GenAI4ELab/papercli-papers huggingface dataset:
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Authors: Xiaoxue Han; Zhuo Feng; Yue Ning
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph Neural Networks (GNNs) experience "catastrophic forgetting" in continual learning setups, where they tend to lose previously acquired knowledge and perform poorly on old tasks. Rehearsal-based methods, which consolidate old knowledge with a replay memory buffer, are a de facto solution due to their straightforward workflow. However, these methods often fail to adequately capture topological information, leading to incorrect input-label mappings in replay samples. To address this, we propose TACO, a topology-aware graph coarsening and continual learning framework that stores information from previous tasks as a reduced graph. Throughout each learning period, this reduced graph expands by integrating with a new graph and aligning shared nodes, followed by a "zoom-out" reduction process to maintain a stable size. We have developed a graph coarsening algorithm based on node representation proximities to efficiently reduce a graph while preserving essential topological information. We empirically demonstrate that the learning process on the reduced graph can closely approximate that on the original graph. We compare TACO with a wide range of state-of-the-art baselines, proving its superiority and the necessity of preserving high-quality topological information for effective replaying.
Score-based 3D molecule generation with neural fields
Authors: Matthieu Kirchmeyer; Pedro O. Pinheiro; Saeed Saremi
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We introduce a new representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on walk-jump sampling for unconditional 3D molecule generation in the continuous space using neural fields. Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field, samples noisy codes from a Gaussian-smoothed distribution with Langevin MCMC (walk), denoises these samples in a single step (jump), and finally decodes them into molecular fields. FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most approaches. Our method achieves competitive results on drug-like molecules and easily scales to macro-cyclic peptides, with at least one order of magnitude faster sampling. The code is available at https://github.com/prescient-design/funcmol.
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Authors: Nina Gubina; Andrei Dmitrenko; Gleb Vitalevich Solovev; Lyubov Yamshchikova; Oleg Petrov; Ivan Lebedev; Nikita Serov; Grigorii Kirgizov; Nikolay Nikitin; Vladimir Vinogradov
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. In this work, we present Generative Method for Co-crystal Design (GEMCODE), a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast *de novo* co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEMCODE and discuss its potential in accelerating drug development.
Trajectory Flow Matching with Applications to Clinical Time Series Modelling
Authors: Xi Zhang; Yuan Pu; Yuki Kawamura; Andrew Loza; Yoshua Bengio; Dennis Shung; Alexander Tong
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. 
To address this, we propose **Trajectory Flow Matching** (TFM), which trains a Neural SDE in a *simulation-free* manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on four clinical time series datasets both in terms of absolute performance and uncertainty prediction, a crucial parameter in this setting.
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Authors: Brett Mullins; Miguel Fuentes; Yingtai Xiao; Daniel Kifer; Cameron N Musco; Daniel Sheldon
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other queries that have been measured privately.  Reconstruction is an important subproblem for such mechanisms to economize the privacy budget, minimize error on reconstructed answers, and allow for scalability to high-dimensional datasets. In this paper, we introduce a principled and efficient postprocessing method ReM (Residuals-to-Marginals) for reconstructing answers to marginal queries. Our method builds on recent work on efficient mechanisms for marginal query release, based on making measurements using a residual query basis that admits efficient pseudoinversion, which is an important primitive used in reconstruction. An extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity) reconstructs marginals under Gaussian noise satisfying consistency and non-negativity, which often reduces error on reconstructed answers.  We demonstrate the utility of ReM and GReM-LNN by applying them to improve existing private query answering mechanisms.
Achieving Constant Regret in Linear Markov Decision Processes
Authors: Weitong Zhang; Zhiyuan Fan; Jiafan He; Quanquan Gu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We study the constant regret guarantees in reinforcement learning (RL). Our objective is to design an algorithm that incurs only finite regret over infinite episodes with high probability. We introduce an algorithm, Cert-LSVI-UCB, for misspecified linear Markov decision processes (MDPs) where both the transition kernel and the reward function can be approximated by some linear function up to misspecification level $\zeta$. At the core of Cert-LSVI-UCB is an innovative certified estimator, which facilitates a fine-grained concentration analysis for multi-phase value-targeted regression, enabling us to establish an instance-dependent regret bound that is constant w.r.t. the number of episodes. Specifically, we demonstrate that for a linear MDP characterized by a minimal suboptimality gap $\Delta$, Cert-LSVI-UCB has a cumulative regret of $\tilde{\mathcal{O}}(d^3H^5/\Delta)$ with high probability, provided that the misspecification level $\zeta$ is below $\tilde{\mathcal{O}}(\Delta / (\sqrt{d}H^2))$. Here $d$ is the dimension of the feature space and $H$ is the horizon. Remarkably, this regret bound is independent of the number of episodes $K$. To the best of our knowledge, Cert-LSVI-UCB is the first algorithm to achieve a constant, instance-dependent, high-probability regret bound in RL with linear function approximation without relying on prior distribution assumptions.
Gaussian Process Bandits for Top-k Recommendations
Authors: Mohit Yadav; Cameron N Musco; Daniel Sheldon
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Algorithms that utilize bandit feedback to optimize top-k recommendations are vital for online marketplaces, search engines, and content platforms.  However, the combinatorial nature of this problem poses a significant challenge,  as the possible number of ordered top-k recommendations from $n$ items grows exponentially with $k$. As a result, previous work often relies on restrictive assumptions about the reward or bandit feedback models, such as assuming that the feedback discloses rewards for each recommended item rather than a single scalar feedback for the entire set of top-k recommendations. We introduce a novel contextual bandit algorithm for top-k recommendations, leveraging a Gaussian process with a Kendall kernel to model the reward function.
Our algorithm requires only scalar feedback from 
the top-k recommendations and does not impose restrictive assumptions on the reward structure. 
Theoretical analysis confirms that the proposed algorithm achieves sub-linear regret in relation to the number of rounds and arms. Additionally, empirical results using a bandit simulator demonstrate that the proposed algorithm outperforms other baselines across various scenarios.
ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
Authors: Yuezhu Xu; S Sivaranjani
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations. Since calculating the exact Lipschitz constant is NP-hard, efforts have been made to obtain tight upper bounds on the Lipschitz constant. Typically, this involves solving a large matrix verification problem, the computational cost of which grows significantly for both deeper and wider networks. In this paper, we provide a compositional approach to estimate Lipschitz constants for deep feed-forward neural networks. We first obtain an exact decomposition of the large matrix verification problem into smaller sub-problems. Then, leveraging the underlying cascade structure of the network, we develop two algorithms. The first algorithm explores the geometric features of the problem and enables us to provide Lipschitz estimates that are comparable to existing methods by solving small semidefinite programs (SDPs) that are only as large as the size of each layer. The second algorithm relaxes these sub-problems and provides a closed-form solution to each sub-problem for extremely fast estimation, altogether eliminating the need to solve SDPs. The two algorithms represent different levels of trade-offs between efficiency and accuracy. Finally, we demonstrate that our approach provides a steep reduction in computation time (as much as several thousand times faster, depending on the algorithm for deeper networks) while yielding Lipschitz bounds that are very close to or even better than those achieved by state-of-the-art approaches in a broad range of experiments. In summary, our approach considerably advances the scalability and efficiency of certifying neural network robustness, making it particularly attractive for online learning tasks.
Mixture of Link Predictors on Graphs
Authors: Li Ma; Haoyu Han; Juanhui Li; Harry Shomer; Hui Liu; Xiaofeng Gao; Jiliang Tang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs). Therefore, recent advancements in GNNs for link prediction (GNN4LP) have primarily focused on integrating one or a few types of pairwise information. 
In this work,  we reveal that different node pairs within the same dataset necessitate varied pairwise information for accurate prediction and models that only apply the same pairwise information uniformly could achieve suboptimal performance.
As a result, we propose a simple mixture of experts model Link-MoE for link prediction.  Link-MoE utilizes various GNNs as experts and  strategically selects the appropriate expert for each node pair based on various types of pairwise information. Experimental results across diverse real-world datasets demonstrate substantial performance improvement from Link-MoE. Notably,  Link-Mo achieves a relative improvement of 18.71% on the MRR metric for the Pubmed dataset and 9.59% on the Hits@100 metric for the ogbl-ppa dataset, compared to the best baselines. The code is available at https://github.com/ml-ml/Link-MoE/.
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
Authors: Yu Yang; Siddhartha Mishra; Jeffrey N Chiang; Baharan Mirzasoleiman
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Despite the effectiveness of data selection for pretraining and instruction fine-tuning
large language models (LLMs), improving data efficiency in supervised fine-tuning
(SFT) for specialized domains poses significant challenges due to the complexity
of fine-tuning data. To bridge this gap, we introduce an effective and scalable
data selection method for SFT, SmallToLarge (S2L), which trains a small
model, clusters loss trajectories of the examples, and samples from these clusters to
guide data selection for larger models. We prove that during fine-tuning, samples
within the same loss trajectory cluster exhibit similar gradients. Then, we show
that S2L subsets have a bounded gradient error w.r.t. the full data, hence guarantee
convergence to the neighborhood of the optimal solution. We demonstrate through
extensive experiments that S2L significantly improves data efficiency in SFT for
mathematical problem-solving, reducing the training data requirement to just $11$%
of the original MathInstruct dataset to match full dataset performance while
outperforming state-of-the-art data selection algorithms by an average of $4.7$%
across $6$ in- and out-domain evaluation datasets. Remarkably, selecting only 50K
data for SFT, S2L achieves a $32.7$% accuracy on the challenging MATH
benchmark, improving Phi-2 by $16.6$%. In clinical text summarization on the
MIMIC-III dataset, S2L again outperforms training on the full dataset using
only $50$% of the data. Notably, S2L can perform scalable data selection using a
reference model $100\times$ smaller than the target model, proportionally reducing the
computational cost.
DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations
Authors: Zuowen Wang; Longbiao Cheng; Pehuen Moure; Niklas Hahn; Shih-Chii Liu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Implicit neural networks including deep equilibrium models have achieved superior task performance with better parameter efficiency in various applications. However, it is often at the expense of higher computation costs during inference. In this work, we identify a phenomenon named $\textbf{heterogeneous convergence}$ that exists in deep equilibrium models and other iterative methods. We observe much faster convergence of state activations in certain dimensions therefore indicating the dimensionality of the underlying dynamics of the forward pass is much lower than the defined dimension of the states. We thereby propose to exploit heterogeneous convergence by storing past linear operation results (e.g., fully connected and convolutional layers) and only propagating the state activation when its change exceeds a threshold. Thus, for the already converged dimensions, the computations can be skipped. We verified our findings and reached 84\% FLOPs reduction on the implicit neural representation task, 73\% on the Sintel and 76\% on the KITTI datasets for the optical flow estimation task while keeping comparable task accuracy with the models that perform the full update.
Retrieval & Fine-Tuning for In-Context Tabular Models
Authors: Valentin Thomas; Junwei Ma; Rasa Hosseinzadeh; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Tabular data is a pervasive modality spanning a wide range of domains, and this inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less complex tabular datasets, but have struggled to scale to larger and more complex ones. To address this limitation, we propose a combination of retrieval and fine-tuning: we can adapt the transformer to a local subset of the data by collecting nearest neighbours, and then perform task-specific fine-tuning with this retrieved set of neighbours in context. Using TabPFN as the base model -- currently the best tabular in-context learner -- and applying our retrieval and fine-tuning scheme on top results in what we call a locally-calibrated PFN, or LoCalPFN. We conduct extensive evaluation on 95 datasets curated by TabZilla from OpenML, upon which we establish a new state-of-the-art with LoCalPFN -- even with respect to tuned tree-based models. Notably, we show a significant boost in performance compared to the base in-context model, demonstrating the efficacy of our approach and advancing the frontier of deep learning in tabular data.
