GenAI4ELab/papercli-papers huggingface dataset:
Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection
Authors: Dongsu Song; Daehwa Ko; Jay Hoon Jung
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: It is well known that query-based attacks tend to have relatively higher success
rates in adversarial black-box attacks. While research on black-box attacks is actively
being conducted, relatively few studies have focused on pixel attacks that
target only a limited number of pixels. In image classification, query-based pixel
attacks often rely on patches, which heavily depend on randomness and neglect
the fact that scattered pixels are more suitable for adversarial attacks. Moreover, to
the best of our knowledge, query-based pixel attacks have not been explored in the
field of object detection. To address these issues, we propose a novel pixel-based
black-box attack called Remember and Forget Pixel Attack using Reinforcement
Learning(RFPAR), consisting of two main components: the Remember and Forget
processes. RFPAR mitigates randomness and avoids patch dependency by
leveraging rewards generated through a one-step RL algorithm to perturb pixels.
RFPAR effectively creates perturbed images that minimize the confidence scores
while adhering to limited pixel constraints. Furthermore, we advance our proposed
attack beyond image classification to object detection, where RFPAR reduces
the confidence scores of detected objects to avoid detection. Experiments
on the ImageNet-1K dataset for classification show that RFPAR outperformed
state-of-the-art query-based pixel attacks. For object detection, using the MSCOCO
dataset with YOLOv8 and DDQ, RFPAR demonstrates comparable mAP
reduction to state-of-the-art query-based attack while requiring fewer query. Further
experiments on the Argoverse dataset using YOLOv8 confirm that RFPAR
effectively removed objects on a larger scale dataset. Our code is available at
https://github.com/KAU-QuantumAILab/RFPAR.
DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection
Authors: Sheng Yan; Cunhang Fan; Hongyu Zhang; Xiaoke Yang; Jianhua Tao; Zhao Lv
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. 
However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model's ability to decode brain activity.
To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion \& classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model's ability to capture long-range latent dependencies. The feature fusion \& classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results.
The experimental results indicate that DARNet achieved excellent classification performance, particularly under short decision windows. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91\%. Code is available at: https://github.com/fchest/DARNet.git.
A Full-duplex Speech Dialogue Scheme Based On Large Language Model
Authors: Peng Wang; Songshuo Lu; Yaohua Tang; Sijie Yan; Wei Xia; Yuanjun Xiong
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We present a generative dialogue system capable of operating in a full-duplex manner, allowing for seamless interaction. It is based on a large language model (LLM) carefully aligned to be aware of a perception module, a motor function module, and the concept of a simple finite state machine (called neural FSM) with two states. The perception and motor function modules operate in tandem, allowing the system to speak and listen to the user simultaneously. The LLM generates textual tokens for inquiry responses and makes autonomous decisions to start responding to, wait for, or interrupt the user by emitting control tokens to the neural FSM. All these tasks of the LLM are carried out as next token prediction on a serialized view of the dialogue in real-time. In automatic quality evaluations simulating real-life interaction, the proposed system reduces the average conversation response latency by more than threefold compared with LLM-based half-duplex dialogue systems while responding within less than 500 milliseconds in more than 50% of evaluated interactions. Running an LLM with only 8 billion parameters, our system exhibits an 8% higher interruption precision rate than the best available commercial LLM for voice-based dialogue.
Beyond task diversity: provable representation transfer for sequential multitask linear bandits
Authors: Thang Duong; Zhi Wang; Chicheng Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We study lifelong learning in linear bandits, where a learner interacts with a sequence of linear bandit tasks whose parameters lie in an $m$-dimensional subspace of $\mathbb{R}^d$, thereby sharing a low-rank representation. Current literature typically assumes that the tasks are diverse, i.e., their parameters uniformly span the $m$-dimensional subspace. This assumption allows the low-rank representation to be learned before all tasks are revealed, which can be unrealistic in real-world applications. In this work, we present the first nontrivial result for sequential multi-task linear bandits without the task diversity assumption. We develop an algorithm that efficiently learns and transfers low-rank representations. When facing $N$ tasks, each played over $\tau$ rounds, our algorithm achieves a regret guarantee of $\tilde{O}\big (Nm \sqrt{\tau} + N^{\frac{2}{3}} \tau^{\frac{2}{3}} d m^{\frac13} + Nd^2 + \tau m d \big)$ under the ellipsoid action set assumption.
