Eric T. Nalisnick
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
31
Venues
12
Active years
2013–2025
Best venue rank
A*
Where they publish
Papers
31 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Max-Rank: Efficient Multiple Testing for Conformal Prediction. | Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric T. Nalisnick |
| 2025 | COLING | DefVerify: Do Hate Speech Models Reflect Their Dataset's Definition? | Urja Khurana, Eric T. Nalisnick, Antske Fokkens |
| 2025 | EMNLP | Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach. | Alessa Carbo, Eric T. Nalisnick |
| 2025 | ICLR | ELBOing Stein: Variational Bayes with Stein Mixture Inference. | Ola Rnning, Eric T. Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck |
| 2025 | ICLR | Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence. | Dharmesh Tailor, Alvaro H. C. Correia, Eric T. Nalisnick, Christos Louizos |
| 2025 | UAI | Generative Uncertainty in Diffusion Models. | Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric T. Nalisnick, Stephan Mandt |
| 2025 | UAI | On Continuous Monitoring of Risk Violations under Unknown Shift. | Alexander Timans, Rajeev Verma, Eric T. Nalisnick, Christian A. Naesseth |
| 2024 | AISTATS | Learning to Defer to a Population: A Meta-Learning Approach. | Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick |
| 2024 | ECCV | Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction. | Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric T. Nalisnick |
| 2024 | UAI | Early-Exit Neural Networks with Nested Prediction Sets. | Metod Jazbec, Patrick Forr, Stephan Mandt, Dan Zhang, Eric T. Nalisnick |
| 2023 | AISTATS | Do Bayesian Neural Networks Need To Be Fully Stochastic? | Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth |
| 2023 | AISTATS | Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. | Rajeev Verma, Daniel Barrejn, Eric T. Nalisnick |
| 2023 | ICLR | Sampling-based inference for large linear models, with application to linearised Laplace. | Javier Antorn, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, Jos Miguel Hernndez-Lobato |
| 2023 | UAI | Exploiting Inferential Structure in Neural Processes. | Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick |
| 2022 | DSN | On the impact of non-IID data on the performance and fairness of differentially private federated learning. | Saba Amiri, Adam Belloum, Eric T. Nalisnick, Sander Klous, Leon Gommans |
| 2022 | ICML | Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. | Javier Antorn, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, Jos Miguel Hernndez-Lobato |
| 2022 | ICML | Calibrated Learning to Defer with One-vs-All Classifiers. | Rajeev Verma, Eric T. Nalisnick |
| 2021 | AISTATS | Predictive Complexity Priors. | Eric T. Nalisnick, Jonathan Gordon, Jos Miguel Hernndez-Lobato |
| 2021 | ICML | Bayesian Deep Learning via Subnetwork Inference. | Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorn, Jos Miguel Hernndez-Lobato |
| 2019 | ICLR | Do Deep Generative Models Know What They Don't Know? | Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Grr, Balaji Lakshminarayanan |
| 2019 | ICML | Dropout as a Structured Shrinkage Prior. | Eric T. Nalisnick, Jos Miguel Hernndez-Lobato, Padhraic Smyth |
| 2019 | ICML | Hybrid Models with Deep and Invertible Features. | Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Grr, Balaji Lakshminarayanan |
| 2018 | AISTATS | Learning Priors for Invariance. | Eric T. Nalisnick, Padhraic Smyth |
| 2018 | ICLR | The Effectiveness of a two-Layer Neural Network for Recommendations. | Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric T. Nalisnick, Leo Dirac, Runfei Luo |
| 2017 | ICLR | Stick-Breaking Variational Autoencoders. | Eric T. Nalisnick, Padhraic Smyth |
| 2017 | ICLR | Variational Reference Priors. | Eric T. Nalisnick, Padhraic Smyth |
| 2017 | UAI | Learning Approximately Objective Priors. | Eric T. Nalisnick, Padhraic Smyth |
| 2016 | AAAI | Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. | Jihyun Park, Margaret Blume-Kohout, Ralf Krestel, Eric T. Nalisnick, Padhraic Smyth |
| 2016 | WWW | Improving Document Ranking with Dual Word Embeddings. | Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana |
| 2013 | ACL | Character-to-Character Sentiment Analysis in Shakespeare's Plays. | Eric T. Nalisnick, Henry S. Baird |
| 2013 | ICDAR | Extracting Sentiment Networks from Shakespeare's Plays. | Eric T. Nalisnick, Henry S. Baird |