Kevin Swersky

According to our database, Kevin Swersky authored at least 63 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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 Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

Trans. Mach. Learn. Res., 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

J. Mach. Learn. Res., 2024

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability.

Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith

CoRR, 2024

Exploring and Benchmarking the Planning Capabilities of Large Language Models.

Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi

CoRR, 2024

Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation.

Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi, Azade Nova, Javier Snaider, Hanie Sedghi, Aaron T. Parisi, Michael Collins, Angeliki Lazaridou, Orhan Firat, Noah Fiedel

CoRR, 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, Shixin Luo, Zarana Parekh, Andrew Bunner, Hongliang Fei, Roopal Garg, Mandy Guo, Ivana Kajic, Yeqing Li, Henna Nandwani, Jordi Pont-Tuset, Yasumasa Onoe, Sarah Rosston, Su Wang, Wenlei Zhou, Kevin Swersky, David J. Fleet, Jason M. Baldridge, Oliver Wang

CoRR, 2024

Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks.

Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

Trans. Mach. Learn. Res., 2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single.

Paul Vicol, Zico Kolter, Kevin Swersky

CoRR, 2023

CUF: Continuous Upsampling Filters.

Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Learning to Improve Code Efficiency.

Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

CoRR, 2022

Pre-training helps Bayesian optimization too.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2022

Data-Driven Offline Optimization for Architecting Hardware Accelerators.

Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.

Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2021

Apollo: Transferable Architecture Exploration.

Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon

CoRR, 2021

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.

Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

A hierarchical neural model of data prefetching.

Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin

Proceedings of the ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021

2020

Human 3D keypoints via spatial uncertainty modeling.

Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi

CoRR, 2020

Learned Hardware/Software Co-Design of Neural Accelerators.

Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin

CoRR, 2020

SentenceMIM: A Latent Variable Language Model.

Micha Livne, Kevin Swersky, David J. Fleet

CoRR, 2020

Amortized Bayesian Optimization over Discrete Spaces.

Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Neural Execution Engines: Learning to Execute Subroutines.

Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Big Self-Supervised Models are Strong Semi-Supervised Learners.

Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the 37th International Conference on Machine Learning, 2020

An Imitation Learning Approach for Cache Replacement.

Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Execution through Neural Code fusion.

Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

Proceedings of the 8th International Conference on Learning Representations, 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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

High Mutual Information in Representation Learning with Symmetric Variational Inference.

Micha Livne, Kevin Swersky, David J. Fleet

CoRR, 2019

MIM: Mutual Information Machine.

Micha Livne, Kevin Swersky, David J. Fleet

CoRR, 2019

Learning Sparse Networks Using Targeted Dropout.

Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

CoRR, 2019

Neural Networks for Modeling Source Code Edits.

Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow

CoRR, 2019

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples.

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle

CoRR, 2019

Graph Normalizing Flows.

Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Flexibly Fair Representation Learning by Disentanglement.

Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Learning Memory Access Patterns.

Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan

Proceedings of the 35th International Conference on Machine Learning, 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

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Hard Alignments with Variational Inference.

Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

Improving Bayesian Optimization for Machine Learning using Expert Priors.

Kevin Swersky

PhD thesis, 2017

An online sequence-to-sequence model for noisy speech recognition.

Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly

CoRR, 2017

Prototypical Networks for Few-shot Learning.

Jake Snell, Kevin Swersky, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Taking the Human Out of the Loop: A Review of Bayesian Optimization.

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas

Proc. IEEE, 2016

The Variational Fair Autoencoder.

Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel

Proceedings of the 4th International Conference on Learning Representations, 2016

2015

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

Generative Moment Matching Networks.

Yujia Li, Kevin Swersky, Richard S. Zemel

Proceedings of the 32nd International Conference on Machine Learning, 2015

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions.

Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014

Freeze-Thaw Bayesian Optimization.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

CoRR, 2014

Learning unbiased features.

Yujia Li, Kevin Swersky, Richard S. Zemel

CoRR, 2014

Input Warping for Bayesian Optimization of Non-Stationary Functions.

Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Multi-Task Bayesian Optimization.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning Fair Representations.

Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork

Proceedings of the 30th International Conference on Machine Learning, 2013

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning.

Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Fast Exact Inference for Recursive Cardinality Models.

Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Cardinality Restricted Boltzmann Machines.

Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Probabilistic n-Choose-k Models for Classification and Ranking.

Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Estimating the Hessian by Back-propagating Curvature.

James Martens, Ilya Sutskever, Kevin Swersky

Proceedings of the 29th International Conference on Machine Learning, 2012

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults.

Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

On Autoencoders and Score Matching for Energy Based Models.

Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Inductive Principles for Restricted Boltzmann Machine Learning.

Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

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

Proceedings of the Information Theory and Applications Workshop, 2010