NeurIPS 2025PastRobustness
NeurIPS 2025 Workshop: Reliable ML from Unreliable Data
NeurIPS 2025 - Reliable ML Workshop
- Submission deadline
- Aug 30, 2025, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (149)
Fetched from OpenReview (v2) on 2026-06-10.
$\texttt{strategic-fl-sim}$: An Extensible Package for Simulating Strategic Behavior in Federated Learning
A Few Bad Neurons: Isolating and Surgically Correcting Sycophancy
A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy
A Multi-Method Interpretability Framework for Probing Cognitive Processing in Deep Neural Networks across Vision and Biomedical Domains
Active Slice Discovery in Large Language Models
Adaptive Norm Selection Prevents Catastrophic Overfitting in Fast Adversarial Training
Adversarial Attacks against Context-dependent Visual Association in Referring Multi-Object Tracking Systems
Adversarially-robust probes for Deep Networks
Aggregated Individual Reporting for Post-Deployment Evaluation: Mechanism Design & Modeling Considerations
Ambient Diffusion Omni
Ambient Proteins: Training Diffusion Models on Low Quality Structures
An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
Approximate Leave-One-Out Cross Validation for Robust Scatter Matrix Estimation
Approximating Human Preferences Using a Multi-Judge Learned System
AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin
Automated Generation of Multilingual Jailbreak Prompts
Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes
Bayesian Decision Making around Experts
Better Data for Satellite Super Resolution
Beyond Per-Question Privacy: Multi-Query Differential Privacy for RAG Systems
Beyond Static Bias: Quantifying Fairness Variability in CheXpert
Beyond Text: Multimodal Jailbreaking of Vision-Language and Audio Models through Perceptually-Aware Transformations
Breaking Bad: Exploring the Dangers of LLM-generated Misinformation from Fringe Social Media
Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators
BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
Certified Adversarial Robustness via Mixture-of-Gaussians Randomized Smoothing
Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety
Clean-Label Physical Backdoor Attacks with Data Distillation
COIR: Chain-of-Intention Reasoning Elicits Defense in Multimodal Large Language Models
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Concept-Based Masking: A Patch-Agnostic Defense Against Adversarial Patch Attacks
Conformal Prediction for Molecular Properties under Label Shift
Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
Cost Efficient Fairness Audit Under Partial Feedback
CroPA++: Exposing Vulnerabilities in Vision Language Models and Enhancing Adversarial Transferability of Cross-Prompt Attacks
Cross-Lingual Multimodal Retrieval-Augmented Generation for Open Question Answering in Tamil and Yoruba
Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
Data Decomposition beyond Splitting for Causal Estimation
Data-Efficient and Robust Coreset Selection via Sparse Adversarial Perturbations
Deep Research Brings Deeper Harm
Diffusion-supplemented Implicit Layers: Operator Smoothing for better Implicit Solvers
Disarming Strategic Text: Span-Aware Counterfactuals for Robust Content Moderation
Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
Do Internal Layers of LLMs Reveal Patterns for Jailbreak Detection?
Domain Generalization: A Tale of Two ERMs
Don’t Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning
Double Machine Learning Evaluation Under Distribution Shift and Selection Bias
Drawing Reliable Conclusions with Imperfect Synthetic Data
DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
Efficiently Robust In-Context Reinforcement Learning with Adversarial Generalization and Adaptation
Energy-Shaped Manifold Projections Enable Adversarial Detection
ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models
Evaluating robustness of tabular models under meta-features based shifts
Evaluating the Quality of AI-Generated Resolutions from Conversational vs Structured Sources: Implications for Enterprise Knowledge Automation
Extracting Latent Generalization from Models Trained with Noisy Labels
Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration
FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
Fairness Implications of GNN-to-MLP Knowledge Distillation
Fairness Through Independence via Cramér-von Mises Regularization
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize
FAVAE-Effective Frequency Aware Latent Tokenizer
Few-Shot Knowledge Distillation for Language Models via Counterfactual Explanations
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning
From Clutter to Clarity: Visual Recognition through Foveated Object-Centric Learning (FocL)
From Evidence to Knowledge: A Hierarchical Probabilistic Model of the Scientific Knowledge Landscape at Web Scale
From Many Voices to One: A Statistically Principled Aggregation of LLM Judges
From Search to Decision: A Framework for Adversarially Robust Approximate Nearest Neighbor Search
From Semantics to Symbols: A Two-Stage Framework for Deconstructing LLM Reasoning into Concepts and Rules
