ICLR 2024 Conference Submissions

Is Pre-training Truly Better Than Meta-Learning?
Brando Miranda, Saumya Goyal, Patrick Yu, Yu-Xiong Wang, Sanmi Koyejo
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Deep Learning-based Discrimination of Pause Episodes in Insertable Cardiac Monitors
Elnaz Lashgari, Daniel Monje, Fady Dawoud, Fujian Qu
- 22 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Fast and Reliable Generation of EHR Time Series via Diffusion Models
Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, Anru Zhang
- 22 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Harmonic Prior Flow Matching for Multi-Ligand Docking and Binding Site Design
Hannes Stark, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data
Brando Miranda, Alycia Lee, Sudharsan Sundar, Sanmi Koyejo
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Junxiong Wang, Kaiwen Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Learning Conditional Policy for Crystal Design using Offline Reinforcement Learning
Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar
- 22 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Privileged Sensing Scaffolds Reinforcement Learning
Edward S. Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman
- Published: 16 Jan 2024, Last Modified: 16 Apr 2024
- ICLR 2024 spotlight
- Readers: Everyone
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, Kavita Bala
- Published: 16 Jan 2024, Last Modified: 12 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
LumiNet: The Bright Side of Perceptual Knowledge Distillation
Md. Ismail Hossain, M M Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian, Fuad Rahman, Nabeel Mohammed, Shafin Rahman
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
DEBOSH: Deep Bayesian Shape Optimization
Nikita Durasov, Artem Lukoianov, Jonathan Donier, Pascal Fua
- 22 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
Blaise Delattre, Alexandre Araujo, Quentin Barthélemy, Alexandre Allauzen
- Published: 16 Jan 2024, Last Modified: 13 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition
Zhongtian Chen, Edmund Lau, Jake Mendel, Susan Wei, Daniel Murfet
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Vincent Grari, Thibault Laugel, Tatsunori Hashimoto, sylvain lamprier, Marcin Detyniecki
- Published: 16 Jan 2024, Last Modified: 13 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models
Preston K Robinette, Daniel Moyer, Taylor T Johnson
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu, Shaohan Hu, Marco Pistoia, Tarek F. Abdelzaher
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar
- Published: 16 Jan 2024, Last Modified: 16 Apr 2024
- ICLR 2024 oral
- Readers: Everyone
Learning Planning Abstractions from Language
Weiyu Liu, Geng Chen, Joy Hsu, Jiayuan Mao, Jiajun Wu
- Published: 16 Jan 2024, Last Modified: 21 Apr 2024
- ICLR 2024 poster
- Readers: Everyone
Towards Neural Architecture Search through Hierarchical Generative Modeling
Lichuan Xiang, Łukasz Dudziak, Mohamed S Abdelfattah, Abhinav Mehrotra, Nicholas Donald Lane, Hongkai Wen
- 22 Sept 2023 (modified: 07 May 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Learning to Act without Actions
Dominik Schmidt, Minqi Jiang
- Published: 16 Jan 2024, Last Modified: 26 Mar 2024
- ICLR 2024 spotlight
- Readers: Everyone
Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation
Chen Dun, Mirian Del Carmen Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah, Anastasios Kyrillidis, Robert Sim
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Conditional Diffusion Distillation
Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M. Patel, Peyman Milanfar
- 22 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
FT-SHIELD: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
Yingqian Cui, Jie Ren, Yuping Lin, Han Xu, Pengfei He, Yue Xing, Wenqi Fan, Hui Liu, Jiliang Tang
- 22 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Tailoring Self-Rationalizers with Multi-Reward Distillation
Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, Xiang Ren
- Published: 16 Jan 2024, Last Modified: 15 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Building Cooperative Embodied Agents Modularly with Large Language Models
Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan
- Published: 16 Jan 2024, Last Modified: 19 Apr 2024
- ICLR 2024 poster
- Readers: Everyone