Kevin Swersky
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
29
Venues
11
Active years
2010–2026
Best venue rank
A*
Where they publish
Papers
29 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | WACV | Do generative video models understand physical principles? | Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, Robert Geirhos |
| 2024 | ICLR | Directly Fine-Tuning Diffusion Models on Differentiable Rewards. | Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet |
| 2023 | CVPR | CUF: Continuous Upsampling Filters. | Cristina Nader Vasconcelos, A. Cengiz ztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi |
| 2022 | ICDM | Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. | Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra |
| 2022 | ICLR | Data-Driven Offline Optimization for Architecting Hardware Accelerators. | Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine |
| 2021 | ASPLOS | A hierarchical neural model of data prefetching. | Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin |
| 2021 | ICLR | No MCMC for me: Amortized sampling for fast and stable training of energy-based models. | Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud |
| 2021 | ICML | Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. | Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison |
| 2020 | ICLR | Your classifier is secretly an energy based model and you should treat it like one. | Will Grathwohl, Kuan-Chieh Wang, Jrn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky |
| 2020 | ICLR | Learning Execution through Neural Code fusion. | Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi |
| 2020 | ICLR | Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. | Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle |
| 2020 | ICML | An Imitation Learning Approach for Cache Replacement. | Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn |
| 2020 | ICML | Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. | Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier |
| 2020 | UAI | Amortized Bayesian Optimization over Discrete Spaces. | Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy |
| 2019 | ICML | Flexibly Fair Representation Learning by Disentanglement. | Elliot Creager, David Madras, Jrn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel |
| 2018 | ICASSP | Learning Hard Alignments with Variational Inference. | Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly |
| 2018 | ICLR | Meta-Learning for Semi-Supervised Few-Shot Classification. | Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel |
| 2018 | ICML | Learning Memory Access Patterns. | Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan |
| 2015 | ICCV | Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions. | Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov |
| 2015 | ICML | Generative Moment Matching Networks. | Yujia Li, Kevin Swersky, Richard S. Zemel |
| 2015 | ICML | Scalable Bayesian Optimization Using Deep Neural Networks. | Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams |
| 2014 | ICML | Input Warping for Bayesian Optimization of Non-Stationary Functions. | Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams |
| 2013 | ICML | Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. | Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel |
| 2013 | ICML | Learning Fair Representations. | Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork |
| 2012 | AAAI | Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. | Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas |
| 2012 | ICML | Estimating the Hessian by Back-propagating Curvature. | James Martens, Ilya Sutskever, Kevin Swersky |
| 2012 | UAI | Fast Exact Inference for Recursive Cardinality Models. | Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey |
| 2011 | ICML | On Autoencoders and Score Matching for Energy Based Models. | Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas |
| 2010 | ITA | A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. | Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas |