ICML

NSP: A Neuro-Symbolic Natural Language Navigational Planner

William English, Dominic Simon, Sumit Jha, Rickard Ewetz

Comments 10 pages, Preprint of paper accepted at 23rd International Conference on Machine Learning and Applications (ICMLA) 2024

Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency

Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu

Comments Accepted by ICML 2024

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features

Aleksandr Beznosikov, David Dobre, Gauthier Gidel

Comments Appears in: the 41st International Conference on Machine Learning (ICML 2024). 26 pages, 2 algorithms, 5 figures, 2 tables. Reference: https://proceedings.mlr.press/v235/beznosikov24a.html

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl

Comments ICML 2023, Project Webpage: https://energy-based-model.github.io/reduce-reuse-recycle/

Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds

Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour

Comments Intl. Conference on Machine Learning (ICML), 2024

Adaptive Sampling for Continuous Group Equivariant Neural Networks

Berfin Inal, Gabriele Cesa

Comments 9 pages, published in the Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop at ICML 2024

A call for embodied AI

Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl

Comments Published in ICML 2024 Position paper track

Journal ref PMLR 235:39493-39508, 2024

Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice

Jonathan Li, Rohan Bhambhoria, Samuel Dahan, Xiaodan Zhu

Comments Accepted into GenLaw '24 (ICML 2024 workshop)

Extreme Compression of Large Language Models via Additive Quantization

Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh

Comments ICML, 2024

Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records

Zoe Hancox, Sarah R. Kingsbury, Andrew Clegg, Philip G. Conaghan, Samuel D. Relton

Comments Accepted to the 2024 International Conference on Machine Learning and Applications (ICMLA). 8 pages, 3 figures, 7 tables

Learning Multiple Secrets in Mastermind

Milind Prabhu, David Woodruff

Comments This work appeared at ICML 2024

Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations

Pablo Rivas, Gisela Bichler, Tomas Cerny, Laurie Giddens, Stacie Petter

Comments 2022 LXAI Workshop at the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data

Shunxing Fan, Mingming Gong, Kun Zhang

Comments ICML 2024

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Journal ref Proceedings of the 41st International Conference on Machine Learning (ICML), 2024, pp. 22624--22642

Optimal Differentially Private Model Training with Public Data

Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

Comments ICML 2024

TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors

Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang

Journal ref International Conference on Machine Learning 2024

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol-Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree Vadgama, Erik Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matouš Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovač, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Mežnar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frederic Wantiez, Alexander Weers, Patrik Zajec, Blaž Škrlj, Nina Miolane

Comments Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024

Centralized Selection with Preferences in the Presence of Biases

L. Elisa Celis, Amit Kumar, Nisheeth K. Vishnoi, Andrew Xu

Comments The conference version of this paper appears in ICML 2024

Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses

Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright

Comments The 41st International Conference on Machine Learning (ICML 2024)

Cooperative Minibatching in Graph Neural Networks

Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek

Comments Under submission. Was first proposed in Aug 2022 in https://github.com/dmlc/dgl/pull/4337 and submitted to ICML in Jan 2023

Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation

Heath Arthur-Loui, Amina Mollaysa, Michael Krauthammer

Journal ref Proceedings of the ICML 2024 Workshop on Accessible and Effi- cient Foundation Models for Biological Discovery

Multiply-Robust Causal Change Attribution

Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman

Journal ref Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization

Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak

Comments International Conference on Machine Learning 2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

Journal ref Proceedings of the 41st International Conference on Machine Learning, PMLR 235:1730-1762, 2024

Spectral Phase Transition and Optimal PCA in Block-Structured Spiked models

Pierre Mergny, Justin Ko, Florent Krzakala

Comments 26 pages, 2 figures

Journal ref Proceedings of the 41st International Conference on Machine Learning, PMLR 235:35470-35491, 2024

Asymptotics of feature learning in two-layer networks after one gradient-step

Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro

Journal ref Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9662-9695, 2024

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová, Florent Krzakala

Comments Accepted at the International Conference on Machine Learning (ICML), 2024

Journal ref Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9991-10016, 2024

Understanding the Role of Functional Diversity in Weight-Ensembling with Ingredient Selection and Multidimensional Scaling

Alex Rojas, David Alvarez-Melis

Comments Published at the ICML 2024 (Vienna, Austria) Workshop on Foundation Models in the Wild

Self-Supervised Learning for Identifying Defects in Sewer Footage

Daniel Otero, Rafael Mateus

Comments Poster at the LatinX in AI Workshop @ ICML 2024

Revisiting Character-level Adversarial Attacks for Language Models

Elias Abad Rocamora, Yongtao Wu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher

Comments Accepted in ICML 2024