Swersky, Kevin

38 publications

TMLR 2024 Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T Parisi, Abhishek Kumar, Alexander A Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura A Culp, Lechao Xiao, Maxwell Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

ICLR 2024 Directly Fine-Tuning Diffusion Models on Differentiable Rewards Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet

TMLR 2024 Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Yeqing Li, Shixin Luo, Yasumasa Onoe, Zarana Parekh, Ivana Kajic, Mandy Guo, Wenlei Zhou, Sarah Rosston, Roopal Garg, Hongliang Fei, Jordi Pont-Tuset, Su Wang, Henna Nandwani, Andrew Bunner, Kevin Swersky, David J. Fleet, Oliver Wang, Jason Michael Baldridge

JMLR 2024 Pre-Trained Gaussian Processes for Bayesian Optimization Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CVPR 2023 CUF: Continuous Upsampling Filters Cristina N. Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

TMLR 2023 Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

ICLR 2022 Data-Driven Offline Optimization for Architecting Hardware Accelerators Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

ICLR 2021 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

ICML 2021 Oops I Took a Gradient: Scalable Sampling for Discrete Distributions Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison

UAI 2020 Amortized Bayesian Optimization over Discrete Spaces Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy

ICML 2020 An Imitation Learning Approach for Cache Replacement Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

NeurIPS 2020 Big Self-Supervised Models Are Strong Semi-Supervised Learners Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton

ICLR 2020 Learning Execution Through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

ICLR 2020 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

ICLR 2020 Neural Execution Engines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

NeurIPS 2020 Neural Execution Engines: Learning to Execute Subroutines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

ICML 2020 Optimizing Long-Term Social Welfare in Recommender Systems: A Constrained Matching Approach Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier

ICLR 2020 Your Classifier Is Secretly an Energy Based Model and You Should Treat It like One Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky

ICML 2019 Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

NeurIPS 2019 Graph Normalizing Flows Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

ICML 2018 Learning Memory Access Patterns Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan

ICLR 2018 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

NeurIPS 2017 Prototypical Networks for Few-Shot Learning Jake Snell, Kevin Swersky, Richard Zemel

ICLR 2016 The Variational Fair Autoencoder Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel

ICML 2015 Generative Moment Matching Networks Yujia Li, Kevin Swersky, Rich Zemel

ICCV 2015 Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions Jimmy Lei Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov

ICML 2015 Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams

ICML 2014 Input Warping for Bayesian Optimization of Non-Stationary Functions Jasper Snoek, Kevin Swersky, Rich Zemel, Ryan Adams

ICML 2013 Learning Fair Representations Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork

NeurIPS 2013 Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams

ICML 2013 Stochastic K-Neighborhood Selection for Supervised and Unsupervised Learning Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Rich Zemel

NeurIPS 2012 Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan Salakhutdinov, Ryan P. Adams

ICML 2012 Estimating the Hessian by Back-Propagating Curvature James Martens, Ilya Sutskever, Kevin Swersky

UAI 2012 Fast Exact Inference for Recursive Cardinality Models Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey

AAAI 2012 Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas

NeurIPS 2012 Probabilistic N-Choose-K Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams

ICML 2011 On Autoencoders and Score Matching for Energy Based Models Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas

AISTATS 2010 Inductive Principles for Restricted Boltzmann Machine Learning Benjamin Marlin, Kevin Swersky, Bo Chen, Nando Freitas