GenAI4ELab/papercli-papers huggingface dataset:
IPO: Interpretable Prompt Optimization for Vision-Language Models
Authors: Yingjun Du; Wenfang Sun; Cees G. M. Snoek
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful prompt template engineering. Instead, current approaches to prompt optimization learn the prompts through gradient descent, where the prompts are treated as adjustable parameters. However, these methods tend to lead to overfitting of the base classes seen during training and produce prompts that are no longer understandable by humans. This paper introduces a simple but interpretable prompt optimizer (IPO), that utilizes large language models (LLMs) to generate textual prompts dynamically. We introduce a Prompt Optimization Prompt that not only guides LLMs in creating effective prompts but also stores past prompts with their performance metrics, providing rich in-context information. Additionally, we incorporate a large multimodal model (LMM) to condition on visual content by generating image descriptions, which enhance the interaction between textual and visual modalities. This allows for the creation of dataset-specific prompts that improve generalization performance, while maintaining human comprehension. Extensive testing across 11 datasets reveals that IPO not only improves the accuracy of existing gradient-descent-based prompt learning methods but also considerably enhances the interpretability of the generated prompts. By leveraging the strengths of LLMs, our approach ensures that the prompts remain human-understandable, thereby facilitating better transparency and oversight for vision-language models.
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Authors: Omead Pooladzandi; Sunay Gajanan Bhat; Jeffrey Jiang; Alexander Branch; Gregory Pottie
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often reduce generalization performance, are attack-specific, and impose significant training overhead. To address this, we introduce a set of universal data purification methods using a stochastic transform, $\Psi(x)$, realized via iterative Langevin dynamics of Energy-Based Models (EBMs), Denoising Diffusion Probabilistic Models (DDPMs), or both. These approaches purify poisoned data with minimal impact on classifier generalization. Our specially trained EBMs and DDPMs provide state-of-the-art defense against various attacks (including Narcissus, Bullseye Polytope, Gradient Matching) on CIFAR-10, Tiny-ImageNet, and CINIC-10, without needing attack or classifier-specific information. We discuss performance trade-offs and show that our methods remain highly effective even with poisoned or distributionally shifted generative model training data.
Idiographic Personality Gaussian Process for Psychological Assessment
Authors: Yehu Chen; Muchen Xi; Joshua J. Jackson; Jacob Montgomery; Roman Garnett
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We develop a novel measurement framework based on Gaussian process coregionalization model to address a long-lasting debate in psychometrics: whether psychological features like personality share a common structure across the population or vary uniquely for individuals. We propose idiographic personality Gaussian process (IPGP), an intermediate model that accommodates both shared trait structure across individuals and "idiographic" deviations. IPGP leverages the Gaussian process coregionalization model to conceptualize responses of grouped survey batteries but adjusted to non-Gaussian ordinal data, and exploits stochastic variational inference for latent factor estimation. Using both synthetic data and a novel survey, we show that IPGP improves both prediction of actual responses and estimation of intrapersonal response patterns compared to existing benchmarks. In the survey study, IPGP also identifies unique clusters of personality taxonomies, displaying great potential in advancing individualized approaches to psychological diagnosis.
Graph Diffusion Transformers for Multi-Conditional Molecular Generation
Authors: Gang Liu; Jiaxin Xu; Tengfei Luo; Meng Jiang
Venue: NeurIPS 2024 (NeurIPS 2024 oral)

Abstract: Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT has a condition encoder to learn the representation of numerical and categorical properties and utilizes a Transformer-based graph denoiser to achieve molecular graph denoising under conditions. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, we propose a graph-dependent noise model for training Graph DiT, designed to accurately estimate graph-related noise in molecules. We extensively validate the Graph DiT for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. A polymer inverse design task for gas separation with feedback from domain experts further demonstrates its practical utility. The code is available at https://github.com/liugangcode/Graph-DiT.
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Authors: Otmane Sakhi; Imad Aouali; Pierre Alquier; Nicolas Chopin
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, select, and learn new, potentially better-performing, policies. Motivated by critical applications, we move beyond point estimators. Instead, we adopt the principle of _pessimism_ where we construct upper bounds that assess a policy's worst-case performance, enabling us to confidently select and learn improved policies. Precisely, we introduce novel, fully empirical concentration bounds for a broad class of importance weighting risk estimators. These bounds are general enough to cover most existing estimators and pave the way for the development of new ones. In particular, our pursuit of the tightest bound within this class motivates a novel estimator (LS), that _logarithmically smoothes_ large importance weights. The bound for LS is provably tighter than its competitors, and naturally results in improved policy selection and learning strategies. Extensive policy evaluation, selection, and learning experiments highlight the versatility and favorable performance of LS.
Sequoia: Scalable and Robust Speculative Decoding
Authors: Zhuoming Chen; Avner May; Ruslan Svirschevski; Yu-Hsun Huang; Max Ryabinin; Zhihao Jia; Beidi Chen
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: As the usage of large language models (LLMs) grows, it becomes increasingly important to serve them quickly and efficiently. While speculative decoding has recently emerged as a promising direction for accelerating LLM serving, existing methods are limited in their ability to scale to larger speculation budgets and adapt to different hyperparameters. This paper introduces Sequoia, a scalable and robust algorithm for speculative decoding. To improve scalability, Sequoia introduces a dynamic programming algorithm to find an optimal tree structure for the speculated tokens. To achieve robust speculative decoding, Sequoia uses a novel sampling and verification method that outperforms prior work across different decoding temperatures. Sequoia improves the decoding speed of Llama2-7B, Llama2-13B, and Vicuna-33B on an A100 GPU by up to $4.04\times$, $3.73\times$, and $2.27 \times$. To serve Llama3-70B-Instruct on a single L40 GPU through offloading, Sequoia reduces the per-token decoding latency to 0.60 s/token, $9.5\times$ faster than DeepSpeed-Zero-Inference.
Transformer Doctor: Diagnosing and Treating Vision Transformers
Authors: Jiacong Hu; Hao Chen; Kejia Chen; Yang Gao; Jingwen Ye; Xingen Wang; Mingli Song; Zunlei Feng
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance.
Learning to Decouple the Lights for 3D Face Texture Modeling
Authors: Tianxin Huang; Zhenyu Zhang; Ying Tai; Gim Hee Lee
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions. 
Nevertheless, it remains challenging to recover accurate facial textures from scenarios with complicated illumination affected by external occlusions, \eg a face that is partially obscured by items such as a hat. 
Existing works based on the assumption of single and uniform illumination cannot correctly process these data.
In this work, we introduce a novel approach to model 3D facial textures under such unnatural illumination. Instead of assuming single illumination, our framework learns to imitate the unnatural illumination as a composition of multiple separate light conditions combined with learned neural representations, named Light Decoupling.
According to experiments on both single images and video sequences, we demonstrate the effectiveness of our approach in modeling facial textures under challenging illumination affected by occlusions.
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
Authors: Juliusz Ziomek; Masaki Adachi; Michael A Osborne
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Bayesian Optimization (BO) is widely used for optimising black-box functions but requires us to specify the length scale hyperparameter, which defines the smoothness of the functions the optimizer will consider. Most current BO algorithms choose this hyperparameter by maximizing the marginal likelihood of the observed data, albeit risking misspecification if the objective function is less smooth in regions we have not yet explored. The only prior solution addressing this problem with theoretical guarantees was A-GP-UCB, proposed by Berkenkamp et al. (2019). This algorithm progressively decreases the length scale, expanding the class of functions considered by the optimizer. However, A-GP-UCB lacks a stopping mechanism, leading to over-exploration and slow convergence. To overcome this, we introduce Length scale Balancing (LB) - a novel approach, aggregating multiple base surrogate models with varying length scales. LB intermittently adds smaller length scale candidate values while retaining longer scales, balancing exploration and exploitation. We formally derive a cumulative regret bound of LB and compare it with the regret of an oracle BO algorithm using the optimal length scale. Denoting the factor by which the regret bound of A-GP-UCB was away from oracle as $g(T)$,  we show that LB is only $\log g(T)$ away from oracle regret. We also empirically evaluate our algorithm on synthetic and real-world benchmarks and show it outperforms A-GP-UCB and maximum likelihood estimation.
Generalized Protein Pocket Generation with Prior-Informed Flow Matching
Authors: ZAIXI ZHANG; Marinka Zitnik; Qi Liu
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Designing ligand-binding proteins, such as enzymes and biosensors, is essential in bioengineering and protein biology. One critical step in this process involves designing protein pockets, the protein interface binding with the ligand. Current approaches to pocket generation often suffer from time-intensive physical computations or template-based methods, as well as compromised generation quality due to the overlooking of domain knowledge. To tackle these challenges, we propose PocketFlow, a generative model that incorporates protein-ligand interaction priors based on flow matching. During training, PocketFlow learns to model key types of protein-ligand interactions, such as hydrogen bonds. In the sampling, PocketFlow leverages multi-granularity guidance (overall binding affinity and interaction geometry constraints) to facilitate generating high-affinity and valid pockets. Extensive experiments show that PocketFlow outperforms baselines on multiple benchmarks, e.g., achieving an average improvement of 1.29 in Vina Score and 0.05 in scRMSD. Moreover, modeling interactions make PocketFlow a generalized generative model across multiple ligand modalities, including small molecules, peptides, and RNA.
Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting
Authors: Xingyu Zhu; Beier Zhu; Yi Tan; Shuo Wang; Yanbin Hao; Hanwang Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. While optimizing prompts on downstream labeled data has proven effective in improving performance, these methods entail labor costs for annotations and are limited by their quality. Additionally, since CLIP is pre-trained on highly imbalanced Web-scale data, it suffers from inherent label bias that leads to suboptimal performance. 
 To tackle the above challenges, we propose a label-**F**ree p**ro**mpt distribution **l**earning and b**i**as **c**orrection framework, dubbed as **Frolic**, which boosts zero-shot performance without the need for labeled data. Specifically, our Frolic learns distributions over prompt prototypes to capture diverse visual representations and adaptively fuses these with the original CLIP through confidence matching.
This fused model is further enhanced by correcting label bias via a label-free logit adjustment. Notably, our method is not only training-free but also circumvents the necessity for hyper-parameter tuning. Extensive experimental results across 16 datasets demonstrate the efficacy of our approach, particularly outperforming the state-of-the-art by an average of $2.6\%$ on 10 datasets with CLIP ViT-B/16 and achieving an average margin of $1.5\%$ on ImageNet and its five distribution shifts with CLIP ViT-B/16. Codes are available in [https://github.com/zhuhsingyuu/Frolic](https://github.com/zhuhsingyuu/Frolic).
Unchosen Experts Can Contribute Too: Unleashing MoE Models’ Power by Self-Contrast
Authors: Chufan Shi; Cheng Yang; Xinyu Zhu; Jiahao Wang; Taiqiang Wu; Siheng Li; Deng Cai; Yujiu Yang; Yu Meng
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. However, the unchosen experts in MoE models do not contribute to the output, potentially leading to underutilization of the model's capacity.
In this work, we first conduct exploratory studies to demonstrate that increasing the number of activated experts does not necessarily improve and can even degrade the output quality. Then, we show that output distributions from an MoE model using different routing strategies substantially differ, indicating that different experts do not always act synergistically. 
Motivated by these findings, we propose **S**elf-**C**ontrast **M**ixture-**o**f-**E**xperts (SCMoE), a training-free strategy that utilizes unchosen experts in a self-contrast manner during inference. 
In SCMoE, the next-token probabilities are determined by contrasting the outputs from strong and weak activation using the same MoE model.
