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DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning | Accept (poster) | Summary: This paper introduces Dolphin, a novel neurosymbolic learning framework. Dolphin provides three key abstractions: symbolic objects, tags (which associate symbols with tensors), and distributions (which map symbols to probabilities). Leveraging this abstraction, Dolphin decouples symbolic and probabilistic comp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions and will add the discussions to the revised paper.
# Discussion on other uses of Dolphin
Dolphin can be used for any task where the output of a model can be cast as a distribution over probabilities, including discriminative models. Consider an example... | Summary: This work presents DOLPHIN, a Python library that allows for efficient training of traditional neurosymbolic methods on CPUs and GPUs. The key idea is to accelerate probabilistic symbolic manipulations on GPUs, where other solutions (e.g., Scallop) rely on manipulations on CPUs.
In addition to the efforts in... | Rebuttal 1:
Rebuttal: We thank the reviewer for suggestions on experiment design and additional literature. We will add the results from additional experiments discussed below in the paper and expand the related work.
# Scallop’s Performance on Mugen
Scallop’s Mugen results were obtained from their PLDI’23 artifact. De... | Summary: This paper brings enhanced scalability to neurosymbolic learning. The authors introduce a framework which allows symbolic computation to be conducted on the CPU, and allows vectorized computation of probabilities on the GPU. The authors introduce several Pythonic programming primitives to facilitate writing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions. We will add the unit of time in the caption of Table 2 and fix the grammatical errors they pointed out.
We indicate the provenance used for each benchmark in Section 4.4 (RQ2: Accuracy) and compare both provenances in Section 4.5 (RQ3: Provenance Compa... | null | null | null | null | null | null | null | null |
Open-Set Text Classification with Limited Labeling Budget | Reject | Summary: This paper combines the problem setting of active learning and open-set recognition and proposes two techniques, namely, sample sparsification and sample amplification, for addressing open-set text classification with a limited labeling budget. The first sampling method finds a good quality subset of original ... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewer our paper and comments. Our response for the comments are as below.
**Q1:** the proposed approaches are not particularly novel in terms of methodology. \
**A1:** We respectfully disagree with the reviewer. Our methodology is novel combination of many known an... | Summary: For open set recognition in text, the authors propose using a support
set, which is a subset of original samples within a budget
(sparsification) and an amplified set that is in the unknown category
(amplification). To construct the support set, they cluster the
instances with HDBSCAN, generate bins based on ... | Rebuttal 1:
Rebuttal: We thank reviewer for detailed feedback, Our responses are as follow:
**Q1:** The paper does not include results from SetFit with sample amplification. \
**A1:** In SetFit method, SBERT model is fine-tuned on samples for few-shot settings and looses the properties of SBERT model embedding space... | Summary: For the text classification problem in open domains, this paper proposes sparse sampling of labeled categories and adds samples of unknown categories. This article demonstrates through extensive experiments that the proposed method is effective in solving open domain text classification problems.
Claims And E... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and questions. Please find below our response and hope that clarifies reviewer doubts.
**Q1:** The method proposed in the paper can only solve the text classification problem of open domains involved in training. \
If unknown categories do not participate in ... | null | null | null | null | null | null | null | null |
Improved Off-policy Reinforcement Learning in Biological Sequence Design | Accept (poster) | Summary: This work focuses on the problem of biological sequence design, recognizing that trained proxy scoring models often produce unreliable predictions. To address this challenge, they introduce restrictions into GFlowNet exploration by implementing a masking mechanism that increases the probability of exploring re... | Rebuttal 1:
Rebuttal: >W1, Q2: The term "mask" is confusing
Because our infilling sites are uniformly distributed across the entire sequence, we refer to them as “masks,” similar to BERT. However, we recognize that this term can be confusing. Alternatively, we could describe the algorithm as random teacher forcing: so... | Summary: The manuscript proposes a conservative search method applied in GFlowNets for Biological Sequence Design. The proposed method restricts the number of mutations based on the length of the sequence and the prediction uncertainty. Experiments show that this sampling methodology stabilizes the training of GFlowNet... | Rebuttal 1:
Rebuttal: ### Active learning setting
We follow the existing generative active learning settings (GFN-AL [1], FLEXS [2]), which are standard in this field. As noted in our main text and appendix, we perform active learning as follows:
Starting with an initial dataset $D_0$, each active learning round t co... | Summary: The paper proposes δ-CS, a novel off-policy RL approach for biological sequence design. It addresses the challenge of proxy misspecification, where proxy models used for sequence evaluation are unreliable on ood inputs. The method is integrated into GFlowNets, and works by injecting and denoising noise into h... | Rebuttal 1:
Rebuttal: > (Theoretical Claims) No theoretical guarantees on convergence or stability with increasing rounds, would be interesting to see some theoretical or experimental insights.
Thanks for highlighting the importance of theoretical guarantees and analysis. We acknowledge that rigorous theoretical guara... | Summary: This manuscript proposes a novel off-policy search strategy, δ-Conservative Search (δ-CS), that improves the reliability of GFlowNets for biological sequence design by controlling exploration according to proxy model confidence. The method randomly masks high-scoring offline sequences with probability δ, then ... | Rebuttal 1:
Rebuttal: > W1. The initial value and adjustment strategy of $\delta$ may depend on task experience, and there is a lack of general guidelines.
In this study, we simply set delta=0.5 for DNA/RNA (masking 4 to 7 tokens on average) and delta=0.05 for protein design (masking approximately 4 to 12 tokens). Our... | null | null | null | null | null | null |
Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing | Accept (poster) | Summary: Existing static LLM evaluation benchmarks are limited by chronoeffect, where benchmarks become saturated or contaminated. To overcome this, this paper proposes Generative Evolving Testing Approach (GETA), a dynamic evaluation framework that co-evolves with LLMs by generating adaptive test items tailored to mod... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful suggestions, which is really important to us.
