GenAI4ELab/papercli-papers huggingface dataset:
Linear combinations of latents in generative models: subspaces and beyond
Authors: Erik Bodin; Alexandru I. Stere; Dragos D Margineantu; Carl Henrik Ek; Henry Moss
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalising Flows have shown effectiveness across various modalities, and rely on latent variables for generation. For experimental design or creative applications that require more control over the generation process, it has become common to manipulate the latent variable directly. However, existing approaches for performing such manipulations (e.g. interpolation or forming low-dimensional representations) only work well in special cases or are network or data-modality specific. 
We propose Latent Optimal Linear combinations (LOL) as a general-purpose method to form linear combinations of latent variables that adhere to the assumptions of the generative model. As LOL is easy to implement and naturally addresses the broader task of forming any linear combinations, e.g. the construction of subspaces of the latent space, LOL dramatically simplifies the creation of expressive low-dimensional representations of high-dimensional objects.
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
Authors: Yuze Ge; Rujun Jiang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The spectral risk has wide applications in machine learning, especially in real-world decision-making, where people are concerned with more than just average model performance. By assigning different weights to the losses of different sample points, rather than the same weights as in the empirical risk, it allows the model's performance to lie between the average performance and the worst-case performance. In this paper, we propose SOREL, the first stochastic gradient-based algorithm with convergence guarantees for spectral risks minimization. Previous approaches often rely on smoothing the spectral risk by adding a strongly concave function, thereby lacking convergence guarantees for the original spectral risk.  We theoretically prove that our algorithm achieves a near-optimal rate of $\widetilde{O}(1/\sqrt{\epsilon})$ to obtain an $\epsilon$-optimal solution in terms $\epsilon$. Experiments on real datasets show that our algorithm outperforms existing ones in most cases, both in terms of runtime and sample complexity.
Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?
Authors: Xueru Wen; Jie Lou; Yaojie Lu; Hongyu Lin; XingYu; Xinyu Lu; Ben He; Xianpei Han; Debing Zhang; Le Sun
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Reward Models (RMs) are crucial for aligning language models with human preferences. 
Currently, the evaluation of RMs depends on measuring accuracy against a validation set of manually annotated preference data.
Although this method is straightforward and widely adopted, the relationship between RM accuracy and downstream policy performance remains under-explored.
In this work, we conduct experiments in a synthetic setting to investigate how differences in RM measured by accuracy translate into gaps in optimized policy performance.
Our findings reveal that while there is a weak positive correlation between accuracy and downstream performance, policies optimized towards RMs with similar accuracy can exhibit quite different performance.
Moreover, we discover that the way of measuring accuracy significantly impacts its ability to predict the final policy performance. 
Through the lens of the Regressional Goodhart effect, we recognize that accuracy, when used for measuring RM quality, can fail to fully capture the potential RM overoptimization.
This underscores the inadequacy of relying solely on accuracy to reflect their impact on policy optimization.
Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility
Authors: Martin Kuo; Jingyang Zhang; Jianyi Zhang; Minxue Tang; Louis DiValentin; Aolin Ding; Jingwei Sun; William Chen; Amin Hass; Tianlong Chen; Yiran Chen; Hai Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: With the rise of large language models (LLMs), increasing research has recognized
their risk of leaking personally identifiable information (PII) under malicious
attacks. Although efforts have been made to protect PII in LLMs, existing methods
struggle to balance privacy protection with maintaining model utility. In this paper,
inspired by studies of amnesia in cognitive science, we propose a novel approach,
Proactive Privacy Amnesia (PPA), to safeguard PII in LLMs while preserving their
utility. This mechanism works by actively identifying and forgetting key memories
most closely associated with PII in sequences, followed by a memory implanting
using suitable substitute memories to maintain the LLM’s functionality. We conduct
evaluations across multiple models to protect common PII, such as phone numbers
and physical addresses, against prevalent PII-targeted attacks, demonstrating the
superiority of our method compared with other existing defensive techniques. The
results show that our PPA method completely eliminates the risk of phone number
exposure by 100% and significantly reduces the risk of physical address exposure
by 9.8% – 87.6%, all while maintaining comparable model utility performance.
