Eric Nalisnick

31 papers · 2018–2025 · 11 conferences · across top CS/AI conferences

Achievements

+10 more ↓

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (16) 🌍 Conference Polyglot (11)

🗺️ Taxonomy Completionist (16) 🧭 Keyword Pioneer 👑 Triple Crown 🏆 Keyword Champion (2) 🗃️ Keyword Collector (103) ⚡ Prolific Year (5) 📈 Trend Setter 💎 Century Club (31) 🔥 Unstoppable (5) ❓ The Questioner (4)

Conferences

AISTATS (6) ICML (5) ICLR (4) NIPS (4) UAI (4) EMNLP (2) JMLR (2) COLING (1) ECCV (1) MLHC (1) NAACL (1)

Top co-authors

José Miguel Hernández-Lobato (7) Padhraic Smyth (4) Javier Antoran (4) Metod Jazbec (4) Alexander Timans (4) Dan Zhang (4) Rajeev Verma (4) Kaspar Sakmann (3) Dharmesh Tailor (3) Christian A. Naesseth (3)

Keywords

variational inference (4) uncertainty quantification (3) learning to defer (3) bayesian inference (3) neural network (3) data augmentation (2) few-shot learning (2) posterior approximation (2) semi-supervised learning (2) bayesian prior (2) bayesian deep learning (2) surrogate loss (2) normalizing flow (2) generative model (2) hate speech detection (2) hierarchical classification (1) conformal prediction (1) text classification (1) sentiment analysis (1) contrastive learning (1)

Papers

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence ICLR 2025 Max-Rank: Efficient Multiple Testing for Conformal Prediction AISTATS 2025 DefVerify: Do Hate Speech Models Reflect Their Dataset’s Definition? COLING 2025 On Continuous Monitoring of Risk Violations under Unknown Shift UAI 2025 Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach EMNLP 2025 Generative Uncertainty in Diffusion Models UAI 2025 Lightning UQ Box: Uncertainty Quantification for Neural Networks JMLR 2025 ELBOing Stein: Variational Bayes with Stein Mixture Inference ICLR 2025 Early-Exit Neural Networks with Nested Prediction Sets UAI 2024 A Generative Model of Symmetry Transformations NIPS 2024 Fast yet Safe: Early-Exiting with Risk Control NIPS 2024 Learning to Defer to a Population: A Meta-Learning Approach AISTATS 2024 Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction ECCV 2024 Sampling-based inference for large linear models, with application to linearised Laplace ICLR 2023 Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity NIPS 2023 Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023 Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles AISTATS 2023 Exploiting Inferential Structure in Neural Processes UAI 2023 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning ICML 2022 Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions NAACL 2022 Calibrated Learning to Defer with One-vs-All Classifiers ICML 2022 Predictive Complexity Priors AISTATS 2021 Bayesian Deep Learning via Subnetwork Inference ICML 2021 Normalizing Flows for Probabilistic Modeling and Inference JMLR 2021 How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task EMNLP 2021 Bayesian Batch Active Learning as Sparse Subset Approximation NIPS 2019 Hybrid Models with Deep and Invertible Features ICML 2019 Do Deep Generative Models Know What They Don't Know? ICLR 2019 Dropout as a Structured Shrinkage Prior ICML 2019 Bayesian Trees for Automated Cytometry Data Analysis MLHC 2018 Learning Priors for Invariance AISTATS 2018