---
## Method
### Q1: What if evaluate GETA across more metrics?
In addition to concurrent validity, we verified GETA’s performance with three more metrics:
1. **Stability** - In App. D.2, we hav... | Summary: In view of the shortcomings of existing large language model (LLM) value evaluation methods, such as possible data set leakage or saturation, this work proposes an adaptive testing method, GETA, which is based on Computerized Adaptive Testing (CAT) and Automatic Item Generation (AIG). As a generative evolving ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and suggestions.
---
## Claim
### Q1: Why is variational IRT (VIRT) better than MLE-based IRT with fewer observations?
#### Theoretical
1. Variational Inference (VI) originated in statistical physics and was later generalized to probabilistic model research ... | Summary: The paper aims to tackle the problems of saturation and data leakage when using static benchmarks to evaluate LLMs. To solve this problem, the paper proposes GETA, an approach which leads to generate test items that are tailored to the model's capability.
Claims And Evidence: The main claim of the paper is th... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments.
We sincerely appreciate your efforts in reviewing our paper. As GETA integrates psychometric methods, we have included most of the details in the appendix due to space limits.
---
## Method
### Q1: More details of GETA (training, inference, and... | Summary: The paper proposed GETA, a psychometrics-inspired framework for adaptively evaluating the moral conformity of LLMs. Loosely speaking, the idea is to co-learn (using variational inference) an evaluator and a question generator that supports adaptively adjusting difficulty levels. Authors find that outputs of th... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable insights, which is really important to us.
Due to space limits, we regret that we have to skip some questions and provide only partial responses before your reply.
---
## Theory
### Q8: Why simpler methods fail?
1. Static evaluation (SE) is th... | null | null | null | null | null | null |
NoLiMa: Long-Context Evaluation Beyond Literal Matching | Accept (poster) | Summary: The authors present NoLiMa, which is a long-context benchmark. What differentiates it from previous long-context benchmarks is that there is a small n-gram overlap between the question text and relevant context. The authors describe the benchmark creation process, including filtering steps to remove distractor... | Rebuttal 1:
Rebuttal: First of all, we want to thank the reviewer for their thorough and detailed review and constructive feedback.
**[Limited needle set]**: While we agree the set is limited, many comparable works—such as RULER or vanilla NIAH—use even fewer or similarly limited questions.
**[The Eiffel tower painti... | Summary: This work provides an examination of the capability of large language models (LLMs) to handle long-context information retrieval tasks.
The authors propose a new benchmark, NoLiMa, which is designed to test the ability of LLMs to find relevant information in long texts without relying on direct lexical cues.
T... | Rebuttal 1:
Rebuttal: First of all, we want to thank the reviewer for their thoughtful review and constructive feedback.
**[Our works contribution compared to RULER]:**
- Our related work section highlights how extensively literal matching is involved across various long-context benchmarks—including RULER.
- We show t... | Summary: This paper present a benchmark which extend NIAH with the needle set requiring models to infer latent associations beyond literal matching. It shows current long context LLMs will have performance degradation in their proposed benchmarks.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theor... | Rebuttal 1:
Rebuttal: First of all, we want to thank the reviewer for their thoughtful review and constructive feedback.
**[32K limit]**: NoLiMa has no inherent context length limit. As stated (lines 200–206, 2nd col.), haystacks are built from random story snippets (using the filtered stories) and can be extended to ... | Summary: The paper introduces NoLiMa, a benchmark designed for advanced needle-in-a-haystack (NIAH) tests with minimal lexical overlap between questions and the relevant information (needles) within the context. NoLiMa comprises 56 question-needle pairs, each paired with contexts of varying lengths. The benchmark minim... | Rebuttal 1:
Rebuttal: First of all, we want to thank the reviewer for their thoughtful review and constructive feedback.
**[Multi-hop retrieval]**: Multi-hop retrieval, which involves fact chaining, is discussed in our "Related Work" and "Introduction" (Hsieh et al., 2024; Levy et al., 2024). NoLiMa focuses on a prior... | null | null | null | null | null | null |
Pose Prior Learner: Unsupervised Categorical Prior Learning for Pose Estimation | Reject | Summary: The authors presents Pose Prior Learner (PPL) that learns category-level pose priors from image reconstruction, without human annotations. The motivation is that given two frames of an object instance, an ideal pose prior should be able to reconstruct the image based on the estimated poses and transformations.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and have addressed each question below.
### **Q.1:** Clarity of the Motivation for the Iterative Inference Strategy.
**R.1:** The memory in PPL stores learned prototypical poses, making it a natural choice for correcting inaccurate pose estimations, especia... | Summary: This paper focuses on unsupervised pose estimation and introduces Pose Prior Learner (PPL), which learns a general categorical pose prior in a self-supervised manner to enhance pose estimation performance. The pose prior is designed as a combination of a keypoint prior, distilled from a learnable memory, and a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and have addressed each question below.
### **Q.1:** Lack of comparisons with Unsupervised Keypoints from Pretrained Diffusion Models [1]
**R.1:** We thank the reviewer for pointing us to the recent method [1]. We will include it in the related work and dis... | Summary: This paper primarily addresses the issue of existing pose estimation methods' over-reliance on manually designed prior knowledge and their sensitivity to occlusions, particularly in complex poses. Compared to the approach by He et al., the authors propose a hierarchical part-based memory module, with the follo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. | null | null | null | null | null | null | null | null |
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding | Accept (poster) | Summary: Previous speculative decoding methods treat all tokens in a sequence equally, while this paper demonstrates that earlier tokens are more critical for success. To improve efficiency, the authors propose Gumiho, a hybrid model that prioritizes early tokens with a serial two-layer Transformer and uses lightweight... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and support.
**Q1. There is a lack of comparison to the latest state-of-the-art papers, which propose better results than Eagle2, such as HASS [1] and DDD [2]**
A1:**(1) Hass** improves Eagle2 by addressing the training-inference inconsistency in serial transformer... | Summary: This paper proposes a new speculative decoding method called Gumiho. It combines the parallel draft head architecture and sequential draft head architecture to derive a hybrid architecture. The idea behind the paper is to prioritize the accuracy of the early tokens and make a rigorous mathematical proof to sho... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and support.
