AAAI 2024

2,864 papers

No Internal Regret with Non-convex Loss Functions

Dravyansh Sharma

Phoneme Hallucinator: One-Shot Voice Conversion via Set Expansion

Siyuan Shan, Yang Li, Amartya Banerjee, Junier B. Oliva

Leaving the Nest: Going beyond Local Loss Functions for Predict-Then-Optimize

Sanket Shah, Bryan Wilder, Andrew Perrault, Milind Tambe

What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning

Caleb Schultz Kisby, Saúl A. Blanco, Lawrence S. Moss

Understanding and Leveraging the Learning Phases of Neural Networks

Johannes Schneider, Mohit Prabhushankar

XKD: Cross-Modal Knowledge Distillation with Domain Alignment for Video Representation Learning

Pritam Sarkar, Ali Etemad

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

Augusto Santos, Diogo Rente, Rui Seabra, José M. F. Moura

SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation

Hyun Ryu, Sunjae Yoon, Hee Suk Yoon, Eunseop Yoon, Chang D. Yoo

Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data

Shuvendu Roy, Ali Etemad

Limitations of Face Image Generation

Harrison Rosenberg, Shimaa Ahmed, Guruprasad Ramesh, Kassem Fawaz, Ramya Korlakai Vinayak

Protect Your Score: Contact-Tracing with Differential Privacy Guarantees

Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

Statistical Spatially Inhomogeneous Diffusion Inference

Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff

Towards Improved Proxy-Based Deep Metric Learning via Data-Augmented Domain Adaptation

Li Ren, Chen Chen, Liqiang Wang, Kien Hua

Towards Learning and Explaining Indirect Causal Effects in Neural Networks

Abbavaram Gowtham Reddy, Saketh Bachu, Harsharaj Pathak, Benin Godfrey L, Varshaneya V

NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation

Abbavaram Gowtham Reddy, Vineeth N Balasubramanian

Using Stratified Sampling to Improve LIME Image Explanations

Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari, Damiano Verda

GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

Nisal Ranasinghe, Damith Senanayake, Sachith Seneviratne, Malin Premaratne, Saman Halgamuge

Understanding the Generalization of Pretrained Diffusion Models on Out-of-Distribution Data

Sai Niranjan Ramachandran, Rudrabha Mukhopadhyay, Madhav Agarwal, C.V. Jawahar, Vinay Namboodiri

Fair Participation via Sequential Policies

Reilly Raab, Ross Boczar, Maryam Fazel, Yang Liu

DSD²: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?

Victor Quétu, Enzo Tartaglione

Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks

Xiaofan Que, Qi Yu

LDS2AE: Local Diffusion Shared-Specific Autoencoder for Multimodal Remote Sensing Image Classification with Arbitrary Missing Modalities

Jiahui Qu, Yuanbo Yang, Wenqian Dong, Yufei Yang

Learning the Topology and Behavior of Discrete Dynamical Systems

Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz

Towards Multi-Mode Outlier Robust Tensor Ring Decomposition

Yuning Qiu, Guoxu Zhou, Andong Wang, Zhenhao Huang, Qibin Zhao

Integer Is Enough: When Vertical Federated Learning Meets Rounding

Pengyu Qiu, Yuwen Pu, Yongchao Liu, Wenyan Liu, Yun Yue

Multi-Level Cross-Modal Alignment for Image Clustering

Liping Qiu, Qin Zhang, Xiaojun Chen, Shaotian Cai

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization

Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

Zhen Qin, Feiyi Chen, Chen Zhi, Xueqiang Yan, Shuiguang Deng

EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading

Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang

Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering

Xiaowei Qian, Bingheng Li, Zhao Kang

Towards Modeling Uncertainties of Self-Explaining Neural Networks via Conformal Prediction

Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang

MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

Hao Qian, Hongting Zhou, Qian Zhao, Hao Chen, Hongxiang Yao

Adaptive Feature Imputation with Latent Graph for Deep Incomplete Multi-View Clustering

