Eric Nalisnick
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques
- Adversarial Robustness in Machine Learning
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Information Systems top 10%
- Information Retrieval and Search Behavior
Papers in
-
- Gaussian Processes and Bayesian Inference 5
- Topic Modeling 4
- Anomaly Detection Techniques and Applications 4
- Machine Learning and Algorithms 3
- Machine Learning and Data Classification 3
- Neural Networks and Applications 2
- Sentiment Analysis and Opinion Mining 2
- Explainable Artificial Intelligence (XAI) 2
- Co-authors
- Rich CaruanaBhaskar MitraNick CraswellHenry S. BairdPadhraic SmythBalaji LakshminarayananJosé Miguel Hernández-LobatoAkihiro Matsukawa
- Journals
- Annual Review of Statistics and Its Application (1 paper)UvA-DARE (University of Amsterdam) (2 papers)arXiv (Cornell University) (2 papers)International Conference on Learning Representations (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Eric Nalisnick
19 papers receiving 195 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 173
- Information Systems 47
- Computer Vision and Pattern Recognition 42
- Signal Processing 7
- Statistical and Nonlinear Physics 8
Countries citing papers authored by Eric Nalisnick
This map shows the geographic impact of Eric Nalisnick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eric Nalisnick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Nalisnick more than expected).
Fields of papers citing papers by Eric Nalisnick
This network shows the impact of papers produced by Eric Nalisnick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eric Nalisnick. The network helps show where Eric Nalisnick may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Eric Nalisnick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 4 | |
| 3 | 2022 | 6 | |
| 4 | 2022 | 7 | |
| 5 | 2021 | 3 | |
| 6 | Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference | 2021 | 6 |
| 7 | Bayesian Batch Active Learning as Sparse Subset Approximation | 2019 | 12 |
| 8 | Dropout as a Structured Shrinkage Prior | 2019 | 4 |
| 9 | Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. | 2019 | 21 |
| 10 | 2019 | 4 | |
| 11 | Learning Priors for Invariance. | 2018 | 4 |
| 12 | On Priors for Bayesian Neural Networks | 2018 | 9 |
| 13 | THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS | 2018 | 2 |
| 14 | Variational Reference Priors | 2017 | 1 |
| 15 | Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. | 2016 | 2 |
| 16 | 2016 | 87 | |
| 17 | Character-to-Character Sentiment Analysis in Shakespeare's Plays | 2013 | 21 |
| 18 | Automatic Methods for Tracking Sentiment Dynamics in Plays | 2013 | 1 |
| 19 | 2013 | 19 |
About Eric Nalisnick
Eric Nalisnick is a scholar working on Artificial Intelligence, Signal Processing, Statistics, Probability and Uncertainty, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 214 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (5 papers), Topic Modeling (4 papers), Anomaly Detection Techniques and Applications (4 papers), Machine Learning and Algorithms (3 papers), Machine Learning and Data Classification (3 papers), Neural Networks and Applications (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). The work is most often cited by research in Artificial Intelligence (173 citations), Information Systems (47 citations), Computer Vision and Pattern Recognition (42 citations), Signal Processing (7 citations) and Statistical and Nonlinear Physics (8 citations). Eric Nalisnick has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Rich Caruana, Bhaskar Mitra, Nick Craswell, Henry S. Baird, Padhraic Smyth, Balaji Lakshminarayanan, José Miguel Hernández-Lobato, Akihiro Matsukawa, Yee Whye Teh and Robert Pinsler. Their work appears in journals such as Annual Review of Statistics and Its Application, UvA-DARE (University of Amsterdam), arXiv (Cornell University), International Conference on Learning Representations and International Conference on Artificial Intelligence and Statistics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.