**Q1. The ablation study shows that using FTA has marginal improvement.**
A1: The improvement contributed by our proposed FTA method is non-negligible in the context of the overall performance gain. In the FTA ablation study, the speedup ratio increase... | Summary: This paper improves the self-speculative decoding methods by combining the architecture of Eagle and Medusa. The paper uses sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy, and multiple lightweight MLP heads operating in parallel to enhance efficie... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and support.
**Q1. It is not very clear why full tree attention works better than the tree attention methods in Eagle2, especially why this method cannot be applied in Eagle2. The authors should give an example to illustrate this better.**
A1:
**(1) Why FTA canno... | Summary: This paper proposes Gumiho, a hybrid architecture to prioritize early draft tokens with large and autoregressive heads, and parallel decoding for the rest tokens. The experimental results strongly support the effectiveness of this method.
Claims And Evidence: The experimental results strongly support the effe... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and support.
**Q1. The paper lacks a direct comparison with a strong baseline using flashdecoding.**
A1: We have evaluated the baseline with FlashDecoding (FD) on the MT-Bench dataset using Mi250 GPU, as shown in the table below.
||L2 7B|L2 13B|
|-|-|-|
|FlashDec... | null | null | null | null | null | null |
Learning multivariate Gaussians with imperfect advice | Accept (poster) | Summary: This paper studies distribution learning within the framework of learning-augmented algorithms. Specifically, the authors study how to leverage potentially inaccurate "advice" about the true distribution to achieve lower sample complexity for learning multivariate Gaussian distributions.
For Gaussians with id... | Rebuttal 1:
Rebuttal: Thank you for thoughtful and constructive review!
You are right that while the SDP formulation used in TestAndOptimizeCovariance can be solved in polynomial time from a theoretical perspective (as we show in Appendix C.3), it is definitely impractical in practice. The focus of this work is to est... | Summary: One of the fundamental tasks in statistics is learning a Gaussian in total variation (TV) distance $\epsilon$. It is well known that the sample complexity for this task is on the order of $d/\epsilon^2$ when the covariance is the identity and $d^2/\epsilon^2$ for general Gaussians. This paper investigates whet... | Rebuttal 1:
Rebuttal: Thank you for thoughtful and constructive review!
Thank you for the reference of "Gaussian Mean Testing Made Simple", we will add it in our revision.
You are also indeed correct that there is a fundamental difference between our upper and lower bounds in terms of whether the advice quality is kn... | Summary: This paper studied the problem of learning high dimensional Gaussians given imperfect advice. In particular, the authors show that given imperfect advice that is close to the true statistic quantity (in $l_1$ norm), both the following tasks have polynomial improvements with respect to the dependence of the dim... | Rebuttal 1:
Rebuttal: Thank you for thoughtful and constructive review!
You are right that while the SDP formulation used in TestAndOptimizeCovariance can be solved in polynomial time from a theoretical perspective (as we show in Appendix C.3), it is definitely impractical in practice. The focus of this work is to est... | Summary: Authors study bounds on sample complexity of mean and covariance estimation of multivariate gaussians
in learning-augmented setting. Here, the algorithm receives an untrusted advice in the form
of estimates of the mean and the covariance matrix.
The goal is to improve the sample complexity beyond the classical... | Rebuttal 1:
Rebuttal: Thank you for thoughtful and constructive review!
For the mean setting, one can trivially obtain a sample complexity of $\widetilde{O}(\sqrt{d}/\varepsilon^2)$ when the advice quality is "good enough" by first running the tolerant tester (see Lemam 1.5) with $k = d$ and $\alpha = \varepsilon$, an... | null | null | null | null | null | null |
CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models | Accept (poster) | Summary: This paper focus on addressing the incorrect responses of referring MLLMs tailored to misleading areas adjacent to or similar to the target region. Specifically, it introduces contrastive visual prompts generated by prompt extraction network, which is trained on extra dataset. Besides, to alleviate the computa... | Rebuttal 1:
Rebuttal: ### **Q1 Training process**
Yes, we agree that our training process involves additional stages—specifically, self-training and distillation—which require 30 hours and 25 hours, respectively, to train for 50K steps on 8 A100 GPUs for the 7B model. However, we would like to kindly clarify the follo... | Summary: The paper introduces CPCF (Cross-Prompt Contrastive Framework), a novel approach to improving Referring Multimodal Large Language Models. These models extend standard Multimodal Large Language Models by allowing them to focus on specific image regions using visual prompts. CPCF leverages a contrastive decoding... | Rebuttal 1:
Rebuttal: Thank you so much for your time in reviewing our paper and the valuable comments! We are sincerely encouraged by your recognition that our contributions are meaningful, the proposed method is reasonable, and the ablation study results are sound. We would like to provide the following responses to ... | Summary: The article aims to solve the defects of existing referring MLLMs: it is difficult to accurately locate the prompt, and designs several solutions: 1. an effective referring MLLM framework that contrasts input prompts with contrastive prompts from misleading regions, 2. an automatic prompt extraction mechanism ... | Rebuttal 1:
Rebuttal: Thank you very much for your time in reviewing our paper and the valuable comments! We are sincerely encouraged by your recognition that our experimental results are good, the robustness analysis is detailed, the innovation is clever, and the overall quality of the article is high. For your questi... | Summary: This paper introduces CPCF, a cross-prompt contrastive framework for referring multimodal large language models. It improves region-specific response accuracy by automatically generating contrastive prompts from misleading regions, training these through a self-training strategy on additional unlabeled data, a... | Rebuttal 1:
Rebuttal: We sincerely thank you for the time and effort you dedicated to reviewing our paper, as well as for your highly valuable comments! We would like to provide the following responses to address your concerns and questions:
### **Q1 Concerns on feature contradictions**
Thank you for this comment! We ... | Summary: This paper addresses the performance limitations of referring multimodal large language models (MLLMs), which often misinterpret ambiguous or misleading visual regions during referring comprehension tasks. To overcome this limitation, the authors propose the Cross-Prompt Contrastive Framework (CPCF), which imp... | Rebuttal 1:
Rebuttal: Thank you very much for your time in reviewing our paper and the valuable comments! We are sincerely encouraged by your recognition that our method is novel and well-developed, experiments are extensive, and writing is good. For your questions and concerns, we are happy to provide the following re... | Summary: This paper presents CPCF, a cross-prompt contrastive learning framework designed to enhance the performance of referring multimodal large language models (MLLMs). The proposed approach aims to address a key issue in existing referring MLLMs: errors caused by misleading visual regions adjacent to or similar to ... | Rebuttal 1:
Rebuttal: ### **Q1 Writing**
We will carefully revise the writing based on your suggestions. “CPCF” is an abbreviation formed from the initials of "Cross-Prompt Contrastive Framework". We will clarify this in the revised paper.