Jingyu Pu, Chenhang Cui, Xinyue Chen, Yazhou Ren, Xiaorong Pu

Reconciling Predictive and Statistical Parity: A Causal Approach

Drago Plecko, Elias Bareinboim

Learning Performance Maximizing Ensembles with Explainability Guarantees

Vincent Pisztora, Jia Li

Recurrent Graph Neural Networks and Their Connections to Bisimulation and Logic

Maximilian Pflueger, David Tena Cucala, Egor V. Kostylev

Hypothesis, Verification, and Induction: Grounding Large Language Models with Self-Driven Skill Learning

shaohui peng, Xing Hu, Qi Yi, Rui Zhang, Jiaming Guo

Variational Hybrid-Attention Framework for Multi-Label Few-Shot Aspect Category Detection

Cheng Peng, Ke Chen, Lidan Shou, Gang Chen

CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition

Cheng Peng, Ke Chen, Lidan Shou, Gang Chen

HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning

Hongbin Pei, Taile Chen, Chen A, Huiqi Deng, Jing Tao

CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems

Sagar Patel, Sangeetha Abdu Jyothi, Nina Narodytska

ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models

Maitreya Patel, Tejas Gokhale, Chitta Baral, Yezhou Yang

REPrune: Channel Pruning via Kernel Representative Selection

Mincheol Park, Dongjin Kim, Cheonjun Park, Yuna Park, Gyeong Eun Gong

Convolutional Channel-Wise Competitive Learning for the Forward-Forward Algorithm

Andreas Papachristodoulou, Christos Kyrkou, Stelios Timotheou, Theocharis Theocharides

FedLF: Layer-Wise Fair Federated Learning

Zibin Pan, Chi Li, Fangchen Yu, Shuyi Wang, Haijin Wang

Learning Reduced Fluid Dynamics

Zherong Pan, Xifeng Gao, Kui Wu

A Graph Dynamics Prior for Relational Inference

Liming Pan, Cheng Shi, Ivan Dokmanić

Towards Fair Graph Federated Learning via Incentive Mechanisms

Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu

Multi-Objective Bayesian Optimization with Active Preference Learning

Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki, Shion Takeno, Ichiro Takeuchi

A Primal-Dual Algorithm for Hybrid Federated Learning

Tom Overman, Garrett Blum, Diego Klabjan

Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning

Motoki Omura, Takayuki Osa, Yusuke Mukuta, Tatsuya Harada

Multi-Class Support Vector Machine with Maximizing Minimum Margin

Feiping Nie, Zhezheng Hao, Rong Wang

Improve Robustness of Reinforcement Learning against Observation Perturbations via l∞ Lipschitz Policy Networks

Buqing Nie, Jingtian Ji, Yangqing Fu, Yue Gao

On Inference Stability for Diffusion Models

Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Khoat Than, Toan Tran

Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions

David D. Nguyen, David Liebowitz, Salil S. Kanhere, Surya Nepal

Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption

Adil Nawaz, Guopeng Chen, Muhammad Umair Raza, Zahid Iqbal, Jianqiang Li

Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains

Md Nasim, Yexiang Xue

Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit

Shintaro Nakamura, Masashi Sugiyama

Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning

Ruiqian Nai, Zixin Wen, Ji Li, Yuanzhi Li, Yang Gao

Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing

Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

Input Margins Can Predict Generalization Too

Coenraad Mouton, Marthinus Wilhelmus Theunissen, Marelie H Davel

Leveraging Local Variance for Pseudo-Label Selection in Semi-supervised Learning

Zeping Min, Jinfeng Bai, Chengfei Li

HOP to the Next Tasks and Domains for Continual Learning in NLP

Umberto Michieli, Mete Ozay

Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual Learning

Nicolas Michel, Giovanni Chierchia, Romain Negrel, Jean-François Bercher

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models

Xiangming Meng, Yoshiyuki Kabashima

Optimal Attack and Defense for Reinforcement Learning

Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent

Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Online Markov Decision Processes Configuration with Continuous Decision Space

