GenAI4ELab/papercli-papers huggingface dataset:
Self-Infilling Code Generation
Authors: Lin Zheng; Jianbo Yuan; Zhi Zhang; Hongxia Yang; Lingpeng Kong
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In this work, we introduce self-infilling code generation, a general framework that incorporates infilling operations into auto-regressive decoding. Our approach capitalizes on the observation that recent infilling-capable code language models can perform self-infilling: whereas conventional infilling is designed to fill in the middle based on a predefined prefix and suffix, self-infilling sequentially generates both such surrounding context and the infilled content. We utilize self-infilling to introduce novel interruption and looping mechanisms in conventional decoding, evolving it into a non-monotonic process. Interruptions allow for postponing the generation of specific code until a definitive suffix is established, enhancing control during decoding. Meanwhile, the looping mechanism, which leverages the complementary nature of self-infilling and left-to-right decoding, can iteratively update and synchronize each piece of generation cyclically. Extensive experiments across a variety of code generation benchmarks demonstrate that decoding with self-infilling not only improves the output quality but also regularizes the overall generation, which effectively mitigates potential degeneration and scaffolds code to be more consistent with intended functionality.
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Authors: Yuda Song; Lili Wu; Dylan J Foster; Akshay Krishnamurthy
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs. Toward addressing these challenges, we introduce a new theoretical framework, **RichCLD** (“Rich-Observation RL with Continuous Latent Dynamics”), in which the agent performs control based on high-dimensional observations, but the environment is governed by low-dimensional latent states and Lipschitz continuous dynamics. Our main contribution is a new algorithm for this setting that is provably statistically and computationally efficient. The core of our algorithm is a new representation learning objective; we show that prior representation learning schemes tailored to discrete dynamics do not naturally extend to the continuous setting. Our new objective is amenable to practical implementation, and empirically, we find that it compares favorably to prior schemes in a standard evaluation protocol. We further provide several insights into the statistical complexity of the **RichCLD** framework, in particular proving that certain notions of Lipschitzness that admit sample-efficient learning in the absence of rich observations are insufficient in the rich-observation setting.
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
Authors: Lingxiao Yang; Shutong Ding; Yifan Cai; Jingyi Yu; Jingya Wang; Ye Shi
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training. While these methods achieved certain success, they often compromise on sample quality and require small guidance step sizes, leading to longer sampling processes. This paper reveals that the fundamental issue lies in the manifold deviation during the sampling process when loss guidance is employed. We theoretically show the existence of manifold deviation by establishing a certain lower bound for the estimation error of the loss guidance. To mitigate this problem, we propose Diffusion with Spherical Gaussian constraint (DSG), drawing inspiration from the concentration phenomenon in high-dimensional Gaussian distributions. DSG effectively constrains the guidance step within the intermediate data manifold through optimization and enables the use of larger guidance steps. Furthermore, we present a closed-form solution for DSG denoising with the Spherical Gaussian constraint. Notably, DSG can seamlessly integrate as a plugin module within existing training-free conditional diffusion methods. Implementing DSG merely involves a few lines of additional code with almost no extra computational overhead, yet it leads to significant performance improvements. Comprehensive experimental results in various conditional generation tasks validate the superiority and adaptability of DSG in terms of both sample quality and time efficiency.
From Generalization Analysis to Optimization Designs for State Space Models
Authors: Fusheng Liu; Qianxiao Li
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to transformers in sequence modeling. In this paper, we theoretically study the generalization of SSMs and propose improvements to training algorithms based on the generalization results. Specifically, we give a *data-dependent* generalization bound for SSMs, showing an interplay between the SSM parameters and the temporal dependencies of the training sequences. Leveraging the generalization bound, we (1) set up a scaling rule for model initialization based on the proposed generalization measure, which significantly improves the robustness of the output value scales on SSMs to different temporal patterns in the sequence data; (2) introduce a new regularization method for training SSMs to enhance the generalization performance. Numerical results are conducted to validate our results.
OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
Authors: Bin Lu; Ze Zhao; Luyu Han; Xiaoying Gan; Yuntao Zhou; Lei Zhou; Luoyi Fu; Xinbing Wang; Chenghu Zhou; Jing Zhang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the “breathless ocean” in data-driven manner.
Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training
Authors: Yechan Kim; Hwijoon Lim; Dongsu Han
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Mixture-of-Experts (MoE) is a powerful technique for enhancing the performance of neural networks while decoupling computational complexity from the number of parameters. However, despite this, scaling the number of experts requires adding more GPUs. In addition, the load imbalance in token load across experts causes unnecessary computation or straggler problems. We present ES-MoE, a novel method for efficient scaling MoE training. It offloads expert parameters to host memory and leverages pipelined expert processing to overlap GPU-CPU communication with GPU computation. It dynamically balances token loads across GPUs, improving computational efficiency. ES-MoE accelerates MoE training on a limited number of GPUs without degradation in model performance. We validate our approach on GPT-based MoE models, demonstrating 67$\times$ better scalability and up to 17.5$\times$ better throughput over existing frameworks.