Generalizing Robustness from $\ell_p$ to Unforeseen Attack via Calibrated Adversarial Sampling
GUARD: Guiding Unbiased Alignment through Reward Debiasing
Human Uncertainty-Aware Reliable Data Selection and Efficient Annotation for Visual Question Answering
Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards
Inducing Uncertainty on Open-Weight Models for Test-Time Privacy in Image Recognition
Influence Functions for Preference Dataset Pruning
Information-Theoretic Conditions for Chain-of-Thought Monitorability and Methods for Improving It
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
It is Hard to Unlearn Dogged Backdoor Samples in Diffusion Models
KAIROS: Scalable Model-Agnostic Data Valuation
Keep It Real: Challenges in Attacking Compression-Based Adversarial Purification
Learning reliably under adversarial attacks, distribution shifts and strategic agents
Lightweight Robust Direct Preference Optimization
LoCaTE: A Local and Training Dynamics Perspective at Detecting Label Noise in Deep Classification
Locks Tested Without Burglars: Using Coding Assistants to Break Prompt Injection Defenses
Minimal Repairs for Learning Over Incomplete Data
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Near-Optimal Reinforcement Learning for Linear Distributionally Robust Markov Decision Processes
Not All Samples Are Equal: Quantifying Instance-level Difficulty in Targeted Data Poisoning
Not All Splits Are Equal: Rethinking Attribute Generalization Across Unrelated Categories
Obscurable Fishermen
On Fairness of Task Arithmetic: The Role of Task Vectors
On the Interaction of Compressibility and Adversarial Robustness
Optimal Fair Learning Robust to Adversarial Distribution Shift
Optimal Lower Bounds and New Upper Bounds for Sequential Prediction with Abstention
Persistent and Stealthy Backdoor Attacks in Federated Learning via Layerwise Model Poisoning
Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference
Probabilistic Framework for Robustness of Counterfactual Explanations Under Data Shifts
Quantifying CBRN Risk in Frontier Models
Reasoning as an Adaptive Defense for Safety
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
Regularized Robustly Reliable Learners and Instance Targeted Attacks
Reliable Active Learning from Unreliable Labels via Neural Collapse Geometry
Reliable Compositional Editing with Overlap-Aware Attention in Diffusion Models
Reliable Models via Responsiveness Verification
Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection
Responsible Imputation of User Behavior Surveys via Mask-Aware Transformers
Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
Reweighted Flow Matching via Unbalanced Optimal Transport for Long-tailed Generation
RL-Guided Data Selection for Language Model Finetuning
Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling
Robust Federated Learning under Heterogeneous Data with Generalized Heavy-Ball Momentum
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Robust Multi-task Modeling for Bayesian Optimization via In-Context Learning
Safety by Design: High-Probability Constrained Contextual Bandits
SAGE: Streaming, Agreement-driven Gradient Sketches for Representative Subset Selection
Sandbagging in a Simple Survival Bandit Problem
Selective Cost-Aware Random Forests for Unreliable Data
Selective Preference Aggregation
SIVA: Self-Improving Vulnerability Agent
Sparse Parameter Adaptation for Fair Model Transfer Across Domains
Spectral Regularization as a Safety-Critical Inductive Bias
StealthEval: A Probe-Rewrite-Evaluate Workflow for Reliable Benchmarks
Strategic Feature Selection
Stress-Testing Byzantine Defenses under Data Heterogeneity
Stylistic Shifts in Human–LLM Conversations: Challenges and Adaptation
Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition
Taming the Noisy Oracle: Robust Entity-Centric Question Answering via Learning from Imperfect Feedback
Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks
Teaming LLMs to Detect and Mitigate Hallucinations
Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift
Testing Noise Assumptions of Learning Algorithms
Text‑Guided Data Attribution: Attributing the Influence of Simplicity Bias to Dataset
The Impact of Training Data on Adversarial Robustness
The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge
The Statistical Fairness-Accuracy Frontier
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
Towards Trustworthy Amortized Bayesian Model Comparison
Trust, But Attribute: Tracing Impact of Data on Trustworthiness in Supervised LLM Fine-Tuning
Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Uncertainty-Aware LLMs Fail to Flag Misleading Contexts
Unlocking Transfer Learning for Open-World Few-Shot Recognition
Unspoken Hints: Accuracy Without Acknowledgement in LLM Reasoning
WASP: A Weight-Space Approach to Detecting Learned Spuriousness
Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs
When "Competency" in Reasoning Opens the Door to Vulnerability: Jailbreaking LLMs via Novel Ciphers
Why is Your Language Model a Poor Implicit Reward Model?
Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
Zero-Shot Robustness of Vision Language Models Via Confidence-Aware Weighting