Kevin Swersky

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 68

Top Topics

Diffusion Models

Top Venues

Publications

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
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation. CoRR (2024)
Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi
Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR (2024)
Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet
Directly Fine-Tuning Diffusion Models on Differentiable Rewards. ICLR (2024)
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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024)
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
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models. CoRR (2024)
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. 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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR (2023)
Kevin Clark, Paul Vicol, Kevin Swersky, David J. Fleet
Directly Fine-Tuning Diffusion Models on Differentiable Rewards. CoRR (2023)
Cristina N. Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
CUF: Continuous Upsampling Filters. CVPR (2023)
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. Trans. Mach. Learn. Res. 2023 (2023)
Paul Vicol, Zico Kolter, Kevin Swersky
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. CoRR (2023)
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
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR (2023)
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR (2022)
Cristina Vasconcelos, Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
CUF: Continuous Upsampling Filters. CoRR (2022)
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM (2022)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR (2022)
Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan
Learning to Improve Code Efficiency. CoRR (2022)
Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
Pre-training helps Bayesian optimization too. CoRR (2022)
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR (2021)
Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin
A hierarchical neural model of data prefetching. ASPLOS (2021)
Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR (2021)
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR (2021)
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR (2021)
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML (2021)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR (2021)
Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon
Apollo: Transferable Architecture Exploration. CoRR (2021)
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS (2020)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
Your classifier is secretly an energy based model and you should treat it like one. ICLR (2020)
Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi
Human 3D keypoints via spatial uncertainty modeling. CoRR (2020)
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
Learning Execution through Neural Code fusion. ICLR (2020)
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Neural Execution Engines: Learning to Execute Subroutines. NeurIPS (2020)
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR (2020)
Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy
Amortized Bayesian Optimization over Discrete Spaces. UAI (2020)
Micha Livne, Kevin Swersky, David J. Fleet
SentenceMIM: A Latent Variable Language Model. CoRR (2020)
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
Big Self-Supervised Models are Strong Semi-Supervised Learners. CoRR (2020)
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
An Imitation Learning Approach for Cache Replacement. ICML (2020)
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
An Imitation Learning Approach for Cache Replacement. CoRR (2020)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML (2020)
Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin
Learned Hardware/Software Co-Design of Neural Accelerators. CoRR (2020)
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Neural Execution Engines: Learning to Execute Subroutines. CoRR (2020)
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR (2020)
Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
Learning Sparse Networks Using Targeted Dropout. CoRR (2019)
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
Graph Normalizing Flows. NeurIPS (2019)
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
Learning Execution through Neural Code Fusion. CoRR (2019)
Micha Livne, Kevin Swersky, David J. Fleet
High Mutual Information in Representation Learning with Symmetric Variational Inference. CoRR (2019)
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
Graph Normalizing Flows. CoRR (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. ICML (2019)
Micha Livne, Kevin Swersky, David J. Fleet
MIM: Mutual Information Machine. CoRR (2019)
Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
Neural Networks for Modeling Source Code Edits. CoRR (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. CoRR (2019)
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR (2019)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR (2019)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR (2018)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) (2018)
Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. ICASSP (2018)
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
Learning Memory Access Patterns. ICML (2018)
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
Learning Memory Access Patterns. CoRR (2018)
Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
An online sequence-to-sequence model for noisy speech recognition. CoRR (2017)
Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. CoRR (2017)
Jake Snell, Kevin Swersky, Richard S. Zemel
Prototypical Networks for Few-shot Learning. NIPS (2017)
Jake Snell, Kevin Swersky, Richard S. Zemel
Prototypical Networks for Few-shot Learning. CoRR (2017)
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel
The Variational Fair Autoencoder. ICLR (2016)
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas
Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104 (1) (2016)
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
Scalable Bayesian Optimization Using Deep Neural Networks. ICML (2015)
Yujia Li, Kevin Swersky, Richard S. Zemel
Generative Moment Matching Networks. ICML (2015)
Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions. CoRR (2015)
Yujia Li, Kevin Swersky, Richard S. Zemel
Generative Moment Matching Networks. CoRR (2015)
Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions. ICCV (2015)
Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR (2014)
Yujia Li, Kevin Swersky, Richard S. Zemel
Learning unbiased features. CoRR (2014)
Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML (2014)
Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
Freeze-Thaw Bayesian Optimization. CoRR (2014)
Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) (2013)
Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
Multi-Task Bayesian Optimization. NIPS (2013)
Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork
Learning Fair Representations. ICML (3) (2013)
James Martens, Ilya Sutskever, Kevin Swersky
Estimating the Hessian by Back-propagating Curvature. ICML (2012)
Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
Cardinality Restricted Boltzmann Machines. NIPS (2012)
Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas
Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. AAAI (2012)
Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
Fast Exact Inference for Recursive Cardinality Models CoRR (2012)
Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
Fast Exact Inference for Recursive Cardinality Models. UAI (2012)
Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey
Probabilistic n-Choose-k Models for Classification and Ranking. NIPS (2012)
Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas
On Autoencoders and Score Matching for Energy Based Models. ICML (2011)
Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. ITA (2010)
Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas
Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS (2010)