Variational Best-of-N Alignment
Authors: Afra Amini; Tim Vieira; Elliott Ash; Ryan Cotterell
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Best-of-N (BoN) is a popular and effective algorithm for aligning language models to human preferences. The algorithm works as follows: at inference time, N samples are drawn from the language model, and the sample with the highest reward, as judged by a reward model, is returned as the output. Despite its effectiveness, BoN is computationally expensive; it reduces sampling throughput by a factor of N. 
To make BoN more efficient at inference time, one strategy is to fine-tune the language model to mimic what BoN does during inference. 
To achieve this, we derive the distribution induced by the BoN algorithm. We then propose to fine-tune the language model to minimize backward KL divergence to the BoN distribution. Our approach is analogous to mean-field variational inference and, thus, we term it variational BoN (vBoN). To the extent this fine-tuning is successful and we end up with a good approximation, we have reduced the inference cost by a factor of N. Our experiments on controlled generation and summarization tasks show that BoN is the most effective alignment method, and our variational approximation to BoN achieves the closest performance to BoN and surpasses models fine-tuned using the standard KL-constrained RL objective. In the controlled generation task, vBoN appears more frequently on the Pareto frontier of reward and KL divergence compared to other alignment methods. In the summarization task, vBoN achieves high reward values across various sampling temperatures.
HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts
Authors: Mengqi Liao; Wei Chen; Junfeng Shen; Shengnan Guo; Huaiyu Wan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent studies have combined Mixture of Experts (MoE) and Parameter-Efficient Fine-tuning (PEFT) to fine-tune large language models (LLMs), holding excellent performance in multi-task scenarios while remaining resource-efficient.  However, existing MoE approaches still exhibit the following limitations: (1) Current methods fail to consider that different LLM layers capture features at varying levels of granularity, leading to suboptimal performance. (2) Task-level routing methods lack generalizability to unseen tasks. (3) The uncertainty introduced by load imbalance loss undermines the effective specialization of the experts. To address these challenges, we propose HMoRA, a Hierarchical fine-tuning method that combines MoE and LoRA, employing hybrid routing that integrates token-level and task-level routing in a hierarchical manner. This hierarchical hybrid routing allows the model to more efficiently capture both fine-grained token information and broader task contexts. To improve the certainty of expert selection, a novel routing auxiliary loss is introduced. This auxiliary function also enhances the task router's ability to differentiate tasks and its generalization to unseen tasks. Additionally, several optional lightweight designs have been proposed to significantly reduce both the number of trainable parameters and computational costs. Experimental results demonstrate that HMoRA outperforms full fine-tuning across multiple NLP benchmarks, while fine-tuning only 3.9\% of the parameters. The code is  available on: https://github.com/LiaoMengqi/HMoRA.
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Authors: Anh Tong; Thanh Nguyen-Tang; Dongeun Lee; Duc Nguyen; Toan Tran; David Leo Wright Hall; Cheongwoong Kang; Jaesik Choi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent advancements in large language models (LLMs) based on transformer architectures have sparked significant interest in understanding their inner workings. In this paper, we introduce a novel approach to modeling transformer architectures using highly flexible non-autonomous neural ordinary differential equations (ODEs). Our proposed model parameterizes all weights of attention and feed-forward blocks through neural networks, expressing these weights as functions of a continuous layer index. Through spectral analysis of the model's dynamics, we uncover an increase in eigenvalue magnitude that challenges the weight-sharing assumption prevalent in existing theoretical studies. We also leverage the Lyapunov exponent to examine token-level sensitivity, enhancing model interpretability. Our neural ODE transformer demonstrates performance comparable to or better than vanilla transformers across various configurations and datasets, while offering flexible fine-tuning capabilities that can adapt to different architectural constraints.
