ICML

Why Would You Suggest That? Human Trust in Language Model Responses

Manasi Sharma, Ho Chit Siu, Rohan Paleja, Jaime D. Peña

Journal ref ICML Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact Workshop 2024

Forecasting Smog Clouds With Deep Learning

Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro

Journal ref Oldenburg, V., Cardenas-Cartagena, J., & Valdenegro-Toro, M. (2024). Forecasting smog clouds with deep learning: A proof-of-concept. In ICML 2024 AI for Science Workshop. https://openreview.net/forum?id=UQa2PEVHMF

Robustness of Explainable Artificial Intelligence in Industrial Process Modelling

Benedikt Kantz, Clemens Staudinger, Christoph Feilmayr, Johannes Wachlmayr, Alexander Haberl, Stefan Schuster, Franz Pernkopf

Comments 11 pages, 3 figures, accepted at the ICML'24 Workshop ML4LMS; updated with improved results and fixed typos

Squeeze-and-Remember Block

Rinor Cakaj, Jens Mehnert, Bin Yang

Comments Accepted by The International Conference on Machine Learning and Applications (ICMLA) 2024

TaskComplexity: A Dataset for Task Complexity Classification with In-Context Learning, FLAN-T5 and GPT-4o Benchmarks

Areeg Fahad Rasheed, M. Zarkoosh, Safa F. Abbas, Sana Sabah Al-Azzawi

Comments This papaer has been accepted to The 3nd International conference on Machine Learning and Data Engineering (ICMLDE 2024)

From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation

Kun Su, Xiulong Liu, Eli Shlizerman

Comments Accepted by ICML 2024

Spectral Wavelet Dropout: Regularization in the Wavelet Domain

Rinor Cakaj, Jens Mehnert, Bin Yang

Comments Accepted by The International Conference on Machine Learning and Applications (ICMLA) 2024

CodonMPNN for Organism Specific and Codon Optimal Inverse Folding

Hannes Stark, Umesh Padia, Julia Balla, Cameron Diao, George Church

Comments Appeared at the 2024 ICML AI4Science workshop

Task Addition in Multi-Task Learning by Geometrical Alignment

Soorin Yim, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Chanhui Lee, Sehui Han

Comments 11 pages, 5 figures, Accepted at AI for Science Workshop at 41st International Conference on Machine Learning

Understanding the Cognitive Complexity in Language Elicited by Product Images

Yan-Ying Chen, Shabnam Hakimi, Monica Van, Francine Chen, Matthew Hong, Matt Klenk, Charlene Wu

Journal ref Published by ICML 2024 Workshop on LLMs and Cognition

A Differentiable Approach to Multi-scale Brain Modeling

Chaoming Wang, Muyang Lyu, Tianqiu Zhang, Sichao He, Si Wu

Comments 2nd Differentiable Almost Everything Workshop at ICML 2024. https://github.com/chaoming0625/differentiable-brain-modeling-workflow

Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion

Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue

Comments ICML 2024 (Oral)

Incentivizing Exploration with Linear Contexts and Combinatorial Actions

Mark Sellke

Comments International Conference on Machine Learning (ICML) 2023

The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition

Oline Ranum, David R. Wessels, Gomer Otterspeer, Erik J. Bekkers, Floris Roelofsen, Jari I. Andersen

Comments Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 251, 2024

Protein Conformation Generation via Force-Guided SE(3) Diffusion Models

Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu

Comments ICML 2024

On Computing Optimal Tree Ensembles

Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge

Comments Accepted at ICML 2023

Behavioral Bias of Vision-Language Models: A Behavioral Finance View

Yuhang Xiao, Yudi Lin, Ming-Chang Chiu

Comments ICML 2024 Workshop on Large Language Models and Cognition

Learning the Target Network in Function Space

Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor

Comments Accepted to International Conference on Machine Learning (ICML24)

Stochastic interpolants with data-dependent couplings

Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden

Comments ICML 2024

Taylorformer: Probabilistic Modelling for Random Processes including Time Series

Omer Nivron, Raghul Parthipan, Damon J. Wischik

Comments Presented at ICML 2023, New Frontiers in Learning, Control, and Dynamical Systems Workshop

Learning Recourse Costs from Pairwise Feature Comparisons

Kaivalya Rawal, Himabindu Lakkaraju

Comments "Recourse for Humans", paper 49 from the Participatory Approaches to Machine Learning workshop at the International Conference on Machine Learning (ICML) 2020. For workshop website, see https://participatoryml.github.io/#49

TracrBench: Generating Interpretability Testbeds with Large Language Models

Hannes Thurnherr, Jérémy Scheurer

Comments 6 pages + appendix, 4 figures, ICML Mechanistic Interpretability Workshop

How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?

Ke Sun, Bei Jiang, Linglong Kong

Comments ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and Theorists (ARLET 2024)

Optimizing DDPM Sampling with Shortcut Fine-Tuning

Ying Fan, Kangwook Lee

Comments ICML 2023

Deep generative models as an adversarial attack strategy for tabular machine learning

Salijona Dyrmishi, Mihaela Cătălina Stoian, Eleonora Giunchiglia, Maxime Cordy

Comments Accepted at ICMLC 2024 (International Conference on Machine Learning and Cybernetics)

Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation

Ossi Räisä, Joonas Jälkö, Antti Honkela

Comments After the publication of this work (ICML 2024), the Conjecture 6.3 has been proven by Kalinin (2024). Kalinin, N. P. Notes on Sampled Gaussian Mechanism. arXiv:2409.04636, 2024

Deep Stochastic Mechanics

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

Comments ICML 2024

Journal ref Proceedings of the 41st International Conference on Machine Learning, 235, 2024, 38779-38814; https://proceedings.mlr.press/v235/orlova24a.html

Teaching dark matter simulations to speak the halo language

Shivam Pandey, Francois Lanusse, Chirag Modi, Benjamin D. Wandelt

Comments 6 pages, 2 figures. Accepted by the Structured Probabilistic Inference & Generative Modeling workshop of ICML 2024

An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems

Hitesh Tulsiani, David M. Chan, Shalini Ghosh, Garima Lalwani, Prabhat Pandey, Ankish Bansal, Sri Garimella, Ariya Rastrow, Björn Hoffmeister

Comments Presented at ICML 2024

Privately Learning Smooth Distributions on the Hypercube by Projections

Clément Lalanne, Sébastien Gadat

Journal ref ICML 2024 - 41st International Conference on Machine Learning, Jul 2024, Vienna, Austria. 39 p