### **Q2 Figures**
We will incorporate results of the image editing experiment i... | Summary: This paper introduces CPCF, a novel framework designed to enhance referring capabilities in MLLMs. The method leverages a cross-prompt contrastive strategy, in which responses generated from visual prompts are contrasted with those from misleading regions. The framework further incorporates a prompt extraction... | Rebuttal 1:
Rebuttal: **Q1 Discussion with other contrastive decoding (CD) methods (like CRG)**
* Although both methods use CD, the contrastive targets are different. CRG constructs contrastive targets by removing the target region directly from the image, which may severely disrupt the image’s integrity—especially wh... |
Improving the Scaling Laws of Synthetic Data with Deliberate Practice | Accept (oral) | Summary: The authors propose a method for synthetically generating data based on an entropy-guided sampling of diffusion models. Their method is dynamic in that they call their entropy-guided sampling every time their model's validation accuracy plateaus. They show that their synthetically generated data is better than... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and detailed feedback.
---
### **On theory**
**Relation to Active Learning.** Though related at a high level, our work differs from standard active learning, which focuses on querying labels for unlabeled data. We instead focus on generating useful tra... | Summary: This paper focuses on the task of synthetic data generation for image classification. Specifically, traditional methods of synthetic data generation in classification tasks suffer from diminishing returns as the dataset size increases, leading to inefficient use of generated data. Inspired by the concept of de... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and encouraging review. We’re glad to hear that you found the method to be conceptually intuitive, the theoretical analysis to be sound, and the experimental setup and evaluation thorough.
### **Qualitative Analysis of Hard Samples:**
Thank you for raising... | Summary: This paper empirically demonstrates that "delibrate practice" is meaningful in improving the scaling law of synthetic data generation. Broadly speaking, this falls under the general umbrella of efficiently collecting as few data as possible, with the additional problem context being, a generative model is used... | Rebuttal 1:
Rebuttal: Thank you for your review.
The concept of "deliberate practice" has indeed been borrowed from psychology and has thematic connections to "curiosity". However, to the best of our knowledge, we are the first to adapt and implement this principle in the setting of training classifiers entirely on s... | Summary: The authors introduce a new methodology called "Deliberate Practice for Synthetic Data Generation" (abbreviated DP) to train a machine learning model for classification using entirely synthetic samples generated from a pre-trained diffusion model. Instead of generating many samples and pruning, the method uses... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. We're glad that you found the method conceptually clear. Also, thank you for the helpful proofreading suggestions. We will incorporate these corrections in the final version.
---
**Connection Between Theory and Practice:** We agree that the linear... | Summary: This work addresses the challenge of improving data size scaling laws for models trained on synthetic data. In particular, it uses the intuitive idea of generating more synthetic data where the model being trained (referred to as the learner) has high entropy. To do so, the paper relies on a setting where the ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and encouraging review, we are very pleased that you found the paper intuitive, practical, and effective.
### **Related Work and Data Pruning:**
Thanks for pointing out these relevant papers. While our method focuses on improving training via generation of informati... | null | null | null | null |
Understanding Nonlinear Implicit Bias via Region Counts in Input Space | Accept (poster) | Summary: The paper proposes a new region count metric for characterizing neural networks' implicit biases. This metric counts the number of connected regions in the input space with the same predicted label in a low-dimensional subspace. One of the advantages of this output-based metric over parameter-based metrics, su... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent on reviewing our work and for the very detailed comments. Please find the details below.
>Q1: How to use the region count metric for regularization?
**A1:** Due to the word limit, we respectully refer the reviewer to Reviewer VXuC's response A2.
>Q2: ... | Summary: This paper introduces the notion of connected region count in the input space and shows that it is strongly correlated with the generalization gap. Moreover, it is noted that larger learning rates and smaller batch sizes can lead to smaller region counts. Theoretically, It is proved that for a two-layer ReLU n... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive suggestions. In the following, we address the main concern raised. Please find the details below.
>Q1: It would be nice if more datasets and architectures can be analyzed.
**A1:** We thank the reviewer for the suggestion. We add a experime... | Summary: In this work, authors propose region count as a metric to quantify implicit bias / generalizability of neural networks. A region is defined as a set of input points which are classified in the same way by the network; authors show that fewer regions lead to increased generalizability (quantified as gap between... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We address the main concerns below:
>Q1: Personally, I would like to see some results for networks which did not converge and exhibit a high test error (the lowest result in Tab. 2 is 0.78 on imagenet). To what extent (going down in generalizabilit... | Summary: The authors propose using low-dimension region counts as a proxy for the generalization performance. They empirically test the correlation between region count and generalization performance, and provide a bound on the region count for two layer ReLU neural networks.
Claims And Evidence: The claims are not ad... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's comments and valuable suggestions. We added extensive experiments and summarize them below. We will include more detailed results in the revision.
