ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
title stringlengths 15 163 | paper_url stringlengths 42 42 | authors listlengths 1 40 | type stringclasses 3
values | primary_area stringclasses 84
values | abstract large_stringlengths 393 2.6k | keywords listlengths 1 20 | TL;DR large_stringlengths 7 250 ⌀ | submission_number int64 1 16.4k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|
UnHiPPO: Uncertainty-aware Initialization for State Space Models | https://openreview.net/forum?id=U8GUmxnzXn | [
"Marten Lienen",
"Abdullah Saydemir",
"Stephan Günnemann"
] | Poster | deep_learning->sequential_models_time_series | State space models are emerging as a dominant model class for sequence problems with many relying on the HiPPO framework to initialize their dynamics. However, HiPPO fundamentally assumes data to be noise-free; an assumption often violated in practice. We extend the HiPPO theory with measurement noise and derive an unc... | [
"state space",
"uncertainty",
"hippo",
"mamba",
"kalman",
"noise",
"filter"
] | HiPPO extension based on linear stochastic control theory and the Kalman filter making SSMs more robust against noise | 16,431 | 2506.05065 | title_snapshot | [
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When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series | https://openreview.net/forum?id=Dqp6IMI3gQ | [
"Min-Yeong Park",
"Won-Jeong Lee",
"Seong Tae Kim",
"Gyeong-Moon Park"
] | Poster | deep_learning->sequential_models_time_series | Recently, forecasting future abnormal events has emerged as an important scenario to tackle realworld necessities. However, the solution of predicting specific future time points when anomalies will occur, known as Anomaly Prediction (AP), remains under-explored. Existing methods dealing with time series data fail in A... | [
"Time series forecasting",
"time series anomaly detection"
] | A2P: See anomalies before they strike! | 16,430 | 2506.23596 | title_snapshot | [
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KGMark: A Diffusion Watermark for Knowledge Graphs | https://openreview.net/forum?id=GKZySvM2t9 | [
"Hongrui Peng",
"Haolang Lu",
"Yuanlong Yu",
"WeiYe Fu",
"Kun Wang",
"Guoshun Nan"
] | Poster | social_aspects->fairness | Knowledge graphs (KGs) are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be applied to dynamic gra... | [
"Watermarking",
"Knowledge Graph",
"Diffusion Models",
"Generative Models"
] | We present KGMark, the first watermarking method for knowledge graph embeddings that ensures high detectability, transparency, and robustness across various graph modifications. | 16,409 | 2505.23873 | title_snapshot | [
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... |
LSCD: Lomb--Scargle Conditioned Diffusion for Time series Imputation | https://openreview.net/forum?id=GdYg0Ohx0k | [
"Elizabeth Fons",
"Alejandro Sztrajman",
"Yousef El-Laham",
"Luciana Ferrer",
"Svitlana Vyetrenko",
"Manuela Veloso"
] | Poster | deep_learning->sequential_models_time_series | Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform sampling, therefore requiring prior interpolation that can distort the spectra. To address this limitation,... | [
"time series",
"diffusion models",
"frequency spectrum"
] | We propose Lomb–Scargle Conditioned Diffusion (LSCD), a diffusion-based time series imputation method that leverages a differentiable Lomb–Scargle periodogram to handle irregular sampling and preserve spectral consistency | 16,408 | 2506.17039 | title_snapshot | [
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Emoji Attack: Enhancing Jailbreak Attacks Against Judge LLM Detection | https://openreview.net/forum?id=Q0rKYiVEZq | [
"Zhipeng Wei",
"Yuqi Liu",
"N. Benjamin Erichson"
] | Poster | social_aspects->safety | Jailbreaking techniques trick Large Language Models (LLMs) into producing restricted output, posing a potential threat. One line of defense is to use another LLM as a Judge to evaluate the harmfulness of generated text. However, we reveal that these Judge LLMs are vulnerable to token segmentation bias, an issue that ar... | [
"LLM safety; Jailbreaking Attacks; Judge LLMs; Token Segmentation"
] | We introduce Emoji Attack, an adversarial strategy that exploits token segmentation bias in Judge LLMs by inserting emojis to manipulate tokenization, enhancing the effectiveness of jailbreak attacks against Judge LLM detection. | 16,371 | 2411.01077 | title_snapshot | [
