GenAI4ELab/papercli-papers huggingface dataset:
An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
Authors: Dong Li; Aijia Zhang; Junqi Gao; Biqing Qi
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph incremental learning has gained widespread attention for its ability to mitigate catastrophic forgetting for graph neural networks (GNN). Conventional methods typically require numerous labels for node classification. However, obtaining abundant labels is often challenging in practice, which makes graph few-shot incremental learning necessary. Current approaches rely on large number of samples from meta-learning to construct memories, and heavy fine-tuning of the GNN parameters that lead to the loss of past knowledge. These result in significant memory consumption and loss of past knowledge information, respectively. To tackle these issues, We introduce Mecoin to efficient construct and Preserve memory. For efficient storage and update of class prototypes, Mecoin use Structured Memory Unit (SMU) to cache prototypes of the seen classes and update new class prototypes through interaction between nodes and the cached prototypes by Memory Construction module(MeCo). Besides, to avoid extensive parameter fine-tuning and forgetting, we introduce a Memory Representation Adaptive Module called MRaM to separate the learning of prototypes and class representations and use Graph Knowledge Interchange Module (GKIM) to injects past knowledge information into GNN. We analyze the effectiveness of our paradigm from the perspectives of generalization error, and discuss the impact of different distillation methods on model performance through experiments and VC-dimension. By comparison with other related methods, we validate that Mecoin achieves higher accuracy and lower forgetting rate.
Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
Authors: Wenjie Fu; Huandong Wang; Chen Gao; Guanghua Liu; Yong Li; Tao Jiang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear promising performance by calibrating the probability measured on the target model with reference models, this illusion of privacy risk heavily depends on a reference dataset that closely resembles the training set. Both two types of attacks are predicated on the hypothesis that training records consistently maintain a higher probability of being sampled. However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. Thus, these reasons lead to high false-positive rates of MIAs in practical scenarios.
We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA). 
Specifically, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs.
Furthermore, we introduce probabilistic variation, a more reliable membership signal based on LLM memorization rather than overfitting, from which we rediscover the neighbour attack with theoretical grounding. 
Comprehensive evaluation conducted on three datasets and four exemplary LLMs shows that SPV-MIA raises the AUC of MIAs from 0.7 to a significantly high level of 0.9. Our code and dataset are available at: https://github.com/tsinghua-fib-lab/NeurIPS2024_SPV-MIA
Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework
Authors: Zhihai Wang; Jie Wang; Qingyue Yang; Yinqi Bai; Xing Li; Lei Chen; Jianye HAO; Mingxuan Yuan; Bin Li; Yongdong Zhang; Feng Wu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Logic Synthesis (LS) aims to generate an optimized logic circuit satisfying a given functionality, which generally consists of circuit translation and optimization. It is a challenging and fundamental combinatorial optimization problem in integrated circuit design. Traditional LS approaches rely on manually designed heuristics to tackle the LS task, while machine learning recently offers a promising approach towards next-generation logic synthesis by neural circuit generation and optimization. In this paper, we first revisit the application of differentiable neural architecture search (DNAS) methods to circuit generation and found from extensive experiments that existing DNAS methods struggle to exactly generate circuits, scale poorly to large circuits, and exhibit high sensitivity to hyper-parameters. Then we provide three major insights for these challenges from extensive empirical analysis: 1) DNAS tends to overfit to too many skip-connections, consequently wasting a significant portion of the network's expressive capabilities; 2) DNAS suffers from the structure bias between the network architecture and the circuit inherent structure, leading to inefficient search; 3) the learning difficulty of different input-output examples varies significantly, leading to severely imbalanced learning. To address these challenges in a systematic way, we propose a novel regularized triangle-shaped circuit network generation framework, which leverages our key insights for completely accurate and scalable circuit generation. Furthermore, we propose an evolutionary algorithm assisted by reinforcement learning agent restarting technique for efficient and effective neural circuit optimization. Extensive experiments on four different circuit benchmarks demonstrate that our method can precisely generate circuits with up to 1200 nodes. Moreover, our synthesized circuits significantly outperform the state-of-the-art results from several competitive winners in IWLS 2022 and 2023 competitions.