Imitation Learning from Purified Demonstrations
Authors: Yunke Wang; Minjing Dong; Yukun Zhao; Bo Du; Chang Xu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects.
Reflected Flow Matching
Authors: Tianyu Xie; Yu Zhu; Longlin Yu; Tong Yang; Ziheng Cheng; Shiyue Zhang; Xiangyu Zhang; Cheng Zhang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Continuous normalizing flows (CNFs) learn an ordinary differential equation to transform prior samples into data. Flow matching (FM) has recently emerged as a simulation-free approach for training CNFs by regressing a velocity model towards the conditional velocity field. However, on constrained domains, the learned velocity model may lead to undesirable flows that result in highly unnatural samples, e.g., oversaturated images, due to both flow matching error and simulation error. To address this, we add a boundary constraint term to CNFs, which leads to reflected CNFs that keep trajectories within the constrained domains. We propose reflected flow matching (RFM) to train the velocity model in reflected CNFs by matching the conditional velocity fields in a simulation-free manner, similar to the vanilla FM. Moreover, the analytical form of conditional velocity fields in RFM avoids potentially biased approximations, making it superior to existing score-based generative models on constrained domains. We demonstrate that RFM achieves comparable or better results on standard image benchmarks and produces high-quality class-conditioned samples under high guidance weight.
Diversified Batch Selection for Training Acceleration
Authors: Feng Hong; Yueming Lyu; Jiangchao Yao; Ya Zhang; Ivor Tsang; Yanfeng Wang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption. To save cost, a prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. Although recent efforts achieve advancements by measuring the impact of each sample on generalization, their reliance on additional reference models inherently limits their practical applications, when there are no such ideal models available. On the other hand, the vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner, which sacrifices the diversity and induces the redundancy. To tackle this dilemma, we propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples. Specifically, we define a novel selection objective that measures the group-wise orthogonalized representativeness to combat the redundancy issue of previous sample-wise criteria, and provide a principled selection-efficient realization. Extensive experiments across various tasks demonstrate the significant superiority of DivBS in the performance-speedup trade-off. The code is publicly available.
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Authors: Didi Zhu; Zhongyisun Sun; Zexi Li; Tao Shen; Ke Yan; Shouhong Ding; Chao Wu; Kun Kuang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks. This paper presents a comprehensive analysis of catastrophic forgetting in MLLMs and introduces a post-training adjustment method called Model Tailor. Our method primarily preserves the pre-trained parameters while replacing a small number ($\leq$ 10%) of fine-tuned parameters, maintaining $\sim$ 99% effectiveness on original tasks versus pre-training, and achieving $\sim$ 97% on new tasks compared to standard fine-tuning. Specifically, we derive a sparse mask to identify the model patch, based on a fusion strategy that integrates salience and sensitivity analysis. Subsequently, a compensation mechanism is introduced to decorate the patch, enhancing the model's performance on both target and original tasks. Additionally, our method is adaptable to multi-task scenarios. Through extensive experiments on InstructBLIP and LLaVA-1.5 in both image captioning and visual question answering tasks, our approach demonstrates significant task adaptability while preserving inherent pre-trained capabilities.
AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training
Authors: Li Ding; Wen Fei; Yuyang Huang; Shuangrui Ding; Wenrui Dai; Chenglin Li; Junni Zou; Hongkai Xiong
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Low-bit integer training emerges as a promising approach to mitigate the heavy burden during network training by quantizing the weights, activations, and gradients. However, existing methods cannot well achieve mixed-precision quantization for low-bit training and are commonly limited to INT8 precision. In this paper, we propose a novel low-bit integer training framework that, for the first time, achieves adaptive mixed-precision allocation (AMPA) for weights, activations, and gradients, and pushes the boundaries to a precision level below INT8. We develop a novel magnitude-based sensitivity measurement with regard to the quantization losses of weight, activation, and gradient quantization and the average gradient magnitudes, which is demonstrated as an upper bound of quantization influence in theory. We further design a layer-wise precision update strategy under observations on the quantization losses and their effects on model performance in low-bit training. Extensive experiments on different backbones and datasets show that, compared to INT8 quantization, the proposed method can achieve more than 38% BitOPs reduction with a tolerable loss below 2% in image classification, image segmentation, and language modeling.