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Towards Practical Defect-Focused Automated Code Review | https://openreview.net/forum?id=mEV0nvHcK3 | [
"Junyi Lu",
"Lili Jiang",
"Xiaojia Li",
"Jianbing Fang",
"Fengjun Zhang",
"Li Yang",
"Chun Zuo"
] | Spotlight | applications | The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation and relying on text similarity metrics like BLEU for evaluation. These methods overlook repository context, real-world merge request evaluati... | [
"Automated Code Review",
"Merge Request Analysis",
"Large Language Models (LLMs)",
"Defect Detection",
"Evaluation Metrics for Code Review",
"Code Context Extraction",
"Multi-Agent LLM Collaboration"
] | This work presents an end-to-end approach to automated code review that goes beyond snippet-level generation and text-similarity metrics, achieving significant gains over existing baselines in real-world, industry-scale codebases. | 16,368 | 2505.17928 | title_snapshot | [
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... |
HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks | https://openreview.net/forum?id=rnx11J4hsg | [
"Julia Gusak",
"Xunyi Zhao",
"Théotime Le Hellard",
"Zhe LI",
"Lionel Eyraud-Dubois",
"Olivier Beaumont"
] | Poster | general_machine_learning->hardware_and_software | Training deep neural networks (DNNs) on memory-limited GPUs is challenging, as storing intermediate activations often exceeds available memory. Re-materialization, a technique that preserves exact computations, addresses this by selectively recomputing activations instead of storing them. However, existing methods eit... | [
"Rematerialization",
"Checkpointing",
"Memory-Efficient Training",
"Neural Networks",
"PyTorch",
"Integer Linear Programming",
"Training"
] | null | 16,364 | null | null | [
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-0.05117946118116379,
... |
ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset | https://openreview.net/forum?id=GByP03IitA | [
"Yilin wang",
"Peixuan Lei",
"Jie Song",
"Yuzhe Hao",
"Tao Chen",
"Yuxuan Zhang",
"LEI JIA",
"Yuanxiang Li",
"zhongyu wei"
] | Poster | applications->time_series | Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research. However, effectively integrating these high-dimensional temporal signals with natural language for dynamic, interactive tasks remains a significant challenge. To address this, we introduce th... | [
"Time Series Analysis",
"Time-Series Question Answering",
"Time-Series-Textual Alignment",
"Time-Series-Textual Fusion"
] | Bridging time-series data and natural language, we propose ITFormer and introduce EngineMT-QA, enabling efficient and accurate Time-Series Question Answering for multimodal AI | 16,325 | 2506.20093 | title_snapshot | [
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-0.02503211051... |
GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning | https://openreview.net/forum?id=7QFmZ7i7sr | [
"Nannan Wu",
"Yuming Huang",
"Yiming Zhao",
"Jie Chen",
"Wenjun Wang"
] | Poster | deep_learning->graph_neural_networks | Subgraph representation learning has attracted growing interest due to its wide applications in various domains. However, existing methods primarily focus on local neighborhood structures while overlooking the significant impact of global structural information, in particular the influence of multi-hop neighbors beyond... | [
"Subgraph Representation Learning"
] | null | 16,318 | null | null | [
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0.00... |
Fast Min-$\epsilon$ Segmented Regression using Constant-Time Segment Merging | https://openreview.net/forum?id=w2QNIkcwWw | [
"Ansgar Lößer",
"Max Schlecht",
"Florian Schintke",
"Joel Witzke",
"Matthias Weidlich",
"Björn Scheuermann"
] | Poster | general_machine_learning | Segmented regression is a statistical method that approximates a function $f$ by a piecewise function $\hat{f}$ using noisy data samples.
*Min-$\epsilon$* approaches aim to reduce the regression function's mean squared error (MSE) for a given number of $k$ segments.
An optimal solution for *min-$\epsilon$* segmented re... | [
"Regression",
"Segmented Regression",
"Time-Series Analysis"
] | null | 16,316 | null | null | [
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-0.028475690633058548,
-0.00989009439945221,
-0.06543733179569244,
... |
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