>Q1: It is unclear whether region count is a good proxy for generalization performance, because it is unclear wh... | null | null | null | null | null | null |
Positional Attention: Expressivity and Learnability of Algorithmic Computation | Accept (poster) | Summary: This paper is a theoretical study on positional transformers, where the attention is determined solely by positional encoding regardless of the content. The paper includes a representation theory that position transformers can simulate MPC, followed by a generalization bound. Empirical study is conducted on se... | Rebuttal 1:
Rebuttal: We thank the reviewer for allowing us to clarify the motivation of our paper. We will address their questions individually and provide general remarks below.
*High-level motivation:* Generally, there is growing interest in the relationship between neural networks and computational models [1,2,3,4... | Summary: This paper introduces and analyzes positional attention in Transformers where attention weights depend exclusively on positional encodings rather than on input data. The authors prove that Transformer with positional attention with logarithmic depth has the same expressive power as MPC, and demonstrate that po... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to read and review our manuscript. In what follows, we provide a comprehensive response to the weaknesses highlighted by the reviewer.
> The proposed method seems to be heavily dependent on the positional encodings used in the Transformer model ... | Summary: This paper presents transformer with positional attention (PT), a mechanism that implements data-free query and key inputs for computing attention scores. From the empirical side, this mechanism aims to emulate the massively parallel computation (MPC) model, which the authors show expressivity and learnability... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and appreciate their recognition of the soundness of our paper. We appreciate the connections made to other related works, which we will incorporate into the main paper. Below, we address their insightful comments, specifically those written in Methods And ... | Summary: This paper proposes the Positional Transformer architecture for learning algorithmic problems over abstract data structures. In Positional Transformers, the attention maps are computed merely based on the positional embeddings and therefore the learned interaction patterns between different positions in the in... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s time in evaluating our manuscript. We are grateful for their recognition of the novelty and significance of our work. Below, we address their insightful comments.
> There’s a quite extensive literature around GNNs for algorithmic problems and in particular N... | null | null | null | null | null | null |
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations | Accept (poster) | Summary: This paper introduces SDE Matching, a novel simulation-free method for training Latent Stochastic Differential Equations (SDEs). Traditional training of Latent SDEs relies on adjoint sensitivity methods, which are computationally expensive due to numerical integration and backpropagation through SDE solutions... | Rebuttal 1:
Rebuttal: We are delighted to see that the reviewer finds our approach Innovative, efficient and note the reduction in computational cost of training. Below, we address the questions raised in the review.
**Questions:**
In all our experiments, including the 3D stochastic Lorenz attractor, the motion captu... | Summary: The author(s) proposed SDE-matching, a simulation-free method to fit latent SDE models. The key idea is to use a differentiable normalizing flow method to learn the Markovianization of the posterior SDE and match the probability flow ODE defined by the normalizing flow to get back the SDE.
Claims And Evidence... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer finds our claims to be well supported theoretically, the experiments well designed, and the discussions insightful. We are especially grateful that the reviewer took the time to reproduce our experiments. We sincerely thank the reviewer for such attention to our pa... | Summary: This paper improves the method of Course & Nair for variational inference in latent SDEs in 2 ways: A better recognition network and more flexible marginals through normalizing flows.
Claims And Evidence: The claims are basically that it's a fast and flexible approach. Also that it's much faster than the So... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s positive feedback regarding the speed, simplicity, and sensibility of our method, as well as the discussion of its connections to diffusion models. Below, we address the questions and comments raised in the review.
**References:**
- We will cite the *Nature* paper by... | Summary: This paper builds on the observation that the reverse process in score-based diffusion models can be seen as a neural SDE (as fundamentally it is a process with a parameterized drift). Then the authors try to exploit this connection to develop a simulation-free training scheme for latent SDEs.
Claims And Evid... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback, which helps us to improve the paper. Below, we address the questions and comments raised in the review.
**Other Comments:**
We apologise for any confusion regarding terminology. Allow us to provide a clearer definition of the term “simulation-f... | null | null | null | null | null | null |
Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization | Accept (poster) | Summary: This paper targets the problem of solving mathematical problems with LLMs. While the currently prevalent method LongCoT brings promising improvements for mathematical reasoning, they are sometimes unnecessary and cause token waste. To alleviate this issue, the authors propose an algorithm Inference Budget-Cons... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and valuable feedback! We greatly appreciate your insightful suggestions and positive assessment!
---
> The authors demonstrate (specifically in Figure 2 Column 3) that the voting budget can adaptively change w.r.t. the difficulty level of the problem... | Summary: This paper proposes IBPO to optimize reasoning length allocation in large language models (LLMs). While extended reasoning chains improve accuracy, they often lead to inefficiencies by applying unnecessary long reasoning to trivial queries. IBPO formulates this as a constrained reinforcement learning (RL) prob... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time in assessing our paper and your thoughtful feedback.
---
We’d like to first clarify a possible **misunderstanding** regarding the **role of sequential voting** (SV): it is **not** intended as a significant contribution of this work.
**Role of SV:** It is a **s... | Summary: This paper discusses a method for scaling LLM test time compute on adaptive basis based on prompt difficulty. The proposed approach is a novel reinforcement learning technique that allocates more inference to difficult problems (adaptive number of votes - where each vote requires an inference) and fewer votes ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
---
We extend some discussion on **accuracy** to clarify our evaluation design, which also helps explain our choice of baseline later on.
While important, accuracy depends on **two orthogonal axes**: (i) the RL axis; and (ii) the reasoning axis.
$$
\underb... | Summary: With the prevalence of Chain of Thought (CoT) in complex reasoning and the emergence of ultra-long reasoning models such as OpenAI-o1 and DeepSeek-R1, unnecessarily tedious and long generations for trivial problems are increasingly becoming a problem. The paper approaches this problem from a RL perspective, pr... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and valuable feedback! We greatly appreciate your insightful suggestions and positive assessment!
---
We address these three comments collectively, given their relevance to one another.