On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
Authors: Yubo Ye; Maryam Toloubidokhti; Sumeet Vadhavkar; Xiajun Jiang; Huafeng Liu; Linwei Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of _hybrid deep generative models (hybrid-DGMs)_  that integrates known physics-based mathematical expressions in neural generative models. To identify these hybrid-DGMs requires inferring parameters of the physics-based component along with their neural component. The identifiability of these hybrid-DGMs, however, has not yet been theoretically probed or established. How does the existing theory of the un-identifiability of general DGMs apply to hybrid-DGMs? What may be an effective approach to consutrct a hybrid-DGM with theoretically-proven identifiability? This paper provides the first theoretical probe into the identifiability of hybrid-DGMs, and present meta-learning as a novel solution to construct identifiable hybrid-DGMs. On synthetic and real-data benchmarks, we provide strong empirical evidence for the un-identifiability of existing hybrid-DGMs using unconditional priors, and strong identifiability results of the presented meta-formulations of hybrid-DGMs.
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization
Authors: Haocheng Luo; Tuan Truong; Tung Pham; Mehrtash Harandi; Dinh Phung; Trung Le
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles remain poorly understood. In this work, we analyze SAM’s training dynamics using the maximum eigenvalue of the Hessian as a measure of sharpness and propose a third-order stochastic differential equation (SDE), which reveals that the dynamics are driven by a complex mixture of second- and third-order terms. We show that alignment between the perturbation vector and the top eigenvector is crucial for SAM’s effectiveness in regularizing sharpness, but find that this alignment is often inadequate in practice, which limits SAM's efficiency. Building on these insights, we introduce Eigen-SAM, an algorithm that explicitly aims to regularize the top Hessian eigenvalue by aligning the perturbation vector with the leading eigenvector. We validate the effectiveness of our theory and the practical advantages of our proposed approach through comprehensive experiments. Code is available at https://github.com/RitianLuo/EigenSAM.
CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors
Authors: Linye Lyu; Jiawei Zhou; Daojing He; YU LI
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Prior works on physical adversarial camouflage against vehicle detectors mainly focus on the effectiveness and robustness of the attack. The current most successful methods optimize 3D vehicle texture at a pixel level. However, this results in conspicuous and attention-grabbing patterns in the generated camouflage, which humans can easily identify. To address this issue, we propose a Customizable and Natural Camouflage Attack (CNCA) method by leveraging an off-the-shelf pre-trained diffusion model. By sampling the optimal texture image from the diffusion model with a user-specific text prompt, our method can generate natural and customizable adversarial camouflage while maintaining high attack performance. With extensive experiments on the digital and physical worlds and user studies, the results demonstrate that our proposed method can generate significantly more natural-looking camouflage than the state-of-the-art baselines while achieving competitive attack performance.
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
Authors: Atli Kosson; Bettina Messmer; Martin Jaggi
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Learning Rate Warmup is a popular heuristic for training neural networks, especially at larger batch sizes, despite limited understanding of its benefits. Warmup decreases the update size $\Delta \mathbf{w}_t = \eta_t \mathbf{u}_t$ early in training by using lower values for the learning rate $\eta_t$. In this work we argue that warmup benefits training by keeping the overall size of $\Delta \mathbf{w}_t$ limited, counteracting large initial values of $\mathbf{u}_t$. Focusing on small-scale GPT training with AdamW/Lion, we explore the following question: *Why and by which criteria are early updates $\mathbf{u}_t$ too large?* We analyze different metrics for the update size including the $\ell_2$-norm, resulting directional change, and impact on the representations of the network, providing a new perspective on warmup. In particular, we find that warmup helps counteract large angular updates as well as a limited critical batch size early in training. Finally, we show that the need for warmup can be significantly reduced or eliminated by modifying the optimizer to explicitly normalize $\mathbf{u}_t$ based on the aforementioned metrics.
