ICLR 2024PastAI for scienceDatasets
ICLR 2024 Workshop on Data-centric Machine Learning Research (DMLR): Harnessing Momentum for Science
DMLR @ ICLR 2024
- Submission deadline
- Feb 9, 2024, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (85)
Fetched from OpenReview (v2) on 2026-06-10.
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Analyzing Diffusion Models on Synthesizing Training Datasets
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
Annotation Sensitivity: Drivers of Training Data Quality
Atomic Data Groups: An issue in train-test splits for the real world as demonstrated through digital hardware design
Autoregressive activity prediction for low-data drug discovery
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
Bidirectional Long-Range Parser for Sequential Data Understanding
Birbal: An efficient 7B instruct-model fine-tuned with curated datasets
Building Scalable Video Understanding Benchmarks through Sports
Calibrated prediction of scarce adverse drug reaction labels with conditional neural processes
CLE-SMOTE: Addressing Extreme Imbalanced Data Classification with Contrastive Learning-Enhanced SMOTE
Coactive Learning for Large Language Models using Implicit User Feedback
Combining Time Series Modalities to Create Endpoint-driven Patient Records
Computational Copyright: Towards A Royalty Model for AI Music Generation Platforms
Corrective Machine Unlearning
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
Data Distribution Valuation
Data-Efficient Multi-Modal Contrastive Learning: Prioritizing Data Quality over Quantity
Denoising Drug Discovery ADMET Data for Improved Regression Task Performance
Deploying Data Selection Techniques on Dynamic Datasets
Distributional Dataset Distillation with Subtask Decomposition
Empowering Large Language Models for Textual Data Augmentation
Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
Enhanced Variational Autoencoder Estimation from Incomplete Data using Mixture Variational Families
Environment-adjusted Topic Models
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training
Feedback-guided Data Synthesis for Imbalanced Classification
Fractals as Pre-training Datasets for Anomaly Detection and Localization
From Categories to Classifier: Name-Only Continual Learning by Exploring the Web
FTFT: efficient and robust Fine-Tuning by transFerring Training Dynamics
Genetic Learning for Designing Sim-to-Real Data Augmentations
GitChameleon: Breaking the version barrier for code generation models
Graph Kernel Convolutions for Interpretable Classification
GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Heterogeneous Normal Classes Pose a Challenge for Anomaly Detection
Identifying Spurious Correlations Early in Training through the Lens of Simplicity Bias
Improving Semantic Segmentation Models through Synthetic Data Generation via Diffusion Models
Information Compensation: A Fix for Any-scale Dataset Distillation
Interpretable Graph Neural Networks for Tabular Data
Is a picture of a bird a bird? A mixed-methods approach to understanding diverse human perspectives and ambiguity in machine vision models
Is margin all you need? An extensive empirical study of deep active learning on tabular data
Language Models as Science Tutors
Learning Galaxy Intrinsic Alignment Correlations
Learning representations of learning representations
Learning to Rank for One-Round Active Learning
Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress
LLM-Guided Counterfactual Data Generation for Fairer AI
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Measuring Diversity in Datasets
Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism
Multi-model evaluation with labeled and unlabeled data
On the Scalability of GNNs for Molecular Graphs
One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
OODRobustBench: a benchmark and large-scale analysis of adversarial robustness under distribution shift
Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features
PointSAGE : Mesh-independent superresolution approach to fluid flow predictions
PRE: Vision-Language Prompt Learning with Reparameterization Encoder
Pretraining Probabilistic Models for Scalable Precision Agriculture
Private Data Measurements for Decentralized Data Markets
Pushing the Decision Boundaries: Discovering New Classes in Audio Data
QualEval: Qualitative Evaluation for Model Improvement
Quantifying the Importance of Data Alignment in Downstream Model Performance
QuRating: Selecting High-Quality Data for Training Language Models
Re-evaluating Retrosynthesis Algorithms with Syntheseus
Retail-786k: a Large-Scale Dataset for Visual Entity Matching
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
Style-Content Disentanglement Under Conditional Shift
The Science of Data Filtering: Data Curation cannot be Compute Agnostic
TOTEM: Tokenized Time Series Embeddings for General Time Series Analysis
Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values
Towards Efficient Active Learning in NLP via Pretrained Representations
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models
Towards Quantifying the Effect of Datasets for Benchmarking: A Look at Tabular Machine Learning
Towards Robust Data Pruning
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
Unveiling the Intertwined Relationship Between Essential Sparsity and Robustness in Large Pre-trained Models
Urban Sound Propagation: a Benchmark for 1-Step Generative Modeling of Complex Physical Systems
Verified Training for Counterfactual Explanation Robustness under Data Shift
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
When is Off-Policy Evaluation Useful? A Data-Centric Perspective
WINDSET: Weather Insights and Novel Data for Systematic Evaluation and Testing
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling