Daily TMLR digest for Apr 21, 2025
TMLR
New certifications
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Survey Certification: Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Sofiane ENNADIR, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi
https://openreview.net/forum?id=M7Lhr2anjg
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Accepted papers
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Title: Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Authors: Sofiane ENNADIR, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi
Abstract: Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on static graphs, many real-world scenarios involve dynamic, temporally evolving graphs, motivating the need for Continuous-Time Dynamic Graph (CTDG) models. This paper provides a comprehensive review of Graph Representation Learning (GRL) on CTDGs with a focus on Self-Supervised Representation Learning (SSRL). We introduce a novel theoretical framework that analyzes the expressivity of CTDG models through an Information-Flow (IF) lens, quantifying their ability to propagate and encode temporal and structural information. Leveraging this framework, we categorize existing CTDG methods based on their suitability for different graph types and application scenarios. Within the same scope, we examine the design of SSRL methods tailored to CTDGs, such as predictive and contrastive approaches, highlighting their potential to mitigate the reliance on labeled data. Empirical evaluations on synthetic and real-world datasets validate our theoretical insights, demonstrating the strengths and limitations of various methods across long-range, bi-partite and community-based graphs. This work offers both a theoretical foundation and practical guidance for selecting and developing CTDG models, advancing the understanding of GRL in dynamic settings.
URL: https://openreview.net/forum?id=M7Lhr2anjg
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New submissions
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Title: Synthesizing world models for bilevel planning
Abstract: Modern reinforcement learning (RL) systems have demonstrated remarkable capabilities in complex environments, such as video games. However, they still fall short of achieving human-like sample efficiency and adaptability when learning new domains. Theory-based reinforcement learning (TBRL) is an algorithmic framework specifically designed to address this gap. Modeled on cognitive theories, TBRL leverages structured, causal world models---``theories''---as forward simulators for use in planning, generalization and exploration. Although current TBRL systems provide compelling explanations of how humans learn to play video games, they face several technical limitations: their theory languages are restrictive, and their planning algorithms are not scalable. To address these challenges, we introduce TheoryCoder, an instantiation of TBRL that exploits hierarchical representations of theories and efficient program synthesis methods for more powerful learning and planning. TheoryCoder equips agents with general-purpose abstractions (e.g., ``move to''), which are then grounded in a particular environment by learning a low-level transition model (a Python program synthesized from observations by a large language model). A bilevel planning algorithm can exploit this hierarchical structure to solve large domains. We demonstrate that this approach can be successfully applied to diverse and challenging grid-world games, where approaches based on directly synthesizing a policy perform poorly. Ablation studies demonstrate the benefits of using hierarchical abstractions.
URL: https://openreview.net/forum?id=m9V4JHLJrD
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Title: A Comprehensive Survey on Knowledge Distillation
Abstract: Deep Neural Networks (DNNs) have achieved notable performance in the fields of computer vision and natural language processing with various applications in both academia and industry. However, with recent advancements in DNNs and transformer models with a tremendous number of parameters, deploying these large models on edge devices causes serious issues such as high runtime and memory consumption. This is especially concerning with the recent large-scale foundation models, Vision-Language Models (VLMs), and Large Language Models (LLMs). Knowledge Distillation (KD) is one of the prominent techniques proposed to address the aforementioned problems using a teacher-student architecture. More specifically, a lightweight student model is trained using additional knowledge from a cumbersome teacher model. In this work, a comprehensive survey of knowledge distillation methods is proposed. This includes reviewing KD from different aspects: distillation sources, distillation schemes, distillation algorithms, distillation by modalities, applications of distillation,and comparison among existing methods. In contrast to most existing surveys, which are either outdated or simply update former surveys, this work proposes a comprehensive survey with a new point of view and representation structure that categorizes and investigates the most recent methods in knowledge distillation. This survey considers various critically important subcategories, including KD for diffusion models, 3D inputs, foundational models, transformers, and LLMs. Furthermore, existing challenges in KD and possible future research directions are discussed.
URL: https://openreview.net/forum?id=3cbJzdR78B
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Title: Text-Guided Video Amodal Completion
Abstract: Amodal perception enables humans to perceive entire objects even when parts are occluded, a remarkable cognitive skill that artificial intelligence struggles to replicate. While substantial advancements have been made in image amodal completion, video amodal completion remains underexplored despite its high potential for real-world applications in video editing and analysis. In response, we propose a video amodal completion framework to explore this potential direction. Our contributions include (i) a synthetic dataset for video amodal completion with text description for the object of interest. The dataset captures a variety of object types, textures, motions, and scenarios to support zero-shot transferring on natural videos. (ii) A diffusion-based text-guided video amodal completion framework enhanced with a motion continuity module to ensure temporal consistency across frames. (iii) Zero-shot inference for long video, inspired by temporal diffusion techniques to effectively manage long video sequences while improving inference accuracy and maintaining coherent amodal completions. Experimental results shows the efficacy of our approach in handling video amodal completion, opening potential capabilities for advanced video editing and analysis with amodal completion.
URL: https://openreview.net/forum?id=o7taRJqXWJ
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