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
Authors: Xiaoming Shi; Shiyu Wang; Yuqi Nie; Dianqi Li; Zhou Ye; Qingsong Wen; Ming Jin
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility. Code is available at https://github.com/Time-MoE/Time-MoE
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
Authors: Hossein Mirzaei; Mackenzie W Mathis
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Despite significant advancements in out-of-distribution (OOD) detection, existing methods still struggle to maintain robustness against adversarial attacks, compromising their reliability in critical real-world applications. Previous studies have attempted to address this challenge by exposing detectors to auxiliary OOD datasets alongside adversarial training. However, the increased data complexity inherent in adversarial training, and the myriad of ways that OOD samples can arise during testing, often prevent these approaches from establishing robust decision boundaries. To address these limitations, we propose AROS, a novel approach leveraging neural ordinary differential equations (NODEs) with Lyapunov stability theorem in order to obtain robust embeddings for OOD detection. By incorporating a tailored loss function, we apply Lyapunov stability theory to ensure that both in-distribution (ID) and OOD data converge to stable equilibrium points within the dynamical system. This approach encourages any perturbed input to return to its stable equilibrium, thereby enhancing the model’s robustness against adversarial perturbations. To not use additional data, we generate fake OOD embeddings by sampling from low-likelihood regions of the ID data feature space, approximating the boundaries where OOD data are likely to reside. To then further enhance robustness, we propose the use of an orthogonal binary layer following the stable feature space, which maximizes the separation between the equilibrium points of ID and OOD samples. We validate our method through extensive experiments across several benchmarks, demonstrating superior performance, particularly under adversarial attacks. Notably, our approach improves robust detection performance from 37.8% to 80.1% on CIFAR-10 vs. CIFAR-100 and from 29.0% to 67.0% on CIFAR-100 vs. CIFAR-10. Code and pre-trained models are available at https://github.com/AdaptiveMotorControlLab/AROS.
Shape as Line Segments: Accurate and Flexible Implicit Surface Representation
Authors: Siyu Ren; Junhui Hou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Distance field-based implicit representations like signed/unsigned distance fields have recently gained prominence in geometry modeling and analysis. However, these distance fields are reliant on the closest distance of points to the surface, introducing inaccuracies when interpolating along cube edges during surface extraction. Additionally, their gradients are ill-defined at certain locations, causing distortions in the extracted surfaces. To address this limitation, we propose Shape as Line Segments (SALS), an accurate and efficient implicit geometry representation based on attributed line segments, which can handle arbitrary structures. Unlike previous approaches, SALS leverages a differentiable Line Segment Field to implicitly capture the spatial relationship between line segments and the surface. Each line segment is associated with two key attributes, intersection flag and ratio, from which we propose edge-based dual contouring to extract a surface. We further implement SALS with a neural network, producing a new neural implicit presentation. Additionally, based on SALS, we design a novel learning-based pipeline for reconstructing surfaces from 3D point clouds. We conduct extensive experiments, showcasing the significant advantages of our methods over state-of-the-art methods.
The source code is available at https://github.com/rsy6318/SALS.
MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards
Authors: Sheng Wang; Liheng Chen; Pengan CHEN; Jingwei Dong; Boyang XUE; Jiyue Jiang; Lingpeng Kong; Chuan Wu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served simultaneously.
Targeting more parameter-efficient low-rank adaptation (LoRA), parameter sharing presents a promising solution. Empirically, our research into high-level sharing principles highlights the indispensable role of differentiation in reversing the detrimental effects of pure sharing.
Guided by this finding, we propose Mixture of Shards (MoS), incorporating both inter-layer and intra-layer sharing schemes, and integrating four nearly cost-free differentiation strategies, namely subset selection, pair dissociation, vector sharding, and shard privatization. Briefly, it selects a designated number of shards from global pools with a Mixture-of-Experts (MoE)-like routing mechanism before sequentially concatenating them to low-rank matrices.
Hence, it retains all the advantages of LoRA while offering enhanced parameter efficiency, and effectively circumvents the drawbacks of peer parameter-sharing methods.
