus inefficient to train on modern hardware. This work describes a hardware-efficient algorithm for training linear transformers with the delta rule, which exploits a memory-efficient representation for computing products of Householder matrices. This algorithm allows us to scale up DeltaNet to standard language modeling settings. We train a 1.3B model for 100B tokens and find that it outperforms recent linear-time baselines such as Mamba and GLA in terms of perplexity and zero-shot performance on downstream tasks. We also experiment with two hybrid models which combine DeltaNet layers with (1) sliding-window attention layers every other layer or (2) two global attention layers, and find that these hybrids outperform strong transformer baselines.
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Authors: Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin Qian Xie
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be reinterpreted as an acquisition function. We study the use of this acquisition function for cost-aware Bayesian optimization, and demonstrate empirically that it performs well, particularly in medium-high dimensions. We further show that this performance carries over to classical Bayesian optimization without explicit evaluation costs. Our work constitutes a first step towards integrating techniques from Gittins index theory into Bayesian optimization.
Adaptable Logical Control for Large Language Models
Authors: Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck, Nanyun Peng Honghua Zhang
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Despite the success of Large Language Models (LLMs) on various tasks following human instructions, controlling model generation to follow strict constraints at inference time poses a persistent challenge. In this paper, we introduce Ctrl-G, a neuro-symbolic framework that enables tractable and adaptable control of LLM generation to follow logical constraints reliably. Ctrl-G combines any production-ready LLM with a Hidden Markov Model (HMM), guiding LLM outputs to adhere to logical constraints represented as deterministic finite automata. We show that Ctrl-G, when a TULU2-7B model is coupled with a 2B-parameter HMM, outperforms GPT4 in text editing: on the task of generating text insertions/continuations following logical constraints, our approach achieves over 30% higher satisfaction rate in human evaluation. When applied to medium-size language models (e.g., GPT2-large), Ctrl-G also beats its counterparts on standard benchmarks by large margins. Additionally, as a proof-of-concept study, we use Ctrl-G to assist LLM reasoning on the GSM benchmark, foreshadowing the application of Ctrl-G, as well as other constrained generation approaches, beyond traditional language generation tasks.
FLAME : Factuality-Aware Alignment for Large Language Models
Authors: Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, Xilun Chen Sheng-Chieh Lin
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Alignment is a procedure to fine-tune pre-trained large language models (LLMs) to follow natural language instructions and serve as helpful AI assistants. We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e., hallucination). In this paper, we study how to make the LLM alignment process more factual, by first identifying factors that lead to hallucination in both alignment steps: supervised fine-tuning (SFT) and reinforcement learning (RL).In particular, we find that training the LLM on new or unfamiliar knowledge can encourage hallucination.This makes SFT less factual as it trains on human-labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL often inadequately capture factuality and favor longer and more detailed responses, which inadvertently promote hallucination.Based on these observations, we propose FactuaLity-aware AlignMEnt, comprised of factuality-aware SFT and factuality-aware RL through direct preference optimization. Experiments show that our proposed FLAME guides LLMs to output more factual responses while maintaining their instruction-following capability.
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Authors: Xingchen Wang, Yaqing Wang, Tianci Liu, Lu Su, Jing Gao Feijie Wu
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global model training.  To address this issue, the concept of model heterogeneity through submodel extraction has emerged, offering a tailored solution that aligns the model's complexity with each client's computational capacity. In this work, we propose Federated Importance-Aware Submodel Extraction (FIARSE), a novel approach that dynamically adjusts submodels based on the importance of model parameters, thereby overcoming the limitations of previous static and dynamic submodel extraction methods.  Compared to existing works, the proposed method offers a theoretical foundation for the submodel extraction and eliminates the need for additional information beyond the model parameters themselves to determine parameter importance, significantly reducing the overhead on clients. Extensive experiments are conducted on various datasets to showcase the superior performance of the proposed FIARSE.
Clustering in Causal Attention Masking
Authors: Yury Polyanskiy, Philippe Rigollet Nikita Karagodin
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: This work presents a modification of the self-attention dynamics proposed in Geshkovski et al to better reflect the practically relevant, causally masked attention used in transformer architectures for generative AI. This modification translates into an interacting particle system that cannot be interpreted as a mean-field gradient flow. Despite this loss of structure, we significantly strengthen the results of Geshkovski et al in this context: While previous rigorous results focused on cases where all three matrices (key, query, and value) were scaled identities, we prove asymptotic convergence to a single cluster for arbitrary key-query matrices and value matrix equal to the identity.Additionally, we establish a connection to the classical R\'enyi parking problem from combinatorial geometry to make initial theoretical steps towards demonstrating the existence of meta-stable states.
The Selective $G$-Bispectrum and its Inversion: Applications to $G$-Invariant Networks
Authors: Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane Simon Mataigne
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: An important problem in signal processing and deep learning is to achieve *invariance* to nuisance factors not relevant for the task. Since many of these factors are describable as the action of a group $G$ (e.g. rotations, translations, scalings), we want methods to be $G$-invariant. The $G$-Bispectrum extracts every characteristic of a given signal up to group action: for example, the shape of an object in an image, but not its orientation. Consequently, the $G$-Bispectrum has been incorporated into deep neural network architectures as a computational primitive for $G$-invariance\textemdash akin to a pooling mechanism, but with greater selectivity and robustness. However, the computational cost of the $G$-Bispectrum ($\mathcal{O}(|G|^2)$, with $|G|$ the size of the group) has limited its widespread adoption. Here, we show that the $G$-Bispectrum computation contains redundancies that can be reduced into a *selective $G$-Bispectrum* with $\mathcal{O}(|G|)$ complexity. We prove desirable mathematical properties of the selective $G$-Bispectrum and demonstrate how its integration in neural networks enhances accuracy and robustness compared to traditional approaches, while enjoying considerable speeds-up compared to the full $G$-Bispectrum.
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
Authors: Yan Chen, Jiahao Wang, Wenbin An, Mengmeng Wang, Feng Tian, Yong Liu, Guang Dai, Jingdong Wang, Qianying Wang Haonan Lin
Published in: Advances in Neural Information Processing Systems 37  (NeurIPS 2024) (Main Conference Track), 2024

Abstract: Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires inverting the source image into a latent space, a process often hindered by prediction errors inherent in DDIM inversion. These errors accumulate during the diffusion process, resulting in inferior content preservation and edit fidelity, especially with conditional inputs. We address these challenges by investigating the primary contributors to error accumulation in DDIM inversion and identify the singularity problem in traditional noise schedules as a key issue. To resolve this, we introduce the Logistic Schedule, a novel noise schedule designed to eliminate singularities, improve inversion stability, and provide a better noise space for image editing. This schedule reduces noise prediction errors, enabling more faithful editing that preserves the original content of the source image. Our approach requires no additional retraining and is compatible with various existing editing methods. Experiments across eight editing tasks demonstrate the Logistic Schedule's superior performance in content preservation and edit fidelity compared to traditional noise schedules, highlighting its adaptability and effectiveness. The project page is available at https://lonelvino.github.io/SYE/.