ICML 2025 Past Large language models
1st ICML Workshop on Foundation Models for Structured Data
FMSD @ ICML 2025
- Submission deadline
- May 24, 2025, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (70)
Fetched from OpenReview (v2) on 2026-06-10.
-
AdaRec: Adaptive Recommendation with LLMs via Narrative Profiling and Dual-Channel Reasoning
-
Are Time Series Foundation Models Ready for Zero-Shot Forecasting?
-
Assessing the Robustness of Tabular Prior-Data Fitted Network Classifier
-
Calibration Properties of Time Series Foundation Models
-
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data only
-
Causal Foundation Models: Disentangling Physics from Instrument Properties
-
CLEAR: Contextual Logic-based Explanations for Anomaly Reasoning
-
ConTextTab: A Semantics-Aware Tabular In-Context Learner
-
Do Large Foundation Models Improve Time Series Segmentation? An Industrial Case Study in Oil and Gas Drilling
-
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
-
Do-PFN: In-Context Learning for Causal Effect Estimation
-
DriMM: Drilling Multimodal Model for Time-Series and Text in the Era of Large Models
-
Dual Adaptation of Time-Series Foundation Models for Financial Forecasting
-
Early Stopping Tabular In-Context Learning
-
Efficient Table Generation for Zero-Shot Column Type Annotation
-
Eliciting Numerical Predictive Distributions of LLMs Without Auto-Regression
-
Explore the Time Series Forecasting Potential of TabPFN Leveraging the Intrinsic Periodicity of Data
-
Exploring Relational Database Foundation Models from a Graph Perspective
-
Filter, Augment, Forecast: Online Data Selection for Robust Time Series Forecasting
-
FoMo-0D: A Foundation Model for Zero-shot Outlier Detection
-
Foundation Models for Clinical Records at Health System Scale
-
Foundation models for time series forecasting and policy evaluation in infectious disease epidemics: a modelling study
-
From Structured Data to Clinical Notes: Robust Clinical Decision Support with Fine-Tuned LLMs
-
From Tabular to Time Series: Can TabPFN Handle Mixed Data? A Study on PhysioNet
-
From Video Classification to Action Detection: Foundation vs. Task-Specific Models
-
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
-
Gateformer: Advancing Multivariate Time Series Forecasting via Temporal and Variate-Wise Attention with Gated Representations
-
GATS: A Time-Series Dataset for Addressing General Aviation Flight Safety
-
GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
-
Improving Treatment Effect Estimation with LLM-Based Data Augmentation
-
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
-
Instruction Tuning of Large Language Models for Tabular Data Generation—in One Day
-
LEAD - Framework for efficient time-series anomaly detection on large scale data using LLMs
-
Learning What Matters First: Sequential Adaptation of Time Series Foundation Models for Robust Financial Forecasting
-
Lights Out, Tabs On: Advancing Row-Column Encoding for Tabular LLMs
-
LLM Agents Struggle at Time Series Machine Learning Engineering
-
LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
-
Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard
-
Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
-
MORPHEUS : A Foundation Model for Multivariate Time Series Forecasting
-
Multivariate Calibration is Performative: A Perspective on Pitfalls and Progress
-
Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting
-
One-Run Privacy Auditing for Structured Generative and Foundation Models
-
Photoplethysmography, Foundation Models, Hypertension and Diabetes
-
Query, Don’t Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
-
Random Initialization Can’t Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting
-
Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
-
RECoRD: A Multi-Agent LLM Framework for Reverse Engineering Codebase to Relational Diagram
-
Rethinking Description Length: A TabPFN-Based Approximation of Bayesian Mixture Codes
-
Self-Imputation and Cross-Variable Learning Improve Water Quality Prediction with Sparse Data
-
Simulation-Based Pretraining and Domain Adaptation for Astronomical Time Series Tasks with Minimal Labeled Data
-
Soft Contrastive Learning for Irregular Multivariate Time Series
-
Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
-
State-Space Models for Tabular Prior-Data Fitted Networks
-
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
-
TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
-
TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation
-
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
-
TiRex: Zero-Shot Forecasting Across Long and Short Horizons
-
Toto: An Open Time Series Foundation Model Optimized for Observability
-
Toward Scientific Foundation Models for Aquatic Ecosystems
-
Towards a Multi-Modal Foundation Model for Inertial Confinement Fusion: Combining Structured Data and Diagnostic Images
-
Towards Benchmarking Foundation Models for Tabular Data With Text
-
Towards Fair In-Context Learning with Tabular Foundation Models
-
Towards Generalizable Multimodal ECG Representation Learning with LLM-extracted Clinical Entities
-
Towards Interpretable Time Series Foundation Models
-
Towards Synthetic Data for Fine-tuning Tabular Foundation Models
-
Two-Stage Contrastive Language Electrocardiogram Pre-training for Fine-Grained Waveform Features
-
W-LSTMix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting
-
When and How Unlabeled Data Provably Improve In-Context Learning