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Authors: Qian Xie; Raul Astudillo; Peter I. Frazier; Ziv Scully; Alexander Terenin
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be reinterpreted as an acquisition function. We study the use of this acquisition function for cost-aware Bayesian optimization, and demonstrate empirically that it performs well, particularly in medium-high dimensions. We further show that this performance carries over to classical Bayesian optimization without explicit evaluation costs. Our work constitutes a first step towards integrating techniques from Gittins index theory into Bayesian optimization.
Online Budgeted Matching with General Bids
Authors: Jianyi Yang; Pengfei Li; Adam Wierman; Shaolei Ren
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online algorithms typically assume a small bid setting, where the maximum bid-to-budget ratio ($\kappa$) is infinitesimally small. While recent algorithms have tried to address scenarios with non-small or general bids, they often rely on the Fractional Last Matching (FLM) assumption, which allows for accepting partial bids when the remaining budget is insufficient. This assumption, however, does not hold for many applications with indivisible bids. In this paper, we remove the FLM assumption and tackle the open problem of OBM with general bids. We first establish an upper bound of $1-\kappa$ on the competitive ratio for any deterministic online algorithm. We then propose a novel meta algorithm, called MetaAd, which reduces to different algorithms with first known provable competitive ratios parameterized by the maximum bid-to-budget ratio $\kappa\in [0,1]$. As a by-product, we extend MetaAd to the FLM setting and get provable competitive algorithms. Finally, we apply our competitive analysis to the design learning- augmented algorithms.
Risk-Averse Fine-tuning of Large Language Models
Authors: Sapana Chaudhary; Ujwal Dinesha; Dileep Kalathil; Srinivas Shakkottai
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We consider the challenge of mitigating the generation of negative or toxic content by the Large Language Models (LLMs) in response to certain prompts.  We propose integrating risk-averse principles into LLM fine-tuning to minimize the occurrence of harmful outputs, particularly rare but significant events.  By optimizing the risk measure of Conditional Value at Risk (CVaR), our methodology trains LLMs to exhibit superior performance in avoiding toxic outputs while maintaining effectiveness in generative tasks. Empirical evaluations on sentiment modification and toxicity mitigation tasks demonstrate the efficacy of risk-averse reinforcement learning with human feedback (RLHF) in promoting a safer and more constructive online discourse environment.
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
Authors: Yue Yu; Wei Ping; Zihan Liu; Boxin Wang; Jiaxuan You; Chao Zhang; Mohammad Shoeybi; Bryan Catanzaro
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG).  In this work, we propose a novel method called RankRAG, which instruction-tunes a single LLM for both context ranking and answer generation in RAG.  In particular, the instruction-tuned LLMs work surprisingly well by adding a small fraction of ranking data into the training blend, and outperform existing expert ranking models, including the same LLM exclusively fine-tuned on a large amount of ranking data.  For generation, we compare our model with many strong baselines, including ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG benchmarks.  Specifically,  our Llama3-RankRAG-8B and Llama3-RankRAG-70B significantly outperform Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B, respectively, on nine general knowledge-intensive benchmarks for RAG. In addition, it also performs comparably to GPT-4 on five RAG benchmarks in the biomedical domain without instruction fine-tuning on biomedical data, demonstrating its superb capability for generalization to new domains.
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Authors: Yixin Liu; Shiyuan Li; Yu Zheng; Qingfeng Chen; Chengqi Zhang; Shirui Pan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limited generalizability when being applied to new datasets and domains. To address these limitations, this paper proposes ARC, a generalist GAD approach that enables a ``one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. Equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset using few-shot normal samples at the inference stage, without the need for retraining or fine-tuning on the target dataset. ARC comprises three components that are well-crafted for capturing universal graph anomaly patterns: 1) smoothness-based feature **A**lignment module that unifies the features of different datasets into a common and anomaly-sensitive space; 2) ego-neighbor **R**esidual graph encoder that learns abnormality-related node embeddings; and 3) cross-attentive in-**C**ontext anomaly scoring module that predicts node abnormality by leveraging few-shot normal samples. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.