GenAI4ELab/papercli-papers huggingface dataset:
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
Authors: Liang-Hsuan Tseng; En-Pei Hu; Cheng-Han Chiang; Yuan Tseng; Hung-yi Lee; Lin-shan Lee; Shao-Hua Sun
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Unsupervised automatic speech recognition (ASR) aims to learn the mapping between the speech signal and its corresponding textual transcription without the supervision of paired speech-text data. A word/phoneme in the speech signal is represented by a segment of speech signal with variable length and unknown boundary, and this segmental structure makes learning the mapping between speech and text challenging, especially without paired data. In this paper, we propose REBORN, Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR. REBORN alternates between (1) training a segmentation model that predicts the boundaries of the segmental structures in speech signals and (2) training the phoneme prediction model, whose input is a segmental structure segmented by the segmentation model, to predict a phoneme transcription. Since supervised data for training the segmentation model is not available, we use reinforcement learning to train the segmentation model to favor segmentations that yield phoneme sequence predictions with a lower perplexity. We conduct extensive experiments and find that under the same setting, REBORN outperforms all prior unsupervised ASR models on LibriSpeech, TIMIT, and five non-English languages in Multilingual LibriSpeech. We comprehensively analyze why the boundaries learned by REBORN improve the unsupervised ASR performance.
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Authors: Chengting Yu; Lei Liu; Gaoang Wang; Erping Li; Aili Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Recent insights have revealed that rate-coding is a primary form of information representation captured by surrogate-gradient-based Backpropagation Through Time (BPTT) in training deep Spiking Neural Networks (SNNs). Motivated by these findings, we propose rate-based backpropagation, a training strategy specifically designed to exploit rate-based representations to reduce the complexity of BPTT. Our method minimizes reliance on detailed temporal derivatives by focusing on averaged dynamics, streamlining the computational graph to reduce memory and computational demands of SNNs training. We substantiate the rationality of the gradient approximation between BPTT and the proposed method through both theoretical analysis and empirical observations. Comprehensive experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS validate that our method achieves comparable performance to BPTT counterparts, and surpasses state-of-the-art efficient training techniques. By leveraging the inherent benefits of rate-coding, this work sets the stage for more scalable and efficient SNNs training within resource-constrained environments.
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
Authors: Julien Zhou; Pierre Gaillard; Thibaud Rahier; Houssam Zenati; Julyan Arbel
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We address the problem of stochastic combinatorial semi-bandits, where a player selects among $P$ actions from the power set of a set containing $d$ base items. Adaptivity to the problem's structure is essential in order to obtain optimal regret upper bounds. As estimating the coefficients of a covariance matrix can be manageable in practice, leveraging them should improve the regret. We design ``optimistic''  covariance-adaptive algorithms relying on online estimations of the covariance structure, called OLS-UCB-C and COS-V (only the variances for the latter). They both yields improved gap-free regret. Although COS-V can be slightly suboptimal, it improves on computational complexity by taking inspiration from Thompson Sampling approaches. It is the first sampling-based algorithm satisfying a $\sqrt{T}$ gap-free regret (up to poly-logs). We also show that in some cases, our approach efficiently leverages the semi-bandit feedback and outperforms bandit feedback approaches, not only in exponential regimes where $P\gg d$ but also when $P\leq d$, which is not covered by existing analyses.
Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels
Authors: Haoyang Luo; Zheng Zhang; Yadan Luo
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In this paper, we tackle the challenge of generating high-quality hash codes for cross-modal retrieval in the presence of incomplete labels, which creates uncertainty in distinguishing between positive and negative pairs. Vision-language models such as CLIP offer a potential solution by providing generic knowledge for missing label recovery, yet their zero-shot performance remains insufficient. To address this, we propose a novel Prompt Contrastive Recovery approach, \textbf{PCRIL}, which progressively identifies promising positive classes from unknown label sets and recursively searches for other relevant labels. Identifying unknowns is nontrivial due to the fixed and long-tailed patterns of positive label sets in training data, which hampers the discovery of new label combinations. Therefore, we consider each subset of positive labels and construct three types of negative prompts through deletion, addition, and replacement for prompt learning. The augmented supervision guides the model to measure the completeness of label sets, thus facilitating the subsequent greedy tree search for label completion. We also address extreme cases of significant unknown labels and lack of negative pairwise supervision by deriving two augmentation strategies: seeking unknown-complementary samples for mixup and random flipping for negative labels. Extensive experiments reveal the vulnerability of current methods and demonstrate the effectiveness of PCRIL, achieving an average 12\% mAP improvement to the current SOTA across all datasets. Our code is available at https://github.com/E-Galois/PCRIL.
