A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. - Kevin Swersky; Bo Chen 019; Benjamin M. Marlin; Nando de Freitas (ITA, 2010)

Inductive Principles for Restricted Boltzmann Machine Learning. - Benjamin M. Marlin; Kevin Swersky; Bo Chen 019; Nando de Freitas (AISTATS, 2010)

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

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

Estimating the Hessian by Back-propagating Curvature. - James Martens; Ilya Sutskever; Kevin Swersky (ICML, 2012)

Probabilistic n-Choose-k Models for Classification and Ranking. - Kevin Swersky; Daniel Tarlow; Ryan P. Adams; Richard S. Zemel; Brendan J. Frey (NIPS, 2012)

Cardinality Restricted Boltzmann Machines. - Kevin Swersky; Daniel Tarlow; Ilya Sutskever; Ruslan Salakhutdinov; Richard S. Zemel; Ryan P. Adams (NIPS, 2012)

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

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. - Daniel Tarlow; Kevin Swersky; Laurent Charlin; Ilya Sutskever; Richard S. Zemel (ICML (3), 2013)

Learning Fair Representations. - Richard S. Zemel; Yu Wu; Kevin Swersky; Toniann Pitassi; Cynthia Dwork (ICML (3), 2013)

Multi-Task Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (NIPS, 2013)

Input Warping for Bayesian Optimization of Non-Stationary Functions. - Jasper Snoek; Kevin Swersky; Richard S. Zemel; Ryan P. Adams (ICML, 2014)

Learning unbiased features. - Yujia Li 001; Kevin Swersky; Richard S. Zemel (CoRR, 2014)

Freeze-Thaw Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (CoRR, 2014)

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

Generative Moment Matching Networks. - Yujia Li 001; Kevin Swersky; Richard S. Zemel (ICML, 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 (ICML, 2015)

The Variational Fair Autoencoder. - Christos Louizos; Kevin Swersky; Yujia Li 001; Max Welling; Richard S. Zemel (ICLR, 2016)

Taking the Human Out of the Loop: A Review of Bayesian Optimization. - Bobak Shahriari; Kevin Swersky; Ziyu Wang 001; Ryan P. Adams; Nando de Freitas (Proc. IEEE, 2016)

Prototypical Networks for Few-shot Learning. - Jake Snell; Kevin Swersky; Richard S. Zemel (NIPS, 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)

Improving Bayesian Optimization for Machine Learning using Expert Priors. - Kevin Swersky (2017)

Learning Hard Alignments with Variational Inference. - Dieterich Lawson; Chung-Cheng Chiu; George Tucker; Colin Raffel; Kevin Swersky; Navdeep Jaitly (ICASSP, 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 (ICLR (Poster), 2018)

Learning Memory Access Patterns. - Milad Hashemi; Kevin Swersky; Jamie A. Smith; Grant Ayers; Heiner Litz; Jichuan Chang; Christos Kozyrakis; Parthasarathy Ranganathan (ICML, 2018)

Flexibly Fair Representation Learning by Disentanglement. - Elliot Creager; David Madras; Jörn-Henrik Jacobsen; Marissa A. Weis; Kevin Swersky; Toniann Pitassi; Richard S. Zemel (ICML, 2019)

Graph Normalizing Flows. - Jenny Liu; Aviral Kumar; Jimmy Ba; Jamie Kiros; Kevin Swersky (NeurIPS, 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)

Neural Networks for Modeling Source Code Edits. - Rui Zhao; David Bieber; Kevin Swersky; Daniel Tarlow (CoRR, 2019)

Learning Sparse Networks Using Targeted Dropout. - Aidan N. Gomez; Ivan Zhang; Kevin Swersky; Yarin Gal; Geoffrey E. Hinton (CoRR, 2019)

MIM: Mutual Information Machine. - Micha Livne; Kevin Swersky; David J. Fleet (CoRR, 2019)

High Mutual Information in Representation Learning with Symmetric Variational Inference. - Micha Livne; Kevin Swersky; David J. Fleet (CoRR, 2019)

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 002; Kevin Swersky (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)

An Imitation Learning Approach for Cache Replacement. - Evan Zheran Liu; Milad Hashemi; Kevin Swersky; Parthasarathy Ranganathan; Junwhan Ahn (ICML, 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 (ICML, 2020)

Big Self-Supervised Models are Strong Semi-Supervised Learners. - Ting Chen 007; Simon Kornblith; Kevin Swersky; Mohammad Norouzi 002; Geoffrey E. Hinton (NeurIPS, 2020)

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

Amortized Bayesian Optimization over Discrete Spaces. - Kevin Swersky; Yulia Rubanova; David Dohan; Kevin Murphy 002 (UAI, 2020)

SentenceMIM: A Latent Variable Language Model. - Micha Livne; Kevin Swersky; David J. Fleet (CoRR, 2020)

Learned Hardware/Software Co-Design of Neural Accelerators. - Zhan Shi; Chirag Sakhuja; Milad Hashemi; Kevin Swersky; Calvin Lin (CoRR, 2020)

Human 3D keypoints via spatial uncertainty modeling. - Francis Williams; Or Litany; Avneesh Sud; Kevin Swersky; Andrea Tagliasacchi (CoRR, 2020)

A hierarchical neural model of data prefetching. - Zhan Shi; Akanksha Jain; Kevin Swersky; Milad Hashemi; Parthasarathy Ranganathan; Calvin Lin (ASPLOS, 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 002; Kevin Swersky; David Duvenaud (ICLR, 2021)

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. - Will Grathwohl; Kevin Swersky; Milad Hashemi; David Duvenaud; Chris J. Maddison (ICML, 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)

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)

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. - Yujun Yan; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra (ICDM, 2022)

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

Learning to Improve Code Efficiency. - Binghong Chen; Daniel Tarlow; Kevin Swersky; Martin Maas 001; Pablo Ariel Heiber; Ashish Naik; Milad Hashemi; Parthasarathy Ranganathan (CoRR, 2022)

CUF: Continuous Upsampling Filters. - Cristina Nader Vasconcelos; A. Cengiz Öztireli; Mark J. Matthews; Milad Hashemi; Kevin Swersky; Andrea Tagliasacchi (CVPR, 2023)

Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. - Paul Vicol; Zico Kolter; Kevin Swersky (CoRR, 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; et al. (CoRR, 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)

Directly Fine-Tuning Diffusion Models on Differentiable Rewards. - Kevin Clark; Paul Vicol; Kevin Swersky; David J. Fleet (ICLR, 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 001; Angeliki Lazaridou; Orhan Firat; Noah Fiedel (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)

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; et al. (CoRR, 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)

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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 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; et al. (Trans. Mach. Learn. Res., 2024)

Do generative video models learn physical principles from watching videos? - Saman Motamed; Laura Culp; Kevin Swersky; Priyank Jaini; Robert Geirhos (CoRR, 2025)