NeurIPS 2024: all accepted papers
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- Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient ↗Nataša Tagasovska, Vladimir Gligorijević, Kyunghyun Cho et al.
- Improved Guarantees for Fully Dynamic k-Center Clustering with Outliers in General Metric Spaces ↗Leyla Biabani, Annika Hennes, Denise La Gordt Dillie et al.
- Improved Regret of Linear Ensemble Sampling ↗Harin Lee, Min-hwan Oh
- Improved Sample Complexity Bounds for Diffusion Model Training ↗Shivam Gupta, Aditya Parulekar, Eric Price et al.
- Improved off-policy training of diffusion samplers ↗Marcin Sendera, Minsu Kim, Sarthak Mittal et al.
- Improving Decision Sparsity ↗Yiyang Sun, Tong Wang, Cynthia D Rudin
- Improving Environment Novelty Quantification for Effective Unsupervised Environment Design ↗Jayden Teoh, Wenjun Li, Pradeep Reddy Varakantham
- Improving Equivariant Model Training via Constraint Relaxation ↗Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi et al.
- Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes ↗Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec et al.
- Improving Neural Network Surface Processing with Principal Curvatures ↗Josquin Harrison, James Benn, Maxime Sermesant B
- Improving Neural ODE Training with Temporal Adaptive Batch Normalization ↗Zheng Su, Zhengqi Gao, Fan-Keng Sun et al.
- Improving Subgroup Robustness via Data Selection ↗Saachi Jain, Kimia Hamidieh, Kristian Georgiev et al.
- Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection ↗Yinxuan Huang, Chengmin Gao, Bin Li et al.
- Improving self-training under distribution shifts via anchored confidence with theoretical guarantees ↗Taejong Joo, Diego Klabjan
- Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity ↗Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
- In-Context Learning with Representations: Contextual Generalization of Trained Transformers ↗Tong Yang, Yu Huang, Yingbin Liang et al.
- In-Context Symmetries: Self-Supervised Learning through Contextual World Models ↗Sharut Gupta, Chenyu Wang, Yifei Wang et al.
- Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques ↗Manh Binh Dao, Phi Le Nguyen, Thao Truong et al.
- Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery ↗Yongwei Nie, Mingxian Fan, Chengjiang Long et al.
- Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence ↗Feng-Yi Liao, Lijun Ding, Yang Zheng
- Information Re-Organization Improves Reasoning in Large Language Models ↗Xiaoxia Cheng, Zeqi Tan, Wei Xue et al.
- Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation ↗Sagi Eppel, Jolina Li, Manuel S. Drehwald et al.
- Instruction Embedding: Latent Representations of Instructions Towards Task Identification ↗Yiwei Li, Jiayi Shi, Shaoxiong Feng et al.
- Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion ↗Masahito Uwamichi, Simon K. Schnyder, Tetsuya J. Kobayashi et al.
- Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems ↗Dingbang Liu, Shōhei Kato, Wen Gu et al.
- Interaction-Force Transport Gradient Flows ↗Egor Leonidovich Gladin, Pavel Evgenyevich Dvurechensky, Alexander Mielke et al.
- Interfacing Foundation Models' Embeddings ↗Xueyan Zou, Linjie Li, Jianfeng Wang et al.
- Interpolating Item and User Fairness in Multi-Sided Recommendations ↗Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei et al.
- Interpreting Learned Feedback Patterns in Large Language Models ↗Luke Marks, Amir Abdullah, Clement Neo et al.
- Intervention and Conditioning in Causal Bayesian Networks ↗Sainyam Galhotra, Joseph Yehuda Halpern
- Interventionally Consistent Surrogates for Complex Simulation Models ↗Joel Dyer, Nicholas Bishop, Yorgos Felekis et al.
- Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling ↗Wei Tang, Haifeng Xu, Ruimin Zhang et al.
- Invariant subspaces and PCA in nearly matrix multiplication time ↗Aleksandros Sobczyk, Marko Mladenović, Mathieu Luisier
- Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning ↗The Viet Bui, Tien Mai, Thanh Nguyen
- InversionView: A General-Purpose Method for Reading Information from Neural Activations ↗Xinting Huang, Madhur Panwar, Navin Goyal et al.
- Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions ↗Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata et al.
- Is Multiple Object Tracking a Matter of Specialization? ↗Gianluca Mancusi, Mattia Bernardi, Aniello Panariello et al.
- Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL ↗Enoch Hyunwook Kang, P. R. Kumar
- Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models. ↗Athanasios Tragakis, Marco Aversa, Chaitanya Kaul et al.
- Is Score Matching Suitable for Estimating Point Processes? ↗Haoqun Cao, Zizhuo Meng, Tianjun Ke et al.
- Iterative Methods via Locally Evolving Set Process ↗Baojian Zhou, Yifan Sun, Reza Babanezhad et al.
- John Ellipsoids via Lazy Updates ↗David P. Woodruff, Taisuke Yasuda
- JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images ↗Zhecan Wang, Junzhang Liu, Chia-Wei Tang et al.
- Just Add $100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem ↗Mincheol Chang, Siyeong Lee, Nam Il Kim et al.
- Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm ↗Sattar Vakili, Julia Olkhovskaya
- Knowledge Graph Completion by Intermediate Variables Regularization ↗Changyi Xiao, Yixin Cao
- LLaMo: Large Language Model-based Molecular Graph Assistant ↗Jinyoung Park, Minseong Bae, Dohwan Ko et al.
