GenAI4ELab/papercli-papers huggingface dataset:
Lasso Bandit with Compatibility Condition on Optimal Arm
Authors: Harin Lee; Taehyun Hwang; Min-hwan Oh
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has a sparse structure.
In the existing Lasso bandit literature, the compatibility conditions, together with additional diversity conditions on the context features are imposed to achieve regret bounds that only depend logarithmically on the ambient dimension $d$.
In this paper, we demonstrate that even without the additional diversity assumptions, the \textit{compatibility condition on the optimal arm} is sufficient to derive a regret bound that depends logarithmically on $d$, and our assumption is strictly weaker than those used in the lasso bandit literature under the single-parameter setting.
We propose an algorithm that adapts the forced-sampling technique and prove that the proposed algorithm achieves $\mathcal{O}(\text{poly}\log dT)$ regret under the margin condition.
To our knowledge, the proposed algorithm requires the weakest assumptions among Lasso bandit algorithms under the single-parameter setting that achieve $\mathcal{O}(\text{poly}\log dT)$ regret.
Through numerical experiments, we confirm the superior performance of our proposed algorithm.
Equivariant Neural Functional Networks for Transformers
Authors: Hoang V. Tran; Thieu Vo; An Nguyen The; Tho Tran Huu; Minh-Khoi Nguyen-Nhat; Thanh Tran; Duy-Tung Pham; Tan Minh Nguyen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: This paper systematically explores neural functional networks (NFN) for transformer architectures. NFN are specialized neural networks that treat the weights, gradients, or sparsity patterns of a deep neural network (DNN) as input data and have proven valuable for tasks such as learnable optimizers, implicit data representations, and weight editing. While NFN have been extensively developed for MLP and CNN, no prior work has addressed their design for transformers, despite the importance of transformers in modern deep learning. This paper aims to address this gap by providing a systematic study of NFN for transformers. We first determine the maximal symmetric group of the weights in a multi-head attention module as well as a necessary and sufficient condition under which two sets of hyperparameters of the multi-head attention module define the same function. We then define the weight space of transformer architectures and its associated group action, which leads to the design principles for NFN in transformers. Based on these, we introduce Transformer-NFN, an NFN that is equivariant under this group action. Additionally, we release a dataset of more than 125,000 Transformers model checkpoints trained on two datasets with two different tasks, providing a benchmark for evaluating Transformer-NFN and encouraging further research on transformer training and performance.
Improving Deep Regression with Tightness
Authors: Shihao Zhang; Yuguang Yan; Angela Yao
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses fail to sufficiently reduce $H(Z|Y)$, despite its crucial role in generalization performance.  With this motivation, we introduce an optimal transport-based regularizer to preserve the similarity relationships of targets in the feature space to reduce $H(Z|Y)$. Additionally, we introduce a simple yet efficient strategy of duplicating the regressor targets, also with the aim of reducing $H(Z|Y)$.  Experiments on three real-world regression tasks verify the effectiveness of our strategies to improve deep regression.  Code: https://github.com/needylove/Regression_tightness
Looking Backward: Streaming Video-to-Video Translation with Feature Banks
Authors: Feng Liang; Akio Kodaira; Chenfeng Xu; Masayoshi Tomizuka; Kurt Keutzer; Diana Marculescu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts. 
  Unlike prior V2V methods using batches to process limited frames, we opt to process frames in a streaming fashion, to support unlimited frames.
  At the heart of StreamV2V lies a backward-looking principle that relates the present to the past. 
  This is realized by maintaining a feature bank, which archives information from past frames.
  For incoming frames, StreamV2V extends self-attention to include banked keys and values, and directly fuses similar past features into the output.
  The feature bank is continually updated by merging stored and new features, making it compact yet informative.
  StreamV2V stands out for its adaptability and efficiency, seamlessly integrating with image diffusion models without fine-tuning.
  It can run 20 FPS on one A100 GPU, being 15$\times$, 46$\times$, 108$\times$, and 158$\times$ faster than FlowVid, CoDeF, Rerender, and TokenFlow, respectively. 
  Quantitative metrics and user studies confirm StreamV2V's exceptional ability to maintain temporal consistency.
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression
Authors: Jingcun Wang; Yu-Guang Chen; Ing-Chao Lin; Bing Li; Grace Li Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented with a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis-Sharing outperforms state-of-the-art SVD-based compression approaches, especially at large compression ratios.