Our method is conceptually simple and computationally lightweight, as it incurs minimal latency compared to greedy decoding. 
Experiments on several benchmarks (GSM8K, StrategyQA, MBPP and HumanEval) demonstrate that SCMoE can consistently enhance Mixtral 8x7B’s reasoning capability across various domains. For example, it improves the accuracy on GSM8K from 61.79 to 66.94. 
Moreover, combining SCMoE with self-consistency yields additional gains, increasing major@20 accuracy from 75.59 to 78.31.
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Authors: Nikhil Behari; Edwin Zhang; YUNFAN ZHAO; Aparna Taneja; Dheeraj Mysore Nagaraj; Milind Tambe
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB models lack flexibility to adapt to evolving public health policy priorities. Concurrently, Large Language Models (LLMs) have emerged as adept automated planners across domains of robotic control and navigation. In this paper, we propose a Decision Language Model (DLM) for RMABs, enabling dynamic fine-tuning of RMAB policies in public health settings using human-language commands. We propose using LLMs as automated planners to (1) interpret human policy preference prompts, (2) propose reward functions as code for a multi-agent RMAB environment, and (3) iterate on the generated reward functions using feedback from grounded RMAB simulations. We illustrate the application of DLM in collaboration with ARMMAN, an India-based non-profit promoting preventative care for pregnant mothers, that currently relies on RMAB policies to optimally allocate health worker calls to low-resource populations.  We conduct a technology demonstration in simulation using the Gemini Pro model, showing DLM can dynamically shape policy outcomes using only human prompts as input.
Knowledge Circuits in Pretrained Transformers
Authors: Yunzhi Yao; Ningyu Zhang; Zekun Xi; Mengru Wang; Ziwen Xu; Shumin Deng; Huajun Chen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, has allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuit holds potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing.
Phased Consistency Models
Authors: Fu-Yun Wang; Zhaoyang Huang; Alexander William Bergman; Dazhong Shen; Peng Gao; Michael Lingelbach; Keqiang Sun; Weikang Bian; Guanglu Song; Yu Liu; Xiaogang Wang; Hongsheng Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models~(LCMs). We investigate the reasons behind these limitations and propose Phased Consistency Models (PCMs), which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings. While PCMs are specifically designed for multi-step refinement, they achieve comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show the methodology of PCMs is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. Our code is available at https://github.com/G-U-N/Phased-Consistency-Model.
Doubly Mild Generalization for Offline Reinforcement Learning
Authors: Yixiu Mao; Cheems Wang; Yun Qu; Yuhang Jiang; Xiangyang Ji
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Offline Reinforcement Learning (RL) suffers from the extrapolation error and value overestimation. From a generalization perspective, this issue can be attributed to the over-generalization of value functions or policies towards out-of-distribution (OOD) actions. Significant efforts have been devoted to mitigating such generalization, and recent in-sample learning approaches have further succeeded in entirely eschewing it. Nevertheless, we show that mild generalization beyond the dataset can be trusted and leveraged to improve performance under certain conditions. To appropriately exploit generalization in offline RL, we propose Doubly Mild Generalization (DMG), comprising (i) mild action generalization and (ii) mild generalization propagation. The former refers to selecting actions in a close neighborhood of the dataset to maximize the Q values. Even so, the potential erroneous generalization can still be propagated, accumulated, and exacerbated by bootstrapping. In light of this, the latter concept is introduced to mitigate the generalization propagation without impeding the propagation of RL learning signals. Theoretically, DMG guarantees better performance than the in-sample optimal policy in the oracle generalization scenario. Even under worst-case generalization, DMG can still control value overestimation at a certain level and lower bound the performance. Empirically, DMG achieves state-of-the-art performance across Gym-MuJoCo locomotion tasks and challenging AntMaze tasks. Moreover, benefiting from its flexibility in both generalization aspects, DMG enjoys a seamless transition from offline to online learning and attains strong online fine-tuning performance.