BOLD: Boolean Logic Deep Learning
Authors: Van Minh Nguyen; Cristian Ocampo; Aymen Askri; Louis Leconte; Ba-Hien Tran
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Computational intensiveness of deep learning has motivated low-precision arithmetic designs. However, the current quantized/binarized training approaches are limited by: (1) significant performance loss due to arbitrary approximations of the latent weight gradient through its discretization/binarization function, and (2) training computational intensiveness due to the reliance on full-precision latent weights. 
This paper proposes a novel mathematical principle by introducing the notion of Boolean variation such that neurons made of Boolean weights and/or activations can be trained ---for the first time--- natively in Boolean domain instead of latent-weight gradient descent and real arithmetic. We explore its convergence, conduct extensively experimental benchmarking, and provide consistent complexity evaluation by considering chip architecture, memory hierarchy, dataflow, and arithmetic precision. Our approach achieves baseline full-precision accuracy in ImageNet classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. Moreover, it significantly reduces energy consumption during both training and inference.
Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection
Authors: Gyusam Chang; Jiwon Lee; Donghyun Kim; Jinkyu Kim; Dongwook Lee; Daehyun Ji; Sujin Jang; Sangpil Kim
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks.
However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets (i.e., direct transfer) due to the inevitable geometric misalignment between the source and target domains.
In practice, we also encounter constraints on resources for training models and collecting annotations for the successful deployment of 3D object detectors.
In this paper, we propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks.
We first propose Multi-view Overlap Depth Constraint that leverages the strong association between multi-view, significantly alleviating geometric gaps due to perspective view changes.
Then, we present a Label-Efficient Domain Adaptation approach to handle unfamiliar targets with significantly fewer amounts of labels (i.e., 1$\%$ and 5$\%)$, while preserving well-defined source knowledge for training efficiency.
Overall, UDGA framework enables stable detection performance in both source and target domains, effectively bridging inevitable domain gaps, while demanding fewer annotations.
We demonstrate the robustness of UDGA with large-scale benchmarks: nuScenes, Lyft, and Waymo, where our framework outperforms the current state-of-the-art methods.
MindMerger: Efficiently Boosting LLM Reasoning in non-English Languages
Authors: Zixian Huang; Wenhao Zhu; Gong Cheng; Lei Li; Fei Yuan
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Reasoning capabilities are crucial for Large Language Models~(LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to circumvent the challenge of LLM understanding non-English. Unfortunately, these methods often underutilize the built-in skilled reasoning and useful language understanding capabilities of LLMs. In order to better utilize the minds of reasoning and language understanding in LLMs, we propose a new method, namely MergeMinds, which merges LLMs with the external language understanding capabilities from multilingual models to boost the multilingual reasoning performance. Furthermore, a two-step training scheme is introduced to first train to embeded the external capabilities into LLMs and then train the collaborative utilization of the external capabilities and the built-in capabilities in LLMs. Experiments on three multilingual reasoning datasets and a language understanding dataset demonstrate that MergeMinds consistently outperforms all baselines, especially in low-resource languages.  Without updating the parameters of LLMs, the average accuracy improved by 6.7 and 8.0 across all languages and low-resource languages on the MGSM dataset, respectively.
