Kevin Swersky
30 papers · 2010–2026 · 9 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (60) π Academic Marathon Β· Full π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Keyword Trendsetter Combo
(5)
π§
Keyword Pioneer
π
Triple Crown
π
Renaissance Researcher
(5)
π₯
Unstoppable Β· Bronze
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The Questioner
β‘
Prolific Year Β· Productive
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Conference Pioneer
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Century Club Β· Bronze
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Trend Setter
ποΈ
Keyword Collector
(151)
Top co-authors
Research topics
Machine Learning
(27)
Deep Learning
(16)
Optimization & Theory
(14)
Artificial Intelligence
(13)
Learning Types Β· Machine Learning
(11)
Architectures
(10)
Core Methods
(9)
Models
(8)
Learning Paradigms
(7)
Application Areas
(5)
Core AI
(5)
Learning Types Β· Deep Learning
(4)
Computer Vision
(2)
Mathematics & Optimization
(2)
Bayesian & Probabilistic
(2)
Keywords
generative model
(5)
bayesian optimization
(5)
gaussian process
(4)
representation learning
(4)
energy-based model
(3)
hyperparameter optimization
(3)
multi-task learning
(3)
transfer learning
(3)
few-shot learning
(3)
neural network
(3)
prototypical network
(2)
distance metric
(2)
metric learning
(2)
restricted boltzmann machine
(2)
acquisition function
(2)
graph neural network
(2)
zero-shot learning
(2)
hyperparameter tuning
(2)
semi-supervised learning
(2)
probabilistic model
(2)
Papers
Do Generative Video Models Understand Physical Principles?
WACV 2026
Directly Fine-Tuning Diffusion Models on Differentiable Rewards
ICLR 2024
Pre-trained Gaussian Processes for Bayesian Optimization
JMLR 2024
CUF: Continuous Upsampling Filters
CVPR 2023
Data-Driven Offline Optimization for Architecting Hardware Accelerators
ICLR 2022
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
ICLR 2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
ICML 2021
Amortized Bayesian Optimization over Discrete Spaces
UAI 2020
An Imitation Learning Approach for Cache Replacement
ICML 2020
Big Self-Supervised Models are Strong Semi-Supervised Learners
NeurIPS 2020
LEARNING EXECUTION THROUGH NEURAL CODE FUSION
ICLR 2020
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
ICLR 2020
Neural Execution Engines: Learning to Execute Subroutines
NeurIPS 2020
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
ICML 2020
Your classifier is secretly an energy based model and you should treat it like one
ICLR 2020
Flexibly Fair Representation Learning by Disentanglement
ICML 2019
Graph Normalizing Flows
NeurIPS 2019
Learning Memory Access Patterns
ICML 2018
Meta-Learning for Semi-Supervised Few-Shot Classification
ICLR 2018
Prototypical Networks for Few-shot Learning
NeurIPS 2017
Generative Moment Matching Networks
ICML 2015
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions
ICCV 2015
Scalable Bayesian Optimization Using Deep Neural Networks
ICML 2015
Input Warping for Bayesian Optimization of Non-Stationary Functions
ICML 2014
Learning Fair Representations
ICML 2013
Multi-Task Bayesian Optimization
NeurIPS 2013
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning
ICML 2013
Cardinality Restricted Boltzmann Machines
NeurIPS 2012
Probabilistic n-Choose-k Models for Classification and Ranking
NeurIPS 2012
Inductive Principles for Restricted Boltzmann Machine Learning
AISTATS 2010