Davide Maran, Pierriccardo Olivieri, Francesco Emanuele Stradi, Giuseppe Urso, Nicola Gatti

Permutation-Based Hypothesis Testing for Neural Networks

Francesca Mandel, Ian Barnett

One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems

Mikołaj Małkiński, Jacek Mańdziuk

Simple Weak Coresets for Non-decomposable Classification Measures

Jayesh Malaviya, Anirban Dasgupta, Rachit Chhaya

Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation

Louis Mahon, Thomas Lukasiewicz

PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning

Yuting Ma, Yuanzhi Yao, Xiaohua Xu

Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification

Yingfan Ma, xiaoyuan luo, Kexue Fu, Manning Wang

U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting

Xiang Ma, Xuemei Li, Lexin Fang, Tianlong Zhao, Caiming Zhang

One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training

Lianbo Ma, Yuee Zhou, Jianlun Ma, Guo Yu, Qing Li

Discerning Temporal Difference Learning

Jianfei Ma

Three Heads Are Better than One: Complementary Experts for Long-Tailed Semi-supervised Learning

Chengcheng Ma, Ismail Elezi, Jiankang Deng, Weiming Dong, Changsheng Xu

Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision

Nicholas Lui, Bryan Chia, William Berrios, Candace Ross, Douwe Kiela

Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation

Zhuqiang Lu, Kun Hu, Chaoyue Wang, LEI BAI, Zhiyong Wang

Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization

Yongfan Lu, Bingdong Li, Aimin Zhou

Decoupled Contrastive Multi-View Clustering with High-Order Random Walks

Yiding Lu, Yijie Lin, Mouxing Yang, Dezhong Peng, Peng Hu

Federated Learning with Extremely Noisy Clients via Negative Distillation

Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han

NodeMixup: Tackling Under-Reaching for Graph Neural Networks

Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Long Jin

UniADS: Universal Architecture-Distiller Search for Distillation Gap

Liming Lu, Zhenghan Chen, Xiaoyu Lu, Yihang Rao, Lujun Li

Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction

Kangkang Lu, Yanhua Yu, Hao Fei, Xuan Li, Zixuan Yang

CGS-Mask: Making Time Series Predictions Intuitive for All

Feng Lu, Wei Li, Yifei Sun, Cheng Song, Yufei Ren

Layer Collaboration in the Forward-Forward Algorithm

Guy Lorberbom, Itai Gat, Yossi Adi, Alexander Schwing, Tamir Hazan

On the Convergence of an Adaptive Momentum Method for Adversarial Attacks

Sheng Long, Wei Tao, Shuohao LI, Jun Lei, Jun Zhang

Cooperative Knowledge Distillation: A Learner Agnostic Approach

Michael Livanos, Ian Davidson, Stephen Wong

Backdoor Attacks via Machine Unlearning

Zihao Liu, Tianhao Wang, Mengdi Huai, Chenglin Miao

DART: Dual-Modal Adaptive Online Prompting and Knowledge Retention for Test-Time Adaptation

Zichen Liu, Hongbo Sun, Yuxin Peng, Jiahuan Zhou

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates

Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low

Decentralized Sum-of-Nonconvex Optimization

Zhuanghua Liu, Bryan Kian Hsiang Low

Diffusion Language-Shapelets for Semi-supervised Time-Series Classification

Zhen Liu, Wenbin Pei, Disen Lan, Qianli Ma

Text Diffusion with Reinforced Conditioning

YuXuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang

Detection-Based Intermediate Supervision for Visual Question Answering

Yuhang Liu, Daowan Peng, Wei Wei, Yuanyuan Fu, Wenfeng Xie

Causality-Inspired Invariant Representation Learning for Text-Based Person Retrieval

Yu Liu, Guihe Qin, Haipeng Chen, Zhiyong Cheng, Xun Yang

Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches

Yu Liu, Runzhe Wan, James McQueen, Doug Hains, Jinxiang Gu