On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
Authors: Yu Xiang; Jie Qiao; Zefeng Liang; Zihuai Zeng; Ruichu Cai; Zhifeng Hao
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Causal discovery from observational data, especially for count data, is essential across scientific and industrial contexts, such as biology, economics, and network operation maintenance. For this task, most approaches model count data using Bayesian networks or ordinal relations. However, they overlook the inherent branching structures that are frequently encountered, e.g., a browsing event might trigger an adding cart or purchasing event. This can be modeled by a binomial thinning operator (for branching) and an additive independent Poisson distribution (for noising), known as Poisson Branching Structure Causal Model (PB-SCM). There is a provably sound cumulant-based causal discovery method that allows the identification of the causal structure under a branching structure. However, we show that there still remains a gap in that there exist causal directions that are identifiable while the algorithm fails to identify them. In this work, we address this gap by exploring the identifiability of PB-SCM using the Probability Generating Function (PGF). By developing a compact and exact closed-form solution for the PGF of PB-SCM, we demonstrate that each component in this closed-form solution uniquely encodes a specific local structure, enabling the identification of the local structures by testing their corresponding component appearances in the PGF. Building on this, we propose a practical algorithm for learning causal skeletons and identifying causal directions of PB-SCM using PGF. The effectiveness of our method is demonstrated through experiments on both synthetic and real datasets.
Time-Constrained Robust MDPs
Authors: Adil Zouitine; David Bertoin; Pierre Clavier; Matthieu Geist; Emmanuel Rachelson
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Robust reinforcement learning is essential for deploying reinforcement learning algorithms in real-world scenarios where environmental uncertainty predominates.
Traditional robust reinforcement learning often depends on rectangularity assumptions, where adverse probability measures of outcome states are assumed to be independent across different states and actions. 
This assumption, rarely fulfilled in practice, leads to overly conservative policies. 
To address this problem, we introduce a new time-constrained robust MDP (TC-RMDP) formulation that considers multifactorial, correlated, and time-dependent disturbances, thus more accurately reflecting real-world dynamics. This formulation goes beyond the conventional rectangularity paradigm, offering new perspectives and expanding the analytical framework for robust RL.
We propose three distinct algorithms, each using varying levels of environmental information, and evaluate them extensively on continuous control benchmarks. 
Our results demonstrate that these algorithms yield an efficient tradeoff between performance and robustness, outperforming traditional deep robust RL methods in time-constrained environments while preserving robustness in classical benchmarks.
This study revisits the prevailing assumptions in robust RL and opens new avenues for developing more practical and realistic RL applications.
Robust Sparse Regression with Non-Isotropic Designs
Authors: Chih-Hung Liu; Gleb Novikov
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive.
Consider the model $y^*=X^*\beta^*+ \eta$ where $X^*$ is an $n\times d$ random design matrix, $\beta^*\in \mathbb{R}^d$ is a $k$-sparse vector, and the noise $\eta$ is independent of $X^*$ and chosen by the \emph{oblivious adversary}. 
Apart from the independence of $X^*$, we only require a small fraction entries of $\eta$ to have magnitude at most $1$. 
The \emph{adaptive adversary} is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the samples $(X_1^*, y_1^*),\ldots, (X_n^*, y_n^*)$.
Given the $\varepsilon$-corrupted samples $(X_1, y_1),\ldots, (X_n, y_n)$, the goal is to estimate $\beta^*$.  
We assume that the rows of $X^*$ are iid samples from some $d$-dimensional distribution $\mathcal{D}$  with zero mean and (unknown) covariance matrix $\Sigma$ with bounded condition number.

We design several robust algorithms that outperform the state of the art even in the special case of Gaussian noise $\eta \sim N(0,1)^n$. 
In particular, we provide a polynomial-time algorithm that with high probability recovers $\beta^*$ up to error $O(\sqrt{\varepsilon})$  as long as  $n \ge \tilde{O}(k^2/\varepsilon)$, only assuming some bounds on the third and the fourth moments of $\mathcal{D}$. 
In addition, prior to this work, even in the special case of Gaussian design $\mathcal{D} = N(0,\Sigma)$ and noise $\eta \sim N(0,1)$, no polynomial time algorithm was known to achieve error $o(\sqrt{\varepsilon})$ in the sparse setting $n < d^2$.  
We show that under some assumptions on the fourth and the eighth moments of $\mathcal{D}$, there is a polynomial-time algorithm that achieves error $o(\sqrt{\varepsilon})$ as long as $n \ge \tilde{O}(k^4 / \varepsilon^3)$. 
For Gaussian distribution $\mathcal{D} = N(0,\Sigma)$, this algorithm achieves error $O(\varepsilon^{3/4})$. 
Moreover, our algorithm achieves error $o(\sqrt{\varepsilon})$ for all log-concave distributions if $\varepsilon \le 1/\text{polylog(d)}$. 