Our empirical experiments demonstrate approximately $8\times$ parameter savings in a standard LoRA setting. The ablation study confirms the significance of each component.
Our insights into parameter sharing and MoS method may illuminate future developments of more parameter-efficient finetuning methods.
The code is officially available at https://github.com/Forence1999/MoS.
Simulating Human-like Daily Activities with Desire-driven Autonomy
Authors: Yiding Wang; Yuxuan Chen; Fangwei Zhong; Long Ma; Yizhou Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy and behavioral diversity. In this paper, we introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to autonomously propose and select tasks, motivated by satisfying its multi-dimensional desires. Specifically, the motivational framework of D2A is mainly constructed by a dynamic $Value\ System$, inspired by the Theory of Needs. It incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. At each step, the agent evaluates the value of its current state, proposes a set of candidate activities, and selects the one that best aligns with its intrinsic motivations. We conduct experiments on Concordia, a text-based simulator, to demonstrate that our agent generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based agents demonstrates that our approach significantly enhances the rationality of the simulated activities.
Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling
Authors: Guiyu Zhang; Huan-ang Gao; Zijian Jiang; Hao Zhao; Zhedong Zheng
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In this paper, we focus on the task of conditional image generation, where an image is synthesized according to user instructions. The critical challenge underpinning this task is ensuring both the fidelity of the generated images and their semantic alignment with the provided conditions. To tackle this issue, previous studies have employed supervised perceptual losses derived from pre-trained models, i.e., reward models, to enforce alignment between the condition and the generated result. However, we observe one inherent shortcoming: considering the diversity of synthesized images, the reward model usually provides inaccurate feedback when encountering newly generated data, which can undermine the training process. To address this limitation, we propose an uncertainty-aware reward modeling, called Ctrl-U, including uncertainty estimation and uncertainty-aware regularization, designed to reduce the adverse effects of imprecise feedback from the reward model. Given the inherent cognitive uncertainty within reward models, even images generated under identical conditions often result in a relatively large discrepancy in reward loss. Inspired by the observation, we explicitly leverage such prediction variance as an uncertainty indicator. Based on the uncertainty estimation, we regularize the model training by adaptively rectifying the reward. In particular, rewards with lower uncertainty receive higher loss weights, while those with higher uncertainty are given reduced weights to allow for larger variability. The proposed uncertainty regularization facilitates reward fine-tuning through consistency construction. Extensive experiments validate the effectiveness of our methodology in improving the controllability and generation quality, as well as its scalability across diverse conditional scenarios, including segmentation mask, edge, and depth conditions.
Gradient correlation is a key ingredient to accelerate SGD with momentum
Authors: Julien Hermant; Marien Renaud; Jean-François Aujol; Charles Dossal; Aude Rondepierre
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm.
However, the literature has been rather pessimistic, even in the case of convex functions, about the possibility of theoretically proving this observation.
We investigate the possibility of obtaining accelerated convergence of the Stochastic Nesterov Accelerated Gradient (SNAG), a momentum-based version of SGD, when minimizing a sum of functions in a convex setting. 
We demonstrate that the average correlation between gradients allows to verify the strong growth condition, which is the key ingredient to obtain acceleration with SNAG.
Numerical experiments, both in linear regression and deep neural network optimization, confirm in practice our theoretical results.
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
Authors: Naoya Hasegawa; Issei Sato
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Real-world data distributions are often highly skewed. This has spurred a growing body of research on long-tailed recognition, aimed at addressing the imbalance in training classification models. Among the methods studied, multiplicative logit adjustment (MLA) stands out as a simple and effective method. What theoretical foundation explains the effectiveness of this heuristic method?
We provide a justification for the effectiveness of MLA with the following two-step process. First, we develop a theory that adjusts optimal decision boundaries by estimating feature spread on the basis of neural collapse. Second, we demonstrate that MLA approximates this optimal method. Additionally, through experiments on long-tailed datasets, we illustrate the practical usefulness of MLA under more realistic conditions. We also offer experimental insights to guide the tuning of MLA hyperparameters.