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Authors: Yiran Ding; Li Lyna Zhang; Chengruidong Zhang; Yuanyuan Xu; Ning Shang; Jiahang Xu; Fan Yang; Mao Yang
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to around 128k tokens. This paper introduces LongRoPE that, for the first time, extends the context window of pre-trained LLMs to an impressive 2048k tokens, with up to only 1k fine-tuning steps at within 256k training lengths, while maintaining performance at the original short context window. This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance. Extensive experiments on LLaMA2 and Mistral across various tasks demonstrate the effectiveness of our method. Models extended via LongRoPE retain the original architecture with minor modifications to the positional embedding, and can reuse most pre-existing optimizations. Code is available at https://github.com/microsoft/LongRoPE
Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
Authors: Hao Wu; Huiyuan Wang; Kun Wang; Weiyan Wang; ChanganYe; Yangyu Tao; Chong Chen; Xian-Sheng Hua; Xiao Luo
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Fluid dynamics modeling has received extensive attention in the machine learning community. Although numerous graph neural network (GNN) approaches have been proposed for this problem, the problem of out-of-distribution (OOD) generalization remains underexplored. In this work, we propose a new large-scale dataset Prometheus which simulates tunnel and pool fires across various environmental conditions and builds an extensive benchmark of 12 baselines, which demonstrates that the OOD generalization performance is far from satisfactory. To tackle this, this paper introduces a new approach named Disentangled Graph ODE (DGODE), which learns disentangled representations for continuous interacting dynamics modeling. In particular, we utilize a temporal GNN and a frequency network to extract semantics from historical trajectories into node representations and environment representations respectively. To mitigate the potential distribution shift, we minimize the mutual information between invariant node representations and the discretized environment features using adversarial learning. Then, they are fed into a coupled graph ODE framework, which models the evolution using neighboring nodes and dynamical environmental context. In addition, we enhance the stability of the framework by perturbing the environment features to enhance robustness. Extensive experiments validate the effectiveness of DGODE compared with state-of-the-art approaches.
SMaRt: Improving GANs with Score Matching Regularity
Authors: Mengfei Xia; Yujun Shen; Ceyuan Yang; Ran Yi; Wenping Wang; Yong-jin Liu
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex. In this work, we revisit the mathematical foundations of GANs, and theoretically reveal that the native adversarial loss for GAN training is insufficient to fix the problem of $\textit{subsets with positive Lebesgue measure of the generated data manifold lying out of the real data manifold}$. Instead, we find that score matching serves as a promising solution to this issue thanks to its capability of persistently pushing the generated data points towards the real data manifold. We thereby propose to improve the optimization of GANs with score matching regularity (SMaRt). Regarding the empirical evidences, we first design a toy example to show that training GANs by the aid of a ground-truth score function can help reproduce the real data distribution more accurately, and then confirm that our approach can consistently boost the synthesis performance of various state-of-the-art GANs on real-world datasets with pre-trained diffusion models acting as the approximate score function. For instance, when training Aurora on the ImageNet $64\times64$ dataset, we manage to improve FID from 8.87 to 7.11, on par with the performance of one-step consistency model. Code is available at https://github.com/thuxmf/SMaRt.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Authors: Andrew Craig Cullen; Shijie Liu; Paul Montague; Sarah Monazam Erfani; Benjamin I. P. Rubinstein
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: In guaranteeing the absence of adversarial examples in an instance's neighbourhood, certification mechanisms play an important role in demonstrating neural net robustness. In this paper, we ask if these certifications can compromise the very models they help to protect? Our new *Certification Aware Attack* exploits certifications to produce computationally efficient norm-minimising adversarial examples $74$% more often than comparable attacks, while reducing the median perturbation norm by more than $10$%. While these attacks can be used to assess the tightness of certification bounds, they also highlight that releasing certifications can paradoxically reduce security.
Viewing Transformers Through the Lens of Long Convolutions Layers
Authors: Itamar Zimerman; Lior Wolf
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Despite their dominance in modern DL and, especially, NLP domains, transformer architectures exhibit sub-optimal performance on long-range tasks compared to recent layers that are specifically designed for this purpose. In this work, drawing inspiration from key attributes of longrange layers, such as state-space layers, linear RNN layers, and global convolution layers, we demonstrate that minimal modifications to the transformer architecture can significantly enhance performance on the Long Range Arena (LRA) benchmark, thus narrowing the gap with these specialized layers. We identify that two key principles for long-range tasks are (i) incorporating an inductive bias towards smoothness, and (ii) locality. As we show, integrating these ideas into the attention mechanism improves results with a negligible amount of additional computation and without any additional trainable parameters. Our theory and experiments also shed light on the reasons for the inferior performance of transformers on long-range tasks and identify critical properties that are essential for successfully capturing long-range dependencies.
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Authors: Itamar Zimerman; Moran Baruch; Nir Drucker; Gilad Ezov; Omri Soceanu; Lior Wolf
Venue: ICML 2024 (ICML 2024 Poster)

Abstract: Designing privacy-preserving DL solutions is a major challenge within the AI community. Homomorphic Encryption (HE) has emerged as one of the most promising approaches in this realm, enabling the decoupling of knowledge between a model owner and a data owner. Despite extensive research and application of this technology, primarily in CNNs, applying HE on transformer models has been challenging because of the difficulties in converting these models into a polynomial form. We break new ground by introducing the first polynomial transformer, providing the first demonstration of secure inference over HE with full transformers. This includes a transformer architecture tailored for HE, alongside a novel method for converting operators to their polynomial equivalent. This innovation enables us to perform secure inference on LMs and ViTs with several datasts and tasks. Our techniques yield results comparable to traditional models, bridging the performance gap with transformers of similar scale and underscoring the viability of HE for state-of-the-art applications. Finally, we assess the stability of our models and conduct a series of ablations to quantify the contribution of each model component. Our code is publicly available.