Our algorithms are based on the filtering  of the covariates that uses sum-of-squares relaxations, and weighted Huber loss minimization with $\ell_1$ regularizer.  We provide a novel analysis of weighted penalized Huber loss that is suitable for heavy-tailed designs in the presence of two adversaries. Furthermore, we complement our algorithmic results with Statistical Query lower bounds, providing evidence that our estimators are likely to have nearly optimal sample complexity.
Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards
Authors: Qinwei Yang; Xueqing Liu; Yan Zeng; Ruocheng Guo; Yang Liu; Peng Wu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Learning the optimal policy to balance multiple short-term and long-term rewards has extensive applications across various domains. Yet, there is a noticeable scarcity of research addressing policy learning strategies in this context. In this paper, we aim to learn the optimal policy capable of effectively balancing multiple short-term and long-term rewards, especially in scenarios where the long-term outcomes are often missing due to data collection challenges over extended periods. Towards this goal, the conventional linear weighting method, which aggregates multiple rewards into a single surrogate reward through weighted summation, can only achieve sub-optimal policies when multiple rewards are related. Motivated by this, we propose a novel decomposition-based policy learning (DPPL) method that converts the whole problem into subproblems. The DPPL method is capable of obtaining optimal policies even when multiple rewards are interrelated. Nevertheless, the DPPL method requires a set of preference vectors specified in advance, posing challenges in practical applications where selecting suitable preferences is non-trivial. To mitigate this, we further theoretically transform the optimization problem in DPPL into an $\varepsilon$-constraint problem, where $\varepsilon$ represents the minimum acceptable levels of other rewards while maximizing one reward. This transformation provides intuitive into the selection of preference vectors. Extensive experiments are conducted on the proposed method and the results validate the effectiveness of the method.
On Softmax Direct Preference Optimization for Recommendation
Authors: Yuxin Chen; Junfei Tan; An Zhang; Zhengyi Yang; Leheng Sheng; Enzhi Zhang; Xiang Wang; Tat-Seng Chua
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities. Most of the LM-based recommenders convert historical interactions into language prompts, pairing with a positive item as the target response and fine-tuning LM with a language modeling loss. However, the current objective fails to fully leverage preference data and is not optimized for personalized ranking tasks, which hinders the performance of LM-based recommenders. Inspired by the current advancement of Direct Preference Optimization (DPO) in human preference alignment and the success of softmax loss in recommendations, we propose Softmax-DPO (\textbf{S-DPO}) to instill ranking information into the LM to help LM-based recommenders distinguish preferred items from negatives, rather than solely focusing on positives. Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders, which is extended from the traditional full-ranking Plackett-Luce (PL) model to partial rankings and connected to softmax sampling strategies. Theoretically, we bridge S-DPO with the softmax loss over negative sampling and find that it has an inherent benefit of mining hard negatives, which assures its exceptional capabilities in recommendation tasks. Empirically, extensive experiments conducted on three real-world datasets demonstrate the superiority of S-DPO to effectively model user preference and further boost recommendation performance while providing better rewards for preferred items. Our codes are available at https://github.com/chenyuxin1999/S-DPO.
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
Authors: Louis Serrano; Thomas X Wang; Etienne Le Naour; Jean-Noël Vittaut; Patrick Gallinari
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.
WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off
Authors: Eva Giboulot; Teddy Furon
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Watermarking is a technical means to dissuade malfeasant usage of Large Language Models.
This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM.
Its new design leaves the LLM untouched (no modification of the weights, logits or temperature).
WaterMax balances robustness and  computational complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness.
Its performance is both theoretically proven and experimentally validated.
It outperforms all the SotA techniques under the most complete benchmark suite.
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
Authors: Chaoya Jiang; Jia Hongrui; Haiyang Xu; Wei Ye; Mengfan Dong; Ming Yan; Ji Zhang; Fei Huang; Shikun Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image reasoning. Current MLLMs primarily focus on single-image visual understanding, limiting their ability to interpret and integrate information across multiple images. MaVEn addresses this limitation by combining discrete visual symbol sequences, which abstract coarse-grained semantic concepts, with traditional continuous representation sequences that model fine-grained features. This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively. Additionally, we design a dynamic reduction mechanism by for long-sequence continuous features to enhance multi-image processing efficiency. Experimental results demonstrate that MaVEn significantly enhances MLLMs' understanding in complex multi-image scenarios, while also improving performance in single-image contexts.