ICLR

Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training

Ernie Chang, David Ifeoluwa Adelani, Xiaoyu Shen, Vera Demberg

Comments Accepted to Workshop at ICLR 2020

Towards Sustainable Census Independent Population Estimation in Mozambique

Isaac Neal, Sohan Seth, Gary Watmough, Mamadou Saliou Diallo

Comments 5 pages, 2 figures, published in the AI for Public Health Workshop, ICLR 2021

CompOFA: Compound Once-For-All Networks for Faster Multi-Platform Deployment

Manas Sahni, Shreya Varshini, Alind Khare, Alexey Tumanov

Comments Published as a conference paper at ICLR 2021

PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches

Chong Xiang, Prateek Mittal

Comments ICLR 2021 Workshop on Security and Safety in Machine Learning Systems

Generative modeling of spatio-temporal weather patterns with extreme event conditioning

Konstantin Klemmer, Sudipan Saha, Matthias Kahl, Tianlin Xu, Xiao Xiang Zhu

Comments ICLR'21 Workshop AI: Modeling Oceans and Climate Change (AIMOCC)

Weakly Supervised Multi-task Learning for Concept-based Explainability

Catarina Belém, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro

Comments Accepted at ICLR 2021 Workshop on Weakly Supervised Learning (WeaSuL)

DC3: A learning method for optimization with hard constraints

Priya L. Donti, David Rolnick, J. Zico Kolter

Comments In ICLR 2021. Code available at https://github.com/locuslab/DC3

Journal ref International Conference on Learning Representations 2021

Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration

Jivat Neet Kaur, Yiding Jiang, Paul Pu Liang

Comments Accepted at ICLR 2021 Workshop on Embodied Multimodal Learning

Backdoor Attack in the Physical World

Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, Shu-Tao Xia

Comments This work was done when Yiming Li was an intern at Tencent AI Lab, supported by the Tencent Rhino-Bird Elite Training Program (2020). This is a 6-pages short version of our ongoing work, `Rethinking the Trigger of Backdoor Attack' (arXiv:2004.04692). It is accepted by the non-archival ICLR 2021 workshop on Robust and Reliable Machine Learning in the Real World. arXiv admin note: substantial text overlap with arXiv:2004.04692

Enforcing robust control guarantees within neural network policies

Priya L. Donti, Melrose Roderick, Mahyar Fazlyab, J. Zico Kolter

Comments Code available online: https://github.com/locuslab/robust-nn-control

Journal ref International Conference on Learning Representations 2021

Conservative Safety Critics for Exploration

Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg

Comments Published as a conference paper in ICLR 2021

Learning the Pareto Front with Hypernetworks

Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya

Comments Accepted to ICLR 2021

Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification

Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney

Comments Published as a conference paper at ICLR 2021

Lipschitz Recurrent Neural Networks

N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

Comments Published as a conference paper at ICLR 2021

Ensembles of GANs for synthetic training data generation

Gabriel Eilertsen, Apostolia Tsirikoglou, Claes Lundström, Jonas Unger

Comments ICLR 2021 workshop on Synthetic Data Generation: Quality, Privacy, Bias

More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data

Saul Calderon-Ramirez, Luis Oala

Comments Presented as a RobustML workshop paper at ICLR 2021. Both authors contributed equally. This article extends arXiv:2006.07767

Wandering Within a World: Online Contextualized Few-Shot Learning

Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel

Comments ICLR 2021

Dataset Inference: Ownership Resolution in Machine Learning

Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

Comments Published as a conference paper at ICLR 2021 (Spotlight Presentation)

EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss

Zhisheng Xiao, Qing Yan, Yali Amit

Comments EBM Wrokshop at ICLR 2021

Fast Graph Learning with Unique Optimal Solutions

Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

Journal ref ICLR 2021 Workshop on Geometrical and Topological Representation Learning

Making Differentiable Architecture Search less local

Erik Bodin, Federico Tomasi, Zhenwen Dai

Comments ICLR 2021 Workshop on Neural Architecture Search

Gradient Masked Federated Optimization

Irene Tenison, Sreya Francis, Irina Rish

Journal ref ICLR 2021 Distributed and Private Machine Learning(DPML) Workshop

Practical Defences Against Model Inversion Attacks for Split Neural Networks

Tom Titcombe, Adam J. Hall, Pavlos Papadopoulos, Daniele Romanini

Comments ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021)

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Chao Shang, Jie Chen, Jinbo Bi

Comments ICLR 2021. Code is available at https://github.com/chaoshangcs/GTS

Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering

Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler

Comments Accepted to ICLR 2021 as an Oral paper

Neural Networks for Learning Counterfactual G-Invariances from Single Environments

S Chandra Mouli, Bruno Ribeiro

Comments ICLR 2021

Does enhanced shape bias improve neural network robustness to common corruptions?

Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer, Jan Hendrik Metzen

Comments 20 pages, 9 figures, 12 tables, accepted at ICLR 2021

Representing Partial Programs with Blended Abstract Semantics

Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

Comments ICLR 2021

C-Learning: Learning to Achieve Goals via Recursive Classification

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Comments Accepted at ICLR 2021. Project website with videos (https://ben-eysenbach.github.io/c_learning/) and code (https://github.com/google-research/google-research/tree/master/c_learning) are online

Meta-Learning Deep Energy-Based Memory Models

Sergey Bartunov, Jack W Rae, Simon Osindero, Timothy P Lillicrap

Comments ICLR 2020