ICLR 2024

2,260 papers

RingAttention with Blockwise Transformers for Near-Infinite Context

Hao Liu, Matei Zaharia, Pieter Abbeel

In-Context Learning through the Bayesian Prism

Madhur Panwar, Kabir Ahuja, Navin Goyal

Fair and Efficient Contribution Valuation for Vertical Federated Learning

Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Lifan Zhao, Yanyan Shen

Proving Test Set Contamination in Black-Box Language Models

Yonatan Oren, Nicole Meister, Niladri S. Chatterji, Faisal Ladhak, Tatsunori Hashimoto

Multimarginal Generative Modeling with Stochastic Interpolants

Michael Samuel Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob

In-context Autoencoder for Context Compression in a Large Language Model

Tao Ge, Hu Jing, Lei Wang, Xun Wang, Si-Qing Chen

Bayesian Coreset Optimization for Personalized Federated Learning

Prateek Chanda, Shrey Modi, Ganesh Ramakrishnan

DDMI: Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

Dogyun Park, Sihyeon Kim, Sojin Lee, Hyunwoo J. Kim

Manipulating dropout reveals an optimal balance of efficiency and robustness in biological and machine visual systems

Jacob S. Prince, Gabriel Fajardo, George A. Alvarez, Talia Konkle

Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching

Ganesh Ramachandra Kini, Vala Vakilian, Tina Behnia, Jaidev Gill, CHRISTOS THRAMPOULIDIS

Debiased Collaborative Filtering with Kernel-Based Causal Balancing

Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng

Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Niels Mündler, Jingxuan He, Slobodan Jenko, Martin Vechev

Partitioning Message Passing for Graph Fraud Detection

Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan

Dynamics-Informed Protein Design with Structure Conditioning

Urszula Julia Komorowska, Simon V Mathis, Kieran Didi, Francisco Vargas, Pietro Lió

Learning to Reject with a Fixed Predictor: Application to Decontextualization

Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao

Generalization error of spectral algorithms

Maksim Velikanov, Maxim Panov, Dmitry Yarotsky

Robust Classification via Regression for Learning with Noisy Labels

Erik Englesson, Hossein Azizpour

Social-Transmotion: Promptable Human Trajectory Prediction

Saeed Saadatnejad, Yang Gao, Kaouther Messaoud, Alexandre Alahi

LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition

Lingfeng Liu, Dong Ni, Hangjie Yuan

Learning From Simplicial Data Based on Random Walks and 1D Convolutions

Florian Frantzen, Michael T Schaub

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

Hyunwook Lee, Sungahn Ko

Graph Parsing Networks

Yunchong Song, Siyuan Huang, Xinbing Wang, Chenghu Zhou, Zhouhan Lin

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Jifan Yu, Xiaozhi Wang, Shangqing Tu, Shulin Cao, Daniel Zhang-li

An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks

Dongwon Son, Jaehyung Kim, Sanghyeon Son, Beomjoon Kim

CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects

Yoonyoung Cho, Junhyek Han, Yoontae Cho, Beomjoon Kim

Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery

Linan Yue, Qi Liu, Yichao Du, Li Wang, Weibo Gao

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen

Gradual Optimization Learning for Conformational Energy Minimization

Artem Tsypin, Leonid Anatolievich Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev

Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability

Zehao Dong, Muhan Zhang, Philip Payne, Michael A Province, Carlos Cruchaga

t^3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence

Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, Joong-Ho Won

Generalized Policy Iteration using Tensor Approximation for Hybrid Control

Suhan Shetty, Teng Xue, Sylvain Calinon

Multilingual Jailbreak Challenges in Large Language Models

Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing

Demonstration-Regularized RL

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems

Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki

Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching

Aleksandar Makelov, Georg Lange, Atticus Geiger, Neel Nanda

Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction

Anirudh Buvanesh, Rahul Chand, Jatin Prakash, Bhawna Paliwal, Mudit Dhawan

Bellman Optimal Stepsize Straightening of Flow-Matching Models

Bao Nguyen, Binh Nguyen, Viet Anh Nguyen

Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision

Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony

On the Learnability of Watermarks for Language Models

Chenchen Gu, Xiang Lisa Li, Percy Liang, Tatsunori Hashimoto

Mirage: Model-agnostic Graph Distillation for Graph Classification

Mridul Gupta, Sahil Manchanda, HARIPRASAD KODAMANA, Sayan Ranu

Test-time Adaptation against Multi-modal Reliability Bias

Mouxing Yang, Yunfan Li, Changqing Zhang, Peng Hu, Xi Peng

Dissecting learning and forgetting in language model finetuning

Xiao Zhang, Ji Wu

Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks

Kesen Zhao, Liang Zhang

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, Junxian He

Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning

Yeongwoo Song, Hawoong Jeong

Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization

Ravi Francesco Srinivasan, Francesca Mignacco, Martino Sorbaro, Maria Refinetti, Avi Cooper

Controlled Text Generation via Language Model Arithmetic

Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin Vechev

Learning model uncertainty as variance-minimizing instance weights

Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy

Rethinking and Extending the Probabilistic Inference Capacity of GNNs

Tuo Xu, Lei Zou

Generative Judge for Evaluating Alignment

Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao

Bridging Vision and Language Spaces with Assignment Prediction

JungIn Park, Jiyoung Lee, Kwanghoon Sohn

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations

Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao

Locality Sensitive Sparse Encoding for Learning World Models Online

Zichen Liu, Chao Du, Wee Sun Lee, Min Lin

Beyond Memorization: Violating Privacy via Inference with Large Language Models

Robin Staab, Mark Vero, Mislav Balunovic, Martin Vechev

Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models

Sijia Chen, Baochun Li, Di Niu

SaNN: Simple Yet Powerful Simplicial-aware Neural Networks

Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri

TabR: Tabular Deep Learning Meets Nearest Neighbors

Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii, Akim Kotelnikov