Konrad P. Kording

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 24

Top Topics

Variational Inference

Causal Discovery

Top Venues

Trans. Mach. Learn. Res.

Publications

Felipe Parodi, Jordan Matelsky, Alejandra Regla-Vargas, Elizabeth Foglia, Charis Lim, Danielle Weinberg, Konrad P. Kording, Heidi Herrick, Michael Platt
Vision-language models for decoding provider attention during neonatal resuscitation. CoRR (2024)
Jordan K. Matelsky, Lyle H. Ungar, Konrad P. Kording
Empirical influence functions to understand the logic of fine-tuning. CoRR (2024)
Ben Baker, Tony Liu, Jordan Matelsky, Felipe Parodi, Brett Mensh, John W. Krakauer, Konrad P. Kording
Computational kinematics of dance: distinguishing hip hop genres. Frontiers Robotics AI 11 (2024)
Konrad P. Kording
Neural decoding for BCI: the objectives, principles and the future. BCI (2024)
Tony Liu, Lyle H. Ungar, Konrad P. Kording, Morgan McGuire
Measuring Causal Effects of Civil Communication without Randomization. ICWSM (2024)
Marie A. R. Manalili, Amy Pearson, Justin Sulik, Louise Creechan, Mahmoud Elsherif, Inika Murkumbi, Flavio Azevedo, Kathryn L. Bonnen, Judy S. Kim, Konrad P. Kording, Julie J. Lee, Manifold Obscura, Steven K. Kapp, Jan Philipp Röer, Talia Morstead
From Puzzle to Progress: How Engaging With Neurodiversity Can Improve Cognitive Science. Cogn. Sci. 47 (2) (2023)
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio
Tackling Climate Change with Machine Learning. ACM Comput. Surv. 55 (2) (2023)
Tony Liu, Patrick N. Lawlor, Lyle H. Ungar, Konrad P. Kording, Rahul Ladhania
Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery. Trans. Mach. Learn. Res. 2023 (2023)
Ari S. Benjamin, Konrad P. Kording
A role for cortical interneurons as adversarial discriminators. PLoS Comput. Biol. 19 (9) (2023)
Jordan K. Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange, Konrad P. Kording
A large language model-assisted education tool to provide feedback on open-ended responses. CoRR (2023)
Xinyue Wang, Konrad P. Kording
Learning domain-specific causal discovery from time series. Trans. Mach. Learn. Res. 2023 (2023)
Janaki Sheth, Jared S. Collina, Konrad P. Kording, Yale Cohen, Maria N. Geffen
The Interplay of Relevance, Sensory Uncertainty and Statistical Learning Influences Auditory Categorization. CogSci (2023)
Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
Prospective Learning: Principled Extrapolation to the Future. CoLLAs (2023)
Richard D. Lange, David Rolnick, Konrad P. Kording
Clustering units in neural networks: upstream vs downstream information. Trans. Mach. Learn. Res. 2022 (2022)
Xinyue Wang, Konrad P. Kording
Meta-learning Causal Discovery. CoRR (2022)
Richard D. Lange, David S. Rolnick, Konrad P. Kording
Clustering units in neural networks: upstream vs downstream information. CoRR (2022)
Tony Liu, Patrick N. Lawlor, Lyle Ungar, Konrad P. Kording
Data-driven exclusion criteria for instrumental variable studies. CLeaR (2022)
Joshua T. Vogelstein, Timothy Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael Platt, Lyle Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang
Prospective Learning: Back to the Future. CoRR (2022)
Richard D. Lange, Jordan Matelsky, Xinyue Wang, Devin Kwok, David S. Rolnick, Konrad P. Kording
Neural Networks as Paths through the Space of Representations. CoRR (2022)
Jordan Lei, Ari S. Benjamin, Konrad P. Kording
Object Based Attention Through Internal Gating. CoRR (2021)
Tony Liu, Lyle H. Ungar, Konrad P. Kording
Quantifying causality in data science with quasi-experiments. Nat. Comput. Sci. 1 (1) (2021)
Ari S. Benjamin, Konrad P. Kording
An adversarial algorithm for variational inference with a new role for acetylcholine. CoRR (2020)
David Rolnick, Konrad P. Kording
Reverse-engineering deep ReLU networks. ICML (2020)
Wei Shi Wang, Rochelle Mendonca, Konrad P. Kording, Mikael Avery, Michelle J. Johnson
Towards Data-Driven Autonomous Robot-Assisted Physical Rehabilitation Therapy. ICORR (2019)