ICLR

Privacy-preserving Fine-tuning of Large Language Models through Flatness

Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Comments Accepted to ICLR 2024 SeT LLM Workshop

Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations

Xiaolin Sun, Zizhan Zheng

Comments ICLR 2024

Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning

Hyungho Na, Yunkyeong Seo, Il-chul Moon

Comments Accepted at ICLR 2024

On Trojan Signatures in Large Language Models of Code

Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour

Comments This work has been accepted at the International Conference on Learning Representations 2024 Workshop on Secure and Trustworthy Large Language Models, SeT LLM @ ICLR 2024 (Vienna, Austria)

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations

Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato

Comments ICLR 2024 camera-ready version; 27 pages, 17 figures

Unsupervised Pretraining for Fact Verification by Language Model Distillation

Adrián Bazaga, Pietro Liò, Gos Micklem

Comments ICLR 2024 Camera Ready

Polynomial Width is Sufficient for Set Representation with High-dimensional Features

Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang, Pan Li

Comments ICLR 2024

Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework

Eliya Segev, Maya Alroy, Ronen Katsir, Noam Wies, Ayana Shenhav, Yael Ben-Oren, David Zar, Oren Tadmor, Jacob Bitterman, Amnon Shashua, Tal Rosenwein

Comments ICLR 2024

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Yi-Lun Liao, Brandon Wood, Abhishek Das, Tess Smidt

Comments Published as a conference paper at ICLR 2024

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi

Comments accepted in part to ICLR 2024

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, Bryan Hooi

Comments In Proceedings of ICLR 2024

FOSI: Hybrid First and Second Order Optimization

Hadar Sivan, Moshe Gabel, Assaf Schuster

Comments 23 pages, 9 figures. Accepted as a conference paper to ICLR 2024

Verified Training for Counterfactual Explanation Robustness under Data Shift

Anna P. Meyer, Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni

Comments 16 pages, 2 figures. Accepted at DMLR workshop at ICLR 2024

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs

Victor Akinwande, J. Zico Kolter

Comments Accepted at MLGenX @ ICLR 2024. Open source at https://github.com/Viktour19/culingam

SUPClust: Active Learning at the Boundaries

Yuta Ono, Till Aczel, Benjamin Estermann, Roger Wattenhofer

Comments Accepted at ICLR 2024 Workshop on Practical Machine Learning for Low Resource Settings (PML4LRS)

Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN

Biswadeep Chakraborty, Beomseok Kang, Harshit Kumar, Saibal Mukhopadhyay

Comments Published as a conference paper at ICLR 2024

Journal ref ICLR 2024

Learning with Language-Guided State Abstractions

Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah

Comments ICLR 2024

Large-scale Training of Foundation Models for Wearable Biosignals

Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller, Saba Emrani, Udhyakumar Nallasamy, Ian Shapiro

Comments Camera ready version for ICLR 2024

GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri, Oier Lopez de Lacalle, German Rigau, Eneko Agirre

Comments The Twelfth International Conference on Learning Representations - ICLR 2024

Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models

Navid Rajabi, Jana Kosecka

Comments Accepted to DMLR @ ICLR 2024

VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs

Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec

Comments ICLR 2024. Code: https://github.com/YangLing0818/VQGraph

What's In My Big Data?

Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, Jesse Dodge

Comments Published at ICLR 2024 spotlight

Scalable Neural Network Kernels

Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov

Comments ICLR 2024

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning

HeeSun Bae, Seungjae Shin, Byeonghu Na, Il-Chul Moon

Comments 35 pages, 20 figures, Accepted to the twelfth International Conference on Learninig Representations (ICLR 24)

TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts

Hyunwook Lee, Sungahn Ko

Comments 19 pages, 7 figures, Accepted as poster to ICLR 2024. Code: https://github.com/HyunWookL/TESTAM

Journal ref International Conference on Learning Representations (ICLR 2024)

Fast hyperboloid decision tree algorithms

Philippe Chlenski, Ethan Turok, Antonio Moretti, Itsik Pe'er

Journal ref International Conference on Learning Representations (2024)

Identifying Representations for Intervention Extrapolation

Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters

Comments Accepted at the International Conference on Learning Representations (ICLR) 2024

Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition

Faisal Hamman, Sanghamitra Dutta

Comments Published at the International Conference on Learning Representations (ICLR) 2024

Input-gradient space particle inference for neural network ensembles

Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski

Comments Published at ICLR 2024 (spotlight presentation). Code is available at https://github.com/AaltoPML/FoRDE

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Richards

Comments ICLR 2024

Journal ref The Twelfth International Conference on Learning Representations, 2024