ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 14 154 | paper_url stringlengths 42 42 | authors listlengths 1 82 | type stringclasses 3
values | primary_area stringclasses 21
values | abstract large_stringlengths 413 2.52k | keywords listlengths 1 23 | TL;DR large_stringlengths 5 250 ⌀ | submission_number int64 2 14.3k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Beyond Random Masking: When Dropout meets Graph Convolutional Networks | https://openreview.net/forum?id=PwxYoMvmvy | [
"Yuankai Luo",
"Xiao-Ming Wu",
"Hao Zhu"
] | Poster | learning on graphs and other geometries & topologies | Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on graph-structured data, yet the behavior of dropout in these models remains poorly understood. This paper presents a comprehensive theoretical analysis of dropout in GCNs, revealing that its primary role differs fundamentally from standar... | [
"Graph neural networks",
"Dropout"
] | null | 14,284 | null | null | [
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Self-supervised contrastive learning performs non-linear system identification | https://openreview.net/forum?id=ONfWFluZBI | [
"Rodrigo González Laiz",
"Tobias Schmidt",
"Steffen Schneider"
] | Poster | learning on time series and dynamical systems | Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to ... | [
"system identification",
"dynamics learning",
"identifiability",
"self-supervised learning"
] | null | 14,280 | 2410.14673 | title_snapshot | [
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DarkBench: Benchmarking Dark Patterns in Large Language Models | https://openreview.net/forum?id=odjMSBSWRt | [
"Esben Kran",
"Hieu Minh Nguyen",
"Akash Kundu",
"Sami Jawhar",
"Jinsuk Park",
"Mateusz Maria Jurewicz"
] | Oral | alignment, fairness, safety, privacy, and societal considerations | We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns—manipulative techniques that influence user behavior—in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful genera... | [
"Dark Patterns",
"AI Deception",
"Large Language Models"
] | We introduce DarkBench, a benchmark revealing that many large language models employ manipulative dark design patterns. Organizations developing LLMs should actively recognize and mitigate the impact of dark design patterns to promote ethical Al. | 14,257 | 2503.10728 | title_snapshot | [
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Sparse autoencoders reveal selective remapping of visual concepts during adaptation | https://openreview.net/forum?id=imT03YXlG2 | [
"Hyesu Lim",
"Jinho Choi",
"Jaegul Choo",
"Steffen Schneider"
] | Poster | interpretability and explainable AI | Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mechanisms take place during adaptation. Here we develop a new Sparse Autoencoder (SAE) for the CLIP vision transformer, named PatchSAE, to ext... | [
"interpretability",
"vision-language models",
"sparse autoencoder",
"adaptation"
] | null | 14,240 | 2412.05276 | title_snapshot | [
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PIED: Physics-Informed Experimental Design for Inverse Problems | https://openreview.net/forum?id=w7P92BEsb2 | [
"Apivich Hemachandra",
"Gregory Kang Ruey Lau",
"See-Kiong Ng",
"Bryan Kian Hsiang Low"
] | Poster | applications to physical sciences (physics, chemistry, biology, etc.) | In many science and engineering settings, system dynamics are characterized by governing partial differential equations (PDEs), and a major challenge is to solve inverse problems (IPs) where unknown PDE parameters are inferred based on observational data gathered under limited budget.
Due to the high costs of setting ... | [
"Physics-Informed Neural Network",
"PINNs",
"Experimental Design",
"AI For Science",
"Active Learning",
"Data Selection"
] | An experimental design framework for PDE-based inverse problems that uses PINNs and its training dynamics, in a fully differentiable architecture to perform continuous optimization of design parameters. | 14,224 | 2503.07070 | title_snapshot | [
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AgentRefine: Enhancing Agent Generalization through Refinement Tuning | https://openreview.net/forum?id=FDimWzmcWn | [
"Dayuan Fu",
"Keqing He",
"Yejie Wang",
"Wentao Hong",
"Zhuoma GongQue",
"Weihao Zeng",
"Wei Wang",
"Jingang Wang",
"Xunliang Cai",
"Weiran Xu"
] | Poster | other topics in machine learning (i.e., none of the above) | Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial models like the GPT series. In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning. We f... | [
"agent",
"self-refine",
"diversity",
"generalization",
"data synthesis"
] | The self-refine data can expand the search space of LLM agent and improve the reason quality, leading a generalized performance in agent tasks. | 14,212 | 2501.01702 | title_snapshot | [
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TabM: Advancing tabular deep learning with parameter-efficient ensembling | https://openreview.net/forum?id=Sd4wYYOhmY | [
"Yury Gorishniy",
"Akim Kotelnikov",
"Artem Babenko"
] | Poster | other topics in machine learning (i.e., none of the above) | Deep learning architectures for supervised learning on tabular data range from simple multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented methods.
This study highlights a major, yet so far overlooked opportunity for substantially improving tabular MLPs; namely, parameter-efficient ensembl... | [
"tabular",
"tabular data",
"deep learning",
"architecture"
] | Parameter-efficient ensembling has a massive positive impact on tabular MLPs, and TabM is a new SOTA architecture illustrating that. | 14,197 | 2410.24210 | title_snapshot | [
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Multi-Label Test-Time Adaptation with Bound Entropy Minimization | https://openreview.net/forum?id=75PhjtbBdr | [
"Xiangyu Wu",
"Feng Yu",
"Yang Yang",
"Qing-Guo Chen",
"Jianfeng Lu"
] | Poster | unsupervised, self-supervised, semi-supervised, and supervised representation learning | Mainstream test-time adaptation (TTA) techniques endeavor to mitigate distribution shifts via entropy minimization for multi-class classification, inherently increasing the probability of the most confident class. However, when encountering multi-label instances, the primary challenge stems from the varying number of l... | [
"Vision-Language Models",
"Zero-Shot Multi-Label Generalization",
"Test-Time Adaptation"
] | A Multi-Label Test-Time Adaptation method with Bound Entropy Minimization objective. | 14,187 | 2502.03777 | title_snapshot | [
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ToolGen: Unified Tool Retrieval and Calling via Generation | https://openreview.net/forum?id=XLMAMmowdY | [
"Renxi Wang",
"Xudong Han",
"Lei Ji",
"Shu Wang",
"Timothy Baldwin",
"Haonan Li"
] | Poster | applications to computer vision, audio, language, and other modalities | As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often inefficient, retrieval... | [
"Agent",
"Tool Learning",
"Virtual Token"
] | Unified tool retrieval and calling by transforming tools into virtual tokens | 14,183 | 2410.03439 | title_snapshot | [
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Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks | https://openreview.net/forum?id=VNMJfBBUd5 | [
"Danni Yuan",
"Mingda Zhang",
"Shaokui Wei",
"Li Liu",
"Baoyuan Wu"
] | Poster | alignment, fairness, safety, privacy, and societal considerations | This work studies the task of poisoned sample detection for defending against data poisoning based backdoor attacks. Its core challenge is finding a generalizable and discriminative metric to distinguish between clean and various types of poisoned samples (e.g., various triggers, various poisoning ratios). Inspired by ... | [
"Backdoor Defense",
"Poisoned Sample Detection",
"AI security"
] | null | 14,155 | 2312.06230 | title_snapshot | [
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0... |
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