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← Authors

Maxim Panov

28 papers · 2019–2025 · 13 conferences · across top CS/AI conferences

Achievements

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+11 more ↓ 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🌍 Conference Polyglot (13)
🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (6) 🀝 Dynamic Duo (13) πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (2) ⚑ Prolific Year (8) ❓ The Questioner (3) πŸ—ƒοΈ Keyword Collector (95) πŸ’Ž Century Club (28) πŸ”₯ Unstoppable (5)

Conferences

ACL (5) EMNLP (5) ICLR (3) ICML (3) ACML (2) AISTATS (2) IJCAI (2) AACL (1) EACL (1) IJCNLP (1) NAACL (1) NIPS (1) UAI (1)

Top co-authors

Artem Shelmanov (13) Alexander Panchenko (11) Artem Vazhentsev (10) Timothy Baldwin (7) Eric Moulines (6) Gleb Kuzmin (6) Nikita Kotelevskii (5) Akim Tsvigun (5) Alexander Fishkov (5) Roman Vashurin (5)

Keywords

uncertainty quantification (10) uncertainty estimation (6) large language model (6) hallucination detection (4) out-of-distribution detection (3) selective generation (3) federated learning (3) text generation (3) conformal prediction (2) active learning (2) transformer model (2) token-level prediction (2) aleatoric uncertainty (2) monte carlo dropout (2) text summarization (2) epistemic uncertainty (2) multimodal learning (1) autoregressive generation (1) text classification (1) named entity recognition (1)

Papers

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation ICLR 2025 Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models EMNLP 2025 Uncertainty Quantification for Large Language Models ACL 2025 Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models NAACL 2025 Rectifying Conformity Scores for Better Conditional Coverage ICML 2025 Probabilistic Conformal Prediction with Approximate Conditional Validity ICLR 2025 UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models EMNLP 2025 Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification ACL 2024 Reference-free Hallucination Detection for Large Vision-Language Models EMNLP 2024 Efficient Conformal Prediction under Data Heterogeneity AISTATS 2024 Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks IJCAI 2024 Generalization error of spectral algorithms ICLR 2024 Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective UAI 2023 Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? AACL 2023 Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks ACL 2023 Efficient Out-of-Domain Detection for Sequence to Sequence Models ACL 2023 Selective Nonparametric Regression via Testing ACML 2023 LM-Polygraph: Uncertainty Estimation for Language Models EMNLP 2023 Conformal Prediction for Federated Uncertainty Quantification Under Label Shift ICML 2023 Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? IJCNLP 2023 Nonparametric Uncertainty Quantification for Single Deterministic Neural Network NIPS 2022 Uncertainty Estimation of Transformer Predictions for Misclassification Detection ACL 2022 Embedded Ensembles: infinite width limit and operating regimes AISTATS 2022 Active Learning for Abstractive Text Summarization EMNLP 2022 Monte Carlo Variational Auto-Encoders ICML 2021 How Certain is Your Transformer? EACL 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