This result can significantly improve upon the baseline of $\tilde{O} \left (Nd \sqrt{\tau}\right)$ that does not leverage the low-rank structure when the number of tasks $N$ is sufficiently large and $m \ll d$. We also demonstrate empirically on synthetic data that our algorithm outperforms baseline algorithms, which rely on the task diversity assumption.
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Authors: Bo Chen; Zhilei Bei; Xingyi Cheng; Pan Li; Jie Tang; Le Song
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high-quality MSA. Although various methods have been proposed to generate high-quality MSA under these conditions, they fall short in comprehensively capturing the intricate co-evolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pre-training in a low-MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model the complex evolutionary patterns. Endowed by this, the flexible 1D MSA decoding framework facilitates zero- or few-shot learning. Moreover, we demonstrate leveraging the feedback from AlphaFold2 (AF2) can further enhance the model’s capacity via Rejective Fine-tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful and informative MSA (up to +8.5% TM-Score on few-shot scenarios). The transfer learning also demonstrates its great potential for the wide range of tasks resorting to the quality of MSA.
Learning Optimal Tax Design in Nonatomic Congestion Games
Authors: Qiwen Cui; Maryam Fazel; Simon Shaolei Du
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In multiplayer games, self-interested behavior among the players can harm the social welfare. Tax mechanisms are a common method to alleviate this issue and induce socially optimal behavior. In this work, we take the initial step of learning the optimal tax that can maximize social welfare with limited feedback in congestion games. We propose a new type of feedback named \emph{equilibrium feedback}, where the tax designer can only observe the Nash equilibrium after deploying a tax plan. Existing algorithms are not applicable due to the exponentially large tax function space, nonexistence of the gradient, and nonconvexity of the objective. To tackle these challenges, we design a computationally efficient algorithm that leverages several novel components: (1) a piece-wise linear tax to approximate the optimal tax; (2) extra linear terms to guarantee a strongly convex potential function; (3) an efficient subroutine to find the exploratory tax that can provide critical information about the game. The algorithm can find an $\epsilon$-optimal tax with $O(\beta F^2/\epsilon)$ sample complexity, where $\beta$ is the smoothness of the cost function and $F$ is the number of facilities.
Training Compute-Optimal Protein Language Models
Authors: Xingyi Cheng; Bo Chen; Pan Li; Jing Gong; Jie Tang; Le Song
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited.
Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the efficient compute frontier that balances performance and compute budgets.
Our investigation is grounded in a massive dataset consisting of 939 million protein sequences. 
We trained over 300 models ranging from 3.5 million to 10.7 billion parameters on 5 to 200 billion unique tokens, to investigate the relations between model sizes, training token numbers, and objectives.
First, we observed the effect of diminishing returns for the Causal Language Model (CLM) and that of overfitting for Masked Language Model (MLM) when repeating the commonly used Uniref database. To address this, we included metagenomic protein sequences in the training set to increase the diversity and avoid the plateau or overfitting effects. 
Second, we obtained the scaling laws of CLM and MLM on Transformer, tailored to the specific characteristics of protein sequence data. 
Third, we observe a transfer scaling phenomenon from CLM to MLM, further demonstrating the effectiveness of transfer through scaling behaviors based on estimated Effectively Transferred Tokens.
Finally, to validate our scaling laws, we compare the large-scale versions of ESM-2 and PROGEN2 on downstream tasks, encompassing evaluations of protein generation as well as structure- and function-related tasks, all within less or equivalent pre-training compute budgets.
Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints
Authors: Guanyu Nie; Vaneet Aggarwal; Christopher John Quinn
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In this paper, we consider the problem of online monotone DR-submodular maximization subject to long-term stochastic constraints. Specifically, at each round $t\in [T]$, after committing an action $\mathbf{x}_t$, a random reward $f_t(\mathbf{x}_t)$ and an unbiased gradient estimate of the point $\widetilde{\nabla}f_t(\mathbf{x}_t)$ (semi-bandit feedback) are revealed. Meanwhile, a budget of $g_t(\mathbf{x}_t)$, which is linear and stochastic, is consumed of its total allotted budget $B_T$. We propose a gradient ascent based algorithm that achieves $\frac{1}{2}$-regret of $\mathcal{O}(\sqrt{T})$ with $\mathcal{O}(T^{3/4})$ constraint violation with high probability. Moreover, when first-order full-information feedback is available, we propose an algorithm that achieves $(1-1/e)$-regret of $\mathcal{O}(\sqrt{T})$ with $\mathcal{O}(T^{3/4})$ constraint violation. These algorithms significantly improve over the state-of-the-art in terms of query complexity.
Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
Authors: Sujai Hiremath; Jacqueline R. M. A. Maasch; Mengxiao Gao; Promit Ghosal; Kyra Gan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Learning the unique directed acyclic graph corresponding to an unknown causal model is a challenging task. Methods based on functional causal models can identify a unique graph, but either suffer from the curse of dimensionality or impose strong parametric assumptions. To address these challenges, we propose a novel hybrid approach for global causal discovery in observational data that leverages local causal substructures. We first present a topological sorting algorithm that leverages ancestral relationships in linear structural causal models to establish a compact top-down hierarchical ordering, encoding more causal information than linear orderings produced by existing methods. We demonstrate that this approach generalizes to nonlinear settings with arbitrary noise. We then introduce a nonparametric constraint-based algorithm that prunes spurious edges by searching for local conditioning sets, achieving greater accuracy than current methods. We provide theoretical guarantees for correctness and worst-case polynomial time complexities, with empirical validation on synthetic data.
Improving Alignment and Robustness with Circuit Breakers
Authors: Andy Zou; Long Phan; Justin Wang; Derek Duenas; Maxwell Lin; Maksym Andriushchenko; J Zico Kolter; Matt Fredrikson; Dan Hendrycks
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with "circuit breakers." Existing techniques aimed at improving alignment, such as refusal training, are often bypassed. Techniques such as adversarial training try to plug these holes by countering specific attacks. As an alternative to refusal training and adversarial training, circuit-breaking directly controls the representations that are responsible for harmful outputs in the first place. Our technique can be applied to both text-only and multimodal language models to prevent the generation of harmful outputs without sacrificing utility -- even in the presence of powerful unseen attacks. Notably, while adversarial robustness in standalone image recognition remains an open challenge, circuit breakers allow the larger multimodal system to reliably withstand image "hijacks" that aim to produce harmful content. Finally, we extend our approach to AI agents, demonstrating considerable reductions in the rate of harmful actions when they are under attack. Our approach represents a significant step forward in the development of reliable safeguards to harmful behavior and adversarial attacks.
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
Authors: Zekun Shi; Zheyuan Hu; Min Lin; Kenji Kawaguchi
Venue: NeurIPS 2024 (NeurIPS 2024 oral)

Abstract: Optimizing neural networks with loss that contain high-dimensional and high-order differential operators
  is expensive to evaluate with back-propagation due to $\mathcal{O}(d^{k})$ scaling of the derivative tensor size and the $\mathcal{O}(2^{k-1}L)$ scaling in the computation graph, where $d$ is the dimension of the domain, $L$ is the number of ops in the forward computation graph, and $k$ is the derivative order. In previous works, the polynomial scaling in $d$ was addressed by amortizing the computation over the optimization process via randomization. Separately, the exponential scaling in $k$ for univariate functions ($d=1$) was addressed with high-order auto-differentiation (AD). In this work, we show how to efficiently perform arbitrary contraction of the derivative tensor of arbitrary order for multivariate functions, by properly constructing the input tangents to univariate high-order AD, which can be used to efficiently randomize any differential operator.
  When applied to Physics-Informed Neural Networks (PINNs), our method provides >1000$\times$ speed-up and >30$\times$ memory reduction over randomization with first-order AD, and we can now solve 1-million-dimensional PDEs in 8 minutes on a single NVIDIA A100 GPU. This work opens the possibility of using high-order differential operators in large-scale problems.
Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models
Authors: Ziyi Zhou; Xinwei Guo; Jiashi Gao; Xiangyu Zhao; Shiyao Zhang; Xin Yao; Xuetao Wei
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities, surpassing human experts in various benchmark tests and playing a vital role in various industry sectors. Despite their effectiveness, a notable drawback of LLMs is their inconsistent moral behavior, which raises ethical concerns. This work delves into symmetric moral consistency in large language models and demonstrates that modern LLMs lack sufficient consistency ability in moral scenarios. Our extensive investigation of twelve popular LLMs reveals that their assessed consistency scores are influenced by position bias and selection bias rather than their intrinsic abilities. We propose a new framework tSMC, which gauges the effects of these biases and effectively mitigates the bias impact based on the Kullback–Leibler divergence to pinpoint LLMs' mitigated Symmetric Moral Consistency. We find that the ability of LLMs to maintain consistency varies across different moral scenarios. Specifically, LLMs show more consistency in scenarios with clear moral answers compared to those where no choice is morally perfect. The average consistency score of 12 LLMs ranges from $60.7\%$ in high-ambiguity moral scenarios to $84.8\%$ in low-ambiguity moral scenarios.
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
Authors: Shuhao Chen; Weisen Jiang; Baijiong Lin; James Kwok; Yu Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, which learns a router to select the most suitable LLM for each query. However, existing routing models are ineffective when multiple LLMs perform well for a query. To address this problem, in this paper, we propose a method called query-based Router by Dual Contrastive learning (RouterDC). The RouterDC model, which consists of an encoder and LLM embeddings, is trained by two proposed contrastive losses (sample-LLM and sample-sample losses). Experimental results show that RouterDC is effective in assembling LLMs and largely outperforms individual top-performing LLMs as well as existing routing methods on both in-distribution (+2.76\%) and out-of-distribution (+1.90\%) tasks. The source code is available at https://github.com/shuhao02/RouterDC.
GraphVis: Boosting LLMs with Visual Knowledge Graph Integration
Authors: Yihe Deng; Chenchen Ye; Zijie Huang; Mingyu Derek Ma; Yiwen Kou; Wei Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The rapid evolution of large language models (LLMs) has expanded their capabilities across various data modalities, extending from well-established image data to increasingly popular graph data. Given the limitation of LLMs in hallucinations and inaccuracies in recalling factual knowledge, Knowledge Graph (KG) has emerged as a crucial data modality to support more accurate reasoning by LLMs. However, integrating structured knowledge from KGs into LLMs remains challenging, as most current KG-enhanced LLM methods directly convert the KG into linearized text triples, which is not as expressive as the original structured data. To address this, we introduce GraphVis, which conserves the intricate graph structure through the visual modality to enhance the comprehension of KGs with the aid of Large Vision Language Models (LVLMs). Our approach incorporates a unique curriculum fine-tuning scheme which first instructs LVLMs to recognize basic graphical features from the images, and subsequently incorporates reasoning on QA tasks with the visual graphs. This cross-modal methodology not only markedly enhances performance on standard textual QA  but also shows improved zero-shot VQA performance by utilizing synthetic graph images to augment the data for VQA tasks. We present comprehensive evaluations across commonsense reasoning QA benchmarks, where GraphVis provides an average improvement of 11.1% over its base model and outperforms existing KG-enhanced LLM approaches. Across VQA benchmarks such as ScienceQA that share similar scientific diagram images, GraphVis provides a notable gain of 4.32%.
Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products
Authors: Bethia Sun; Maurice Pagnucco; Yang Song
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Since the inception of the classicalist vs. connectionist debate, it has been argued that the ability to systematically combine  symbol-like entities into compositional representations is crucial for human intelligence. In connectionist systems, the field of disentanglement has gained prominence for its ability to produce explicitly compositional representations; however, it relies on a fundamentally *symbolic, concatenative* representation of compositional structure that clashes with the *continuous, distributed* foundations of deep learning. To resolve this tension, we extend Smolensky's Tensor Product Representation (TPR) and introduce *Soft TPR*, a representational form that encodes compositional structure in an inherently *distributed, flexible* manner, along with *Soft TPR Autoencoder*, a theoretically-principled architecture designed specifically to learn Soft TPRs.  Comprehensive evaluations in the visual representation learning domain demonstrate that the Soft TPR framework consistently outperforms conventional disentanglement alternatives -- achieving state-of-the-art disentanglement, boosting representation learner convergence, and delivering superior sample efficiency and low-sample regime performance in downstream tasks. These findings highlight the promise of a *distributed* and *flexible* approach to representing compositional structure by potentially enhancing alignment with the core principles of deep learning over the conventional symbolic approach.