> However, I'm still hesitant to accept that responses construct... | null | null | null | null | null | null |
Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning | Accept (poster) | Summary: This paper proposes a novel approach named positive-unlabeled recommender learning, to handling the challenge of missing negative feedback in implicit feedback recommendation systems. The key contribution is Progressive Proximal Transport (PPT), an optimal transport-based method that estimates the class prior ... | Rebuttal 1:
Rebuttal: #### **[W1] More discussion on scalability.**
**Response.** We express our sincere gratitude for this valuable comment. **We add a scalability analysis on the additional ML-1M dataset that is larger than the datasets involved in this paper.**
In the below table, we summarize the performance of PU... | Summary: - The study reframes implicit feedback recommendation as a weakly supervised learning problem, and introduces a model-agnostic framework, termed PURL, to handle the missing negative feedback problem.
- Central to this framework is the incorporation of the PU-learning method, which ensures unbiasedness given p... | Rebuttal 1:
Rebuttal: #### **[W1] Ablation study matters.**
**Response:** Thank you for your kind comment! In this work, we devise two components: (1) the PURL loss; (2) the PPT strategy. **We have conducted experiments to discern their individual contributions**.
- To discern the contribution of PURL that leverages un... | Summary: The paper addresses the challenge of missing negative feedback (MNF) in implicit feedback-based recommender systems. Traditional approaches often rely on negative sampling, which risks misclassifying positive samples as negative, leading to bias and performance degradation. The authors propose PURL (Positive-U... | Rebuttal 1:
Rebuttal: **Thank you very much for your kind and sincere words. It is impressive to meet a review that candidly express limitations while responsibly and analyzing the claims and details of the paper. We truly appreciate it!**
We have noticed the question about the dataset generation process and are happ... | Summary: This paper focuses on addressing the lack of negative feedback in recommendations with implicit feedback and points out the limitations of recent unbiased estimator-based methods in identifying propensity scores and non-negative estimates. It proposes a novel positive-unlabeled recommender learning (PURL) fram... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer’s great efforts and insightful comments to improve our manuscript. In below, we address the raised concerns point by point and try our best to clarify any confusions.**
#### **[W1] The connection between mass weight $w$ to class prior estimation is not expli... | null | null | null | null | null | null |
COKE: Core Kernel for More Efficient Approximation of Kernel Weights in Multiple Kernel Clustering | Accept (poster) | Summary: The paper introduces a novel approach to multiple kernel clustering by proposing the concept of core kernel. This method aims to reduce the computational complexity of clustering large datasets while preserving performance.
**Main Contributions:**
1. Core Kernel Definition: The core kernel is defined as a se... | Rebuttal 1:
Rebuttal: We appreciate the comments of Reviewer s6E6 and have responded to them individually.
### Q1: Why are kernel weights crucial?
A2: In many previous studies, it can be observed that the kernel weights of multiple kernel clustering (MKC) algorithms have a significant impact on the learning performan... | Summary: This paper introduces a novel concept called the "core kernel," which aims to approximate kernel weights in multiple kernel clustering (MKC) algorithms by running them on smaller-scale base kernel matrices. The core kernel, with a size of $\widetilde{\mathcal{O}}(1/\varepsilon)$, achieves a $1+\varepsilon$-app... | Rebuttal 1:
Rebuttal: We sincerely appreciate the work of Reviewer hzsf and respond to the review comments as follows.
### Q1: Assumption 3.2 assumes eigenvalue gaps, but real-world data may violate this. If eigenvalue gaps approach zero, does Theorem 3.3 still hold? Please discuss this scenario.
A1: The reason for m... | Summary: This paper first proposes the concept of core kernel, and proposes a core kernel construction method based on singular value decomposition, and proves that it meets the core kernel definition of three mainstream MKC algorithms. The correctness of the theoretical results and the effectiveness of the proposed me... | Rebuttal 1:
Rebuttal: We greatly appreciate the feedback of Reviewer qRUV and have addressed each comment point by point, as detailed below.
### Q1: The relationship between Algorithm 1 and spectral clustering.
A1: Assume that the consensus kernel matrix obtained by the original multiple kernel clustering (MKC) is $... | null | null | null | null | null | null | null | null |
TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation | Accept (poster) | Summary: The paper presents TransPL, a novel unsupervised domain adaptation (UDA) method for time series data that improves pseudo-labeling by capturing temporal transitions and channel-wise shifts between domains. Traditional pseudo-labeling methods fail to model these patterns, leading to suboptimal labels. TransPL a... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoroughly going through our manuscript, and providing valuable comments. We want to highlight points that were misunderstood and should be clarified. While we wish to thank you individually for each point, we are constrained by the length limit. Thank you for your unders... | Summary: The paper introduces **TransPL**, a novel unsupervised domain adaptation (UDA) strategy for time series classification. It addresses the key challenge of domain variability in time series data arising from **temporal transitions** and **sensor characteristics**. To tackle this, the method employs a **coarse-to... | Rebuttal 1:
Rebuttal: We first and foremost thank the reviewer for the thorough review of our work. We greatly appreciate your services. Here, we have prepared a detailed explanation to address each of the reviewer's questions.
**E1. Performance of Baseline Methods.** Thank you for pointing this out. The performance ... | Summary: This paper introduces TransPL, a novel pseudo-labeling approach for unsupervised domain adaptation in time series data. The authors argue that traditional pseudo-labeling strategies fail to capture temporal patterns and channel-wise shifts between domains, leading to sub-optimal pseudo labels. To address this,... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thorough evaluation of our manuscript and the valuable feedback provided. We are pleased that the reviewer found our work to have strong experimental results and well-presented visualizations. Below, we have prepared a detailed response to address the reviewe... | Summary: The authors propose a new method, TransPL, for unsupervised time series adaptation. Unsupervised domain adaptation deals with settings where a model trained on source domain data with available labels has to be adapted to perform well in target domain with shifted data without available labels.
