Maxim Panov
29 papers · 2019–2026 · 12 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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Machine Learning
(24)
Optimization & Theory
(16)
Artificial Intelligence
(15)
Learning Types Β· Machine Learning
(15)
Core AI
(10)
Core Methods
(9)
Deep Learning
(8)
Natural Language Processing
(6)
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Keywords
uncertainty quantification
(10)
large language model
(6)
uncertainty estimation
(6)
out-of-distribution detection
(4)
conformal prediction
(4)
federated learning
(4)
hallucination detection
(4)
text generation
(3)
selective generation
(3)
aleatoric uncertainty
(3)
epistemic uncertainty
(3)
active learning
(2)
transformer model
(2)
monte carlo dropout
(2)
conditional coverage
(2)
text summarization
(2)
quantile regression
(2)
data heterogeneity
(2)
token-level prediction
(2)
named entity recognition
(1)
Papers
Adaptive Cumulative Mass Calibration with Conformal Prediction
UAI 2026
Who to Trust? Aggregating Client Predictions in Federated Distillation.
UAI 2026
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
ICLR 2025
Probabilistic Conformal Prediction with Approximate Conditional Validity
ICLR 2025
Rectifying Conformity Scores for Better Conditional Coverage
ICML 2025
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
NAACL 2025
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models
EMNLP 2025
Uncertainty Quantification for Large Language Models
ACL 2025
Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models
EMNLP 2025
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
IJCAI 2024
Efficient Conformal Prediction under Data Heterogeneity
AISTATS 2024
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
ACL 2024
Generalization error of spectral algorithms
ICLR 2024
Reference-free Hallucination Detection for Large Vision-Language Models
EMNLP 2024
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
ICML 2023
Efficient Out-of-Domain Detection for Sequence to Sequence Models
ACL 2023
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks
ACL 2023
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective
UAI 2023
LM-Polygraph: Uncertainty Estimation for Language Models
EMNLP 2023
Selective Nonparametric Regression via Testing
ACML 2023
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?
IJCNLP 2023
Active Learning for Abstractive Text Summarization
EMNLP 2022
Embedded Ensembles: infinite width limit and operating regimes
AISTATS 2022
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
NeurIPS 2022
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
ACL 2022
How Certain is Your Transformer?
EACL 2021
Monte Carlo Variational Auto-Encoders
ICML 2021
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
IJCAI 2019
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension
ACML 2019