DyCAST: Learning Dynamic Causal Structure from Time Series
Authors: Yue Cheng; Bochen Lyu; Weiwei Xing; Zhanxing Zhu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Understanding the dynamics of causal structures is crucial for uncovering the underlying processes in time series data. Previous approaches rely on static assumptions, where contemporaneous and time-lagged dependencies are assumed to have invariant topological structures. However, these models fail to capture the evolving causal relationship between variables when the underlying process exhibits such dynamics. To address this limitation, we propose DyCAST, a novel framework designed to learn dynamic causal structures in time series using Neural Ordinary Differential Equations (Neural ODEs). The key innovation lies in modeling the temporal dynamics of the contemporaneous structure, drawing inspiration from recent advances in Neural ODEs on constrained manifolds. We reformulate the task of learning causal structures at each time step as solving the solution trajectory of a Neural ODE on the directed acyclic graph (DAG) manifold. To accommodate high-dimensional causal structures, we extend DyCAST by learning the temporal dynamics of the hidden state for contemporaneous causal structure. Experiments on both synthetic and real-world datasets demonstrate that DyCAST achieves superior or comparable performance compared to existing causal discovery models.
Towards Calibrated Deep Clustering Network
Authors: Yuheng Jia; Jianhong Cheng; Hui LIU; Junhui Hou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Deep clustering has exhibited remarkable performance; however, the over confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been over looked in prior research. To tackle this critical issue, we pioneer the development  of a calibrated deep clustering framework. Specifically, we propose a novel dual
head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. The calibration head adjusts the overconfident predictions of the clustering head, generating prediction confidence that matches the model learning status. Then, the clustering head dynamically selects reliable high-confidence samples estimated by the calibration head for pseudo-label self-training. Additionally, we introduce an effective network initialization strategy that enhances both training speed and network robustness. The effectiveness of the proposed calibration approach and initialization strategy are both endorsed with solid theoretical guarantees. Extensive experiments demonstrate the proposed calibrated deep clustering model not only  surpasses the state-of-the-art deep clustering methods by 5× on average in terms of expected calibration error,  but also significantly outperforms them in terms of clustering accuracy. The code is available at https://github.com/ChengJianH/CDC.
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
Authors: Byoungwoo Park; Hyungi Lee; Juho Lee
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we propose the Amortized Control of continuous State Space Model (ACSSM) for continuous dynamical modeling of time series for irregular and discrete observations. We first present a multi-marginal Doob's $h$-transform to construct a continuous dynamical system conditioned on these irregular observations. Following this, we introduce a variational inference algorithm with a tight evidence lower bound (ELBO), leveraging stochastic optimal control (SOC) theory to approximate the intractable Doob's $h$-transform and simulate the conditioned dynamics. To improve efficiency and scalability during both training and inference, ACSSM leverages auxiliary variable to flexibly parameterize the latent dynamics and amortized control. Additionally, it incorporates a simulation-free latent dynamics framework and a transformer-based data assimilation scheme, facilitating parallel inference of the latent states and ELBO computation. Through empirical evaluations across a variety of real-world datasets, ACSSM demonstrates superior performance in tasks such as classification, regression, interpolation, and extrapolation, while maintaining computational efficiency.
DisPose: Disentangling Pose Guidance for Controllable Human Image Animation
Authors: Hongxiang Li; Yaowei Li; Yuhang Yang; Junjie Cao; Zhihong Zhu; Xuxin Cheng; Long Chen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference character differs significantly from that of the driving video. In this paper, we present DisPose to mine more generalizable and effective control signals without additional dense input, which disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control. We also extract diffusion features corresponding to pose keypoints from the reference image, and then these point features are transferred to the target pose to provide distinct identity information. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet that improves the quality and consistency of generated videos while freezing the existing model parameters. Extensive qualitative and quantitative experiments demonstrate the superiority of DisPose compared to current methods. Project page: https://github.com/lihxxx/DisPose.