The propo... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer fkT4 for the thorough, insightful, and constructive feedback. We appreciate that the reviewer has found our work a novel contribution for UDA time series that has not been explored elsewhere. We also thank the reviewer for thinking through our problem definition with us... | null | null | null | null | null | null |
Leveraging Per-Instance Privacy for Machine Unlearning | Accept (poster) | Summary: This work introduces a theoretically grounded, data-dependent measure of difficulty between data privacy and unlearning. While the current analysis has limitations (scalability, group guarantees), the empirical results are compelling.
Claims And Evidence: The identification of "harder" forget sets via privacy... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Your review raises several important points: concerns about scalability to larger models and datasets, the practical implications of group-level unlearning, assumptions around noise in SGD, and the strength of causal interpretations in our analysis. You also asked abou... | Summary: This paper presents a per-data sample approach to quantifying the difficulty of unlearning the same via fine-tuning. They do this by adapting the definition and analysis of Thudi et al. (https://arxiv.org/abs/2307.00310) on per-instance DP to the unlearning setting to produce a quantity called a “privacy loss”... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Your review raises two key concerns: the completeness of experimental comparisons between our proposed privacy loss and other proxy metrics, and the practical value of the newly introduced “loss barrier” measure. You also suggest clarifying definitions and improving th... | Summary: The paper proposes a per-instance approach to quantifying the difficulty of unlearning via fine-tuning by replacing the worst-case Rényi-DP bound with per-instance privacy losses. The authors introduce loss barriers as a way for evaluation, which are significantly reduced after unlearning. Alternative cheap pr... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Your review focuses on question around the computational efficiency of our method for computing per-instance privacy losses, especially in the context of large-scale models and datasets. You also provide suggestions for improving the clarity of figure and definitions, ... | Summary: This paper provides a theoretical foundation to understand the relationship between per-instance privacy loss and unlearning hardness. It applies a recent per-instance privacy loss to fine-tuning-based unlearning and builds a relationship bewteen unlearning steps with the bound of Renyi divergence between a mo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Your review primarily raises concerns about the practical utility of our proposed privacy loss metric—specifically, whether its per-instance theoretical guarantees translate to improved empirical unlearning performance. You also highlight the focus on group-... | Summary: This paper considers the machine unlearning problem, which involves removing the influence of a subset of training data from a trained model. The authors explored a setup in which both learning and unlearning are done via noisy gradient descent and proposed to use the "per-instance privacy loss" to estimate th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. Your review raises two main concerns: (1) the completeness of comparisons with alternative proxy metrics, and (2) the clarity of our empirical claims regarding how accurately privacy losses predict unlearning difficulty. To address the first concern, we have... | null | null | null | null |
Conditioning Diffusions Using Malliavin Calculus | Accept (poster) | Summary: In this paper, the authors propose a framework based on Malliavin Calculus to address the robustness issue associated with singular rewards. Their work focuses on the problem of diffusion bridges, a type of diffusion process with fixed endpoints. Using Doob's h-transform and Malliavin Calculus, the authors der... | Rebuttal 1:
Rebuttal: Thanks for your review! We're glad you think the method is interesting. We hope we will be able to address your concerns on the scalability and efficiency of our proposed method, which we discuss below!
### Section q3Kt.1: Larger Experiments and Flow-Matching
Thank you for the great suggestions.... | Summary: The authors propose a novel solution to tackling the interesting and challenging problem of diffusion bridges conditioned on singular rewards.
To solve this issue, they make use of theory of Malliavin calculus (essentially stochastic calculus of variations from my understanding) to handle the singularities.
Us... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive review! We're very happy to hear that you really enjoyed this paper! And we were especially happy that you found the general quality to be on par with some of the seminal papers in this field.
We appreciate your detailed comments and questions, and answer them below.... | Summary: The work develops a technique for learning the control process that
conditions a diffusion on the terminal state given the initial state.
A key advantage of their method is that it is robust to singular
rewards. They test their algorithm on multi-well toy experiments,
and show that their algorithm successfully... | Rebuttal 1:
Rebuttal: We really appreciate your positive review and that you think our paper is well written!
***One weakness is that the experiments are fairly toy***
We based our experiments on setups from the most closely connected literature and are glad you found them more substantial than in similar papers!
W... | Summary: This paper introduces a novel approach to conditioning diffusion processes using Malliavin calculus, enabling stable training of score-based diffusion bridges. By replacing the ordinary derivative by Malliavin derivative, their framework unifies and extends existing diffusion bridge methods. Through controlled... | Rebuttal 1:
Rebuttal: Thanks for your review! We're glad you see our method as unifying and extending diffusion bridge approaches. Below, we address your questions and concerns.