Adaptive Depth Networks with Skippable Sub-Paths
Authors: Woochul Kang; Hyungseop Lee
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Predictable adaptation of network depths can be an effective way to control inference latency and meet the resource condition of various devices. However, previous adaptive depth networks do not provide general principles and a formal explanation on why and which layers can be skipped, and, hence, their approaches are hard to be generalized and require long and complex training steps. In this paper, we present a practical approach to adaptive depth networks that is applicable to various networks with minimal training effort. In our approach, every hierarchical residual stage is divided into two sub-paths, and they are trained to acquire different properties through a simple self-distillation strategy. While the first sub-path is essential for hierarchical feature learning, the second one is trained to refine the learned features and minimize performance degradation if it is skipped. Unlike prior adaptive networks, our approach does not train every target sub-network in an iterative manner. At test time, however, we can connect these sub-paths in a combinatorial manner to select sub-networks of various accuracy-efficiency trade-offs from a single network. We provide a formal rationale for why the proposed training method can reduce overall prediction errors while minimizing the impact of skipping sub-paths. We demonstrate the generality and effectiveness of our approach with convolutional neural networks and transformers.
Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis
Authors: Jianning Deng; Kartic Subr; Hakan Bilen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We propose a novel unsupervised method to learn pose and part-segmentation of articulated objects with rigid parts.
    Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distills the part segmentation and articulation from the second observation while rendering the latter observation.
    Additionally, to tackle the complexities in the joint optimization of part segmentation and articulation, we propose a voxel grid based initialization strategy and a decoupled optimization procedure.
    Compared to the prior unsupervised work, our model obtains significantly better performance, generalizes to objects with multiple parts while it can be efficiently from few views for the latter observation.
FUGAL: Feature-fortified Unrestricted Graph Alignment
Authors: Aditya Bommakanti; Harshith Reddy Vonteri; Konstantinos Skitsas; Sayan Ranu; Davide Mottin; Panagiotis Karras
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The necessity to align two graphs, minimizing a structural distance metric, is prevalent in biology, chemistry, recommender systems, and social network analysis. Due to the problem’s NP-hardness, prevailing graph alignment methods follow a modular and mediated approach, solving the problem by restricting to the domain of intermediary graph representations or products like embeddings, spectra, and graph signals. Restricting the problem to this intermediate space may distort the original problem and are hence predisposed to miss high-quality solutions. In this paper, we propose an unrestricted method, FUGAL, which finds a permutation matrix that maps one graph to another by directly operating on their adjacency matrices with judicious constraint relaxation. Extensive experimentation demonstrates that FUGAL consistently surpasses state-of-the-art graph alignment methods in accuracy across all benchmark datasets without encumbering efficiency.
Hyperbolic Embeddings of Supervised Models
Authors: Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K Warmuth
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Models of hyperbolic geometry have been successfully used in ML for two main tasks: embedding *models* in unsupervised learning (*e.g.* hierarchies) and embedding *data*. 
To our knowledge, there are no approaches that provide embeddings for supervised models; even when hyperbolic geometry provides convenient properties for expressing popular hypothesis classes, such as decision trees (and ensembles).
In this paper, we propose a full-fledged solution to the problem in three independent contributions. The first linking the theory of losses for class probability estimation to hyperbolic embeddings in Poincar\'e disk model. The second resolving an issue for a clean, unambiguous embedding of (ensembles of) decision trees in this model. The third showing how to smoothly tweak the Poincar\'e hyperbolic distance to improve its encoding and visualization properties near the border of the disk, a crucial region for our application, while keeping hyperbolicity.
This last step has substantial independent interest as it is grounded in a generalization of Leibniz-Newton's fundamental Theorem of calculus.
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Authors: Shaoteng Liu; Haoqi Yuan; Minda Hu; Yanwei Li; Yukang Chen; Shu Liu; Zongqing Lu; Jiaya Jia
Venue: NeurIPS 2024 (NeurIPS 2024 oral)

Abstract: Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embodied tasks, high-level planning is amenable to direct coding, while low-level actions often necessitate task-specific refinement, such as Reinforcement Learning (RL). To seamlessly integrate both modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent. The slow agent analyzes actions suitable for coding, while the fast agent executes coding tasks. This decomposition effectively focuses each agent on specific tasks, proving highly efficient within our pipeline. Our approach outperforms traditional RL methods and existing GPT agents, demonstrating superior efficiency. In the Minecraft game, it rapidly obtains diamonds within a single day on an RTX3090. Additionally, it achieves SOTA performance across all designated MineDojo tasks.
Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games
Authors: Ahmed Said Dönmez; Yüksel Arslantaş; Muhammed O. Sayin
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Multi-team games, prevalent in robotics and resource management, involve team members striving for a joint best response against other teams. Team-Nash equilibrium (TNE) predicts the outcomes of such coordinated interactions. However, can teams of self-interested agents reach TNE? We introduce Team-Fictitious Play (Team-FP), a new variant of fictitious play where agents respond to the last actions of team members and the beliefs formed about other teams with some inertia in action updates. This design is essential in team coordination beyond the classical fictitious play dynamics. We focus on zero-sum potential team games (ZSPTGs) where teams can interact pairwise while the team members do not necessarily have identical payoffs. We show that Team-FP reaches near TNE in ZSPTGs with a quantifiable error bound. We extend Team-FP dynamics to multi-team Markov games for model-based and model-free cases. The convergence analysis tackles the challenge of non-stationarity induced by evolving opponent strategies based on the optimal coupling lemma and stochastic differential inclusion approximation methods. Our work strengthens the foundation for using TNE to predict the behavior of decentralized teams and offers a practical rule for team learning in multi-team environments. We provide extensive simulations of Team-FP dynamics and compare its performance with other widely studied dynamics such as smooth fictitious play and multiplicative weights update. We further explore how different parameters impact the speed of convergence.
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction
Authors: Zhihao Yu; Xu Chu; Yujie Jin; Yasha Wang; Junfeng Zhao
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods.
Learning to compute Gröbner bases
Authors: Hiroshi Kera; Yuki Ishihara; Yuta Kambe; Tristan Vaccon; Kazuhiro Yokoyama
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Solving a polynomial system, or computing an associated Gröbner basis, has been a fundamental task in computational algebra. However, it is also known for its notorious doubly exponential time complexity in the number of variables in the worst case. This paper is the first to address the learning of Gröbner basis computation with Transformers. The training requires many pairs of a polynomial system and the associated Gröbner basis, raising two novel algebraic problems: random generation of Gröbner bases and transforming them into non-Gröbner ones, termed as backward Gröbner problem. We resolve these problems with 0-dimensional radical ideals, the ideals appearing in various applications. Further, we propose a hybrid input embedding to handle coefficient tokens with continuity bias and avoid the growth of the vocabulary set. The experiments show that our dataset generation method is a few orders of magnitude faster than a naive approach, overcoming a crucial challenge in learning to compute Gröbner bases, and Gröbner computation is learnable in a particular class.
Improved Algorithms for Contextual Dynamic Pricing
Authors: Matilde Tullii; Solenne Gaucher; Nadav Merlis; Vianney Perchet
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In contextual dynamic pricing,  a seller sequentially prices goods based on contextual information. Buyers will purchase products only if the prices are below their valuations.
The goal of the seller is to design a pricing strategy that collects as much revenue as possible. We focus on two different valuation models. The first assumes that valuations linearly depend on the context and are further distorted by noise. Under minor regularity assumptions, our algorithm achieves an optimal regret bound of $\tilde{\mathcal{O}}(T^{2/3})$, improving the existing results. The second model removes the linearity assumption, requiring only that the expected buyer valuation is $\beta$-H\"older in the context. For this model, our algorithm obtains a regret $\tilde{\mathcal{O}}(T^{d+2\beta/d+3\beta})$, where $d$ is the dimension of the context space.
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Authors: Hai-Vy Nguyen; Fabrice Gamboa; Reda Chhaibi; Sixin Zhang; Serge Gratton; Thierry Giaccone
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We measure the out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called "Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the "depth'' of a point with respect to a distribution in feature space, without any distributional assumption. Our method also has no trainable parameter. The method is applied directly in the feature space at test time and does not intervene in training process. As such, it does not impact the performance of the original model. The proposed method gives excellent qualitative results on toy datasets and can give competitive or better uncertainty estimation on standard deep learning datasets compared to strong baseline methods.