Online-to-Offline RL for Agent Alignment
Authors: Xu Liu; Haobo Fu; Stefano V Albrecht; QIANG FU; Shuai Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Reinforcement learning (RL) has shown remarkable success in training agents to achieve high-performing policies, particularly in domains like Game AI where simulation environments enable efficient interactions. However, despite their success in maximizing these returns, such online-trained policies often fail to align with human preferences concerning actions, styles, and values. The challenge lies in efficiently adapting these online-trained policies to align with human preferences, given the scarcity and high cost of collecting human behavior data. In this work, we formalize the problem as *online-to-offline* RL and propose ALIGNment of Game AI to Preferences (ALIGN-GAP), an innovative approach for the alignment of well-trained game agents to human preferences. Our method features a carefully designed reward model that encodes human preferences from limited offline data and incorporates curriculum-based preference learning to align RL agents with targeted human preferences. Experiments across diverse environments and preference types demonstrate the performance of ALIGN-GAP, achieving effective alignment with human preferences.
SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
Authors: Minjun Kim; Jongjin Kim; U Kang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: How can we accurately quantize a pre-trained model without any data?
Quantization algorithms are widely used for deploying neural networks on resource-constrained edge devices.
Zero-shot Quantization (ZSQ) addresses the crucial and practical scenario where training data are inaccessible for privacy or security reasons.
However, three significant challenges hinder the performance of existing ZSQ methods: 1) noise in the synthetic dataset, 2) predictions based on off-target patterns, and the 3) misguidance by erroneous hard labels.
In this paper, we propose SynQ (Synthesis-aware Fine-tuning for Zero-shot Quantization),
a carefully designed ZSQ framework to overcome the limitations of existing methods.
SynQ minimizes the noise from the generated samples by exploiting a low-pass filter.
Then, SynQ trains the quantized model to improve accuracy by aligning its class activation map with the pre-trained model.
Furthermore, SynQ mitigates misguidance from the pre-trained model's error by leveraging only soft labels for difficult samples.
Extensive experiments show that SynQ provides the state-of-the-art accuracy, over existing ZSQ methods.
CollabEdit: Towards Non-destructive Collaborative Knowledge Editing
Authors: Jiamu Zheng; Jinghuai Zhang; Tianyu Du; Xuhong Zhang; Jianwei Yin; Tao Lin
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Collaborative learning of large language models (LLMs) has emerged as a
new paradigm for utilizing private data from different parties to guarantee
efﬁciency and privacy. Meanwhile, Knowledge Editing (KE) for LLMs has also
garnered increased attention due to its ability to manipulate the behaviors of
LLMs explicitly, yet leaves the collaborative KE case—in which knowledge
edits of multiple parties are aggregated in a privacy-preserving and continual
manner—unexamined. To this end, this manuscript dives into the ﬁrst investigation
 of collaborative KE, in which we start by carefully identifying the unique
three challenges therein, including knowledge overlap, knowledge conﬂict, and
knowledge forgetting. We then propose a non-destructive collaborative KE
framework, COLLABEDIT, which employs a novel model merging mechanism
to mimic the global KE behavior while preventing the severe performance drop.
Extensive experiments on two canonical datasets demonstrate the superiority of
COLLABEDIT compared to other destructive baselines, and results shed light on
addressing three collaborative KE challenges and future applications. Our code is
available at [https://github.com/LINs-lab/CollabEdit](https://github.com/LINs-lab/CollabEdit).
RuAG: Learned-rule-augmented Generation for Large Language Models
Authors: Yudi Zhang; Pei Xiao; Lu Wang; Chaoyun Zhang; Meng Fang; Yali Du; Yevgeniy Puzyrev; Randolph Yao; Si Qin; Qingwei Lin; Mykola Pechenizkiy; Dongmei Zhang; Saravan Rajmohan; Qi Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection. To this end, we propose a novel framework to automatically distill large volumes of offline data into interpretable first-order logic rules, which are injected into LLMs to boost their reasoning capabilities. Our method begins by formulating the search process relying on LLMs' commonsense, where LLMs automatically define head and body predicates. Then, we apply Monte Carlo Tree Search (MCTS)  to address the combinational searching space and efficiently discover logic rules from data. The resulting logic rules are translated into natural language, allowing targeted knowledge injection and seamless integration into LLM prompts for LLM's downstream task reasoning. We evaluate our framework on public and private industrial tasks, including Natural Language Processing (NLP), time-series, decision-making, and industrial tasks, demonstrating its effectiveness in enhancing LLM's capability over diverse tasks.
Reveal Object in Lensless Photography via Region Gaze and Amplification
Authors: Yin Xiangjun; Huihui Yue
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Detecting concealed objects, such as in vivo lesions or camouflage, requires customized imaging systems. Lensless cameras, being compact and flexible, offer a promising alternative to bulky lens systems. However, the absence of lenses leads to measurements lacking visual semantics, posing significant challenges for concealed object detection (COD). To tackle this issue, we propose a region gaze-amplification network (RGANet) for progressively exploiting concealed objects from lensless imaging measurements. Specifically, a region gaze module (RGM) is proposed to mine spatial-frequency cues informed by biological and psychological mechanisms, and a region amplifier (RA) is designed to amplify the details of object regions to enhance COD performance. Furthermore, we contribute the first relevant dataset as a benchmark to prosper the lensless imaging community. Extensive experiments demonstrate the exciting performance of our method. Our codes will be released in \url{https://github.com/YXJ-NTU/Lensless-COD}.
Learning Video-Conditioned Policy on Unlabelled Data with Joint Embedding Predictive Transformer
Authors: Hao Luo; Zongqing Lu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The video-conditioned policy takes prompt videos of the desired tasks as a condition and is regarded for its prospective generalizability. Despite its promise, training a video-conditioned policy is non-trivial due to the need for abundant demonstrations. In some tasks, the expert rollouts are merely available as videos, and costly and time-consuming efforts are required to annotate action labels. To address this, we explore training video-conditioned policy on a mixture of demonstrations and unlabeled expert videos to reduce reliance on extensive manual annotation. We introduce the Joint Embedding Predictive Transformer (JEPT) to learn a video-conditioned policy through sequence modeling. JEPT is designed to jointly learn visual transition prediction and inverse dynamics. The visual transition is captured from both demonstrations and expert videos, on the basis of which the inverse dynamics learned from demonstrations is generalizable to the tasks without action labels. Experiments on a series of simulated visual control tasks evaluate that JEPT can effectively leverage the mixture dataset to learn a generalizable policy. JEPT outperforms baselines in the tasks without action-labeled data and unseen tasks. We also experimentally reveal the potential of JEPT as a simple visual priors injection approach to enhance the video-conditioned policy.
Enhancing Graph Of Thought: Enhancing Prompts with LLM Rationales and Dynamic Temperature Control
Authors: SungUk Shin; Youngjoon Kim
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We introduce Enhancing Graph of Thoughts (EGoT), a method designed to enhance the performance of large language models (LLMs) on complex reasoning tasks. EGoT automates the process of generating accurate responses using given data and a base prompt. The process consists of several steps: It obtains an initial response from the answering node using the base prompt. Evaluation node evaluates the response and generates reasoning for it, utilizing the score's probabilities to enhance evaluation accuracy. The reasoning from both the answering node and the evaluation node is aggregated to identify the problem in the response. This aggregated reasoning is incorporated into the base prompt to obtain an enhanced response. These steps are organized in a graph architecture, where the final leaf nodes are merged to produce a final response. As the graph descends, the temperature is lowered using Cosine Annealing and scoring, to explore diverse responses with earlier nodes and to focus on precise responses with later nodes. The minimum temperature in Cosine Annealing is adjusted based on scoring, ensuring that nodes with low scores continue to explore diverse responses, while those with high scores confirm accurate responses. In sorting 256 elements using GPT-4o mini, EGoT performs 88.31\% accuracy, while GoT (Graph of Thoughts) achieves 84.37\% accuracy. In the frozen lake problem using GPT-4o, EGoT averages 0.55 jumps or falls into the hole, while ToT (Tree of Thoughts) averages 0.89.
SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction
Authors: Jihwan Yoon; Sangbeom Han; Jaeseok Oh; Minsik Lee
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Reconstructing complex scenes with deforming objects for novel view synthesis is a challenging task. Recent works have addressed this with 3D Gaussian Splatting, which effectively reconstructs static scenes with high quality in short training time, by adding specialized modules for the deformations of Gaussian blobs. However, designing an effective deformation module that incorporates appropriate spatiotemporal inductive biases still remains unresolved. To address this issue, we propose SplineGS in this paper, which utilizes non-uniform rational B-splines (NURBS), an extension of B-spline, to represent temporally smooth deformation. A set of representative trajectories are learned based on NURBS, and the individual trajectories of Gaussian blobs are represented as linear combinations of these trajectories for spatial smoothness. The weights of the combinations are trained based on a multi-resolution hash table and an MLP, with the positions of the Gaussian blobs as the keys. Thanks to this design, the proposed method does not need any regularizers for trajectories, which enables efficient training. Experiments demonstrate that the proposed method provides competitive performance over the existing methods with much shorter training time.