***For theorem 2.1 as $\sigma(X_t)$ becomes singular, wouldn't you have stability issues with its inverse? & The method assumes that the matr... | null | null | null | null | null | null |
MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges | Accept (poster) | Summary: The author proposed the MixBridge framework to conduct backdoor attacks on the Image-to-Image diffusion bridge model (called the Image-to-image Schrodinger bridge (I2SB)). Existing methods are homogeneous attacks, while MixBridge can achieve heterogeneous attacks. Specifically, MixBridge introduces the Mixture... | Rebuttal 1:
Rebuttal: We sincerely express our gratitude for your acceptance and constructive comments. Due to space constraints, we include `additional_tables.pdf` and visualizations at the following link: https://anonymous.4open.science/r/ICML25-5787/. References to **Table x** and **Figure x** in our responses corre... | Summary: This paper introduces MixBridge, a novel Diffusion Schrödinger Bridge (DSB)-based approach for enabling heterogeneous backdoor attacks on image-to-image models. The authors first demonstrate that a straightforward method—training a single DSB model with poisoned image pairs—can effectively execute such attacks... | Rebuttal 1:
Rebuttal: We sincerely thank you for accepting our work and for your constructive comments. Due to space constraints, we include `additional_tables.pdf` and visualizations at the following link: https://anonymous.4open.science/r/ICML25-5787/. References to **Table x** and **Figure x** in our responses corre... | Summary: This paper introduces MixBridge, a novel diffusion Schrödinger bridge (DSB) framework designed to implant multiple heterogeneous backdoor triggers in bridge-based diffusion models, which accommodate complex and arbitrary input distributions. Unlike prior backdoor approaches that focus on single-attack scenario... | Rebuttal 1:
Rebuttal: We sincerely thank you for accepting our work and for your constructive comments. Due to space constraints, we include `additional_tables.pdf` and visualizations at the following link: https://anonymous.4open.science/r/ICML25-5787/. References to **Table x** and **Figure x** in our responses corre... | Summary: The paper presents a framework for injecting multiple heterogeneous backdoor triggers into bridge-based diffusion models. The authors propose a "Divide-and-Merge" strategy, where backdoors are trained independently and later integrated using an MoE framework. Additionally, a Weight Reallocation Scheme (WRS) is... | Rebuttal 1:
Rebuttal: We sincerely express our gratitude for your acceptance and constructive comments. Due to space constraints, we include `additional_tables.pdf` and visualizations at the following link: https://anonymous.4open.science/r/ICML25-5787/. References to **Table x** and **Figure x** in our responses corre... | null | null | null | null | null | null |
MindCustomer: Multi-Context Image Generation Blended with Brain Signal | Accept (poster) | Summary: This paper introduces MindCustomer, a novel multi-context image generation framework that integrates visual brain signals with traditional image and text modalities to enable brain-controlled image customization. The authors propose a method to blend brain semantics into the image generation process, addressin... | Rebuttal 1:
Rebuttal: We sincerely thank you for your reviews!
***Rebuttal Link*** (https://anonymous.4open.science/r/ICML2025-Rebuttal-MindCustomer) is for supplemented results in rebuttal, please refer to it.
**Q1: Quantitative measures of ablation study and indicators of reconstructed image**
**In the submitted p... | Summary: This paper proposes a new task: image customization with brain signals, which is novel and interesting. The authors introduce the image-brain translator and brain embedded to align various modalities, including images, fMRI, and texts, together for generating new images with a versatile diffusion model.
Claim... | Rebuttal 1:
Rebuttal: We sincerely thank you for your reviews!
***Rebuttal Link*** (https://anonymous.4open.science/r/ICML2025-Rebuttal-MindCustomer) is for supplemented results in rebuttal, please refer to it.
**Claims And Evidence: Reference error**
Thank you for pointing out the citation error. It should be: Taka... | Summary: This paper proposes a novel framework, MindCustomer, to explore the blending of visual brain signals in multi-context image generation. This approach enables cross-subject generation, delivering unified, high-quality, and natural image generation.
Claims And Evidence: 1. This paper claims some potential for ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your reviews!
***Rebuttal Link*** (https://anonymous.4open.science/r/ICML2025-Rebuttal-MindCustomer) is for supplemented results in rebuttal, please refer to it.
**Q1: Claim of "potential for practical application"**
- **As the first** to propose and achieve the fusio... | Summary: This paper proposes MindCustomer, an image generation method with input of image, text and brain signals. The brain signals is converted by Image-Brain Translator(IBT) into image embeding space for subsequent text-image joint generation. The diffusion model is finetuned and the embeddings is optimized to achie... | Rebuttal 1:
Rebuttal: We sincerely thank you for your reviews!
***Rebuttal Link*** (https://anonymous.4open.science/r/ICML2025-Rebuttal-MindCustomer) is for supplemented results in rebuttal, please refer to it.
**W1: Comments on contributions**
- Diffusion model fine-tuning and linear interpolation are well-establish... | null | null | null | null | null | null |
Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images | Accept (poster) | Summary: This work aims at addressing the quality assessment task for authentically disorted images from a semi-supervised learning perspective. The key idea is to leverage confidence quantifiable pseudo-label learning to confront the data insufficiency challenge. The proposed CPL-IQA can be trained on unlabeled images... | Rebuttal 1:
Rebuttal: Thank you for your affirmation and the valuable comments that help us improve our work. The following is a careful response and explanation about the weaknesses and questions.
# For Experimental Designs Or Analyses
We sincerely appreciate your recognition of our experimental design and analysis, ... | Summary: This paper presents a novel semi-supervised blind image quality assessment framework, named CPL-IQA, for assessing the quality of real distorted images. The method effectively utilizes a large number of unlabeled real distorted images through confidence-quantifiable pseudo-label learning, addressing the challe... | Rebuttal 1:
Rebuttal: Thanks for your affirmation and the valuable comments that help us improve our work. The following is a careful response and explanation about the weaknesses and questions.
# For Weakness 1
We sincerely appreciate the constructive feedback. We would like to clarify that Sections 3.3.1 to 3.3.6 (p... | Summary: This work focuses on semi-supervised blind image quality assessment (BIQA). The method first converts MOS labels to vector labels via entropy minimization, then constructs nearest neighbor graph to help label optimization with confidence. The pseudo labels are then combined with ground truth to guide the model... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments and will improve in final version.
# For Claims & Evidence
This is a misunderstanding. We clarify that:
1. **The purpose of Fig. 1 is to demonstrate the limitations of some existing semi-supervised BIQA methods that require full score distributions (Left Lines ... | Summary: In this paper, an algorithm named CPL-IQA is proposed for semi-supervised BIQA task. The proposed algorithm leverages confidence-quantifiable pseudo label learning to utilize the unlabeled images for training. Specifically, it first converts MOS labels to vector labels via entropy minimization. Then, during tr... | Rebuttal 1:
Rebuttal: Thanks for the affirmation and valuable suggestions. We have addressed each point below in detail.
# For Essential References Not Discussed
In fact, our method is not directly comparable with [1], [2], or [3], as they follow fundamentally different paradigms. Specifically:
* **Ours: A semi-supervi... | null | null | null | null | null | null |
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