- LT-Defense: Searching-free Backdoor Defense via Exploiting the Long-tailed Effect ↗Yixiao Xu, Binxing Fang, Mohan Li et al.
- Label Noise: Ignorance Is Bliss ↗Yilun Zhu, Jianxin Zhang, Aditya Gangrade et al.
- Language Without Borders: A Dataset and Benchmark for Code-Switching Lip Reading ↗Xueyi Zhang, Chengwei Zhang, Mingrui Lao et al.
- Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning ↗Alessandro Montenegro, Marco Mussi, Matteo Papini et al.
- Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models ↗Minki Kang, Sung Ju Hwang, Gibbeum Lee et al.
- Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks ↗Victor Boutin, Rishav Mukherji, Aditya Agrawal et al.
- Learning 1D Causal Visual Representation with De-focus Attention Networks ↗Chenxin Tao, Xizhou Zhu, Shiqian Su et al.
- Learning Better Representations From Less Data For Propositional Satisfiability ↗Mohamed Ghanem, Frederik Schmitt, Julian Siber et al.
- Learning Bregman Divergences with Application to Robustness ↗Mohamed-Hicham Leghettas, Markus Püschel
- Learning Cut Generating Functions for Integer Programming ↗Hongyu Cheng, Amitabh Basu
- Learning Elastic Costs to Shape Monge Displacements ↗Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin et al.
- Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm ↗Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
- Learning Goal-Conditioned Representations for Language Reward Models ↗Vaskar Nath, Dylan Slack, Yuntao Ma et al.
- Learning Group Actions on Latent Representations ↗Yinzhu Jin, Aman Shrivastava, P. Thomas Fletcher
- Learning Infinitesimal Generators of Continuous Symmetries from Data ↗Gyeonghoon Ko, Hyunsu Kim, Juho Lee
- Learning Mixtures of Unknown Causal Interventions ↗Abhinav Kumar, Kirankumar Shiragur, Caroline Uhler
- Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random ↗Gautam Chandrasekaran, Vasilis Kontonis, Konstantinos Stavropoulos et al.
- Learning Partitions from Context ↗Simon Buchholz
- Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation ↗Xinhao Zheng, Yang Li, Cunxin Fan et al.
- Learning Representations for Hierarchies with Minimal Support ↗Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel et al.
- Learning Structure-Aware Representations of Dependent Types ↗Konstantinos Kogkalidis, Orestis Melkonian, Jean-Philippe Bernardy
- Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise ↗Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas et al.
- Learning and Transferring Sparse Contextual Bigrams with Linear Transformers ↗Yunwei Ren, ZiXuan Wang, Jason D. Lee
- Learning from Offline Foundation Features with Tensor Augmentations ↗Emir Konuk, Christos Matsoukas, Moein Sorkhei et al.
- Learning from Snapshots of Discrete and Continuous Data Streams ↗Pramith Devulapalli, Steve Hanneke
- Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms ↗Thanh Nguyen-Tang, Raman Arora
- Learning on Large Graphs using Intersecting Communities ↗Ben Finkelshtein, Ismail Ceylan, Michael Bronstein et al.
- Learning symmetries via weight-sharing with doubly stochastic tensors ↗Putri A. van der Linden, Alejandro García-Castellanos, Sharvaree Vadgama et al.
- Learning the Expected Core of Strictly Convex Stochastic Cooperative Games ↗Nam Tran, The Anh Ta, Shuqing Shi et al.
- Learning to Assist Humans without Inferring Rewards ↗Vivek Myers, Evan Ellis, Sergey Levine et al.
- Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf ↗Xuanfa Jin, Ziyan Wang, Yali Du et al.
- Learning to Embed Distributions via Maximum Kernel Entropy ↗Oleksii Kachaiev, Stefano Recanatesi
- Learning to Price Homogeneous Data ↗Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
- Learning to Reason via Program Generation, Emulation, and Search ↗Nathaniel Weir, Muhammad Khalifa, Linlu Qiu et al.
- Learning via Surrogate PAC-Bayes ↗Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj
- Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models ↗Ruofeng Yang, Zhijie Wang, Bo Jiang et al.
- Leveraging Separated World Model for Exploration in Visually Distracted Environments ↗Kaichen Huang, Le Gan, Minghao Shao et al.
- Light Unbalanced Optimal Transport ↗Milena Gazdieva, Arip Asadulaev, Evgeny Vladimirovich Burnaev et al.
- Linear Causal Bandits: Unknown Graph and Soft Interventions ↗Zirui Yan, Ali Tajer
- Linear Regression using Heterogeneous Data Batches ↗Ayush Jain, Rajat Sen, Weihao Kong et al.
- Linear Time Approximation Algorithm for Column Subset Selection with Local Search ↗Yuanbin Zou, Ziyun Huang, Jinhui Xu et al.
- Linear Uncertainty Quantification of Graphical Model Inference ↗Chenghua Guo, Han Yu, Jiaxin Liu et al.
- Linguistic Collapse: Neural Collapse in (Large) Language Models ↗Robert Wu, Vardan Papyan
- LoCo: Learning 3D Location-Consistent Image Features with a Memory-Efficient Ranking Loss ↗Dominik A. Kloepfer, João Henriques, Dylan Campbell
- LoQT: Low-Rank Adapters for Quantized Pretraining ↗Sebastian Loeschcke, Mads Bech Toftrup, Michael J. Kastoryano et al.
- Local Curvature Smoothing with Stein's Identity for Efficient Score Matching ↗Genki Osada, Makoto Shing, Takashi Nishide
- Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs ↗Davide Maran, Alberto Maria Metelli, Matteo Papini et al.
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