Daniel M. Roy
25 papers · 1999–2025 · 7 venues · across top CS/AI venues
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(11)
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(12)
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(5)
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(141)
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Research topics
Machine Learning
(23)
Optimization & Theory Β· Machine Learning
(21)
Mathematics & Optimization
(10)
Artificial Intelligence
(8)
Bayesian & Probabilistic Β· Machine Learning
(7)
Core Methods
(6)
Deep Learning
(5)
Optimization
(5)
Bayesian & Probabilistic Β· Artificial Intelligence
(4)
Core AI
(4)
Learning Types
(4)
Optimization & Theory Β· Deep Learning
(4)
Application Areas
(3)
Mathematics
(3)
Probability
(3)
Keywords
generalization bound
(7)
posterior distribution
(3)
relational data
(3)
pac-bayes bound
(3)
stochastic gradient descent
(3)
generalization error
(3)
minimax regret
(2)
exchangeable arrays
(2)
conditional mutual information
(2)
online learning
(2)
excess risk
(2)
communication efficiency
(2)
stochastic convex optimization
(2)
mutual information
(2)
information theory
(2)
bayesian inference
(2)
bayesian nonparametrics
(2)
markov chain monte carlo
(2)
gradient descent
(2)
mondrian process
(2)
Papers
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
COLT 2025
Leveraging Per-Instance Privacy for Machine Unlearning
ICML 2025
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
ICLR 2025
The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws
AISTATS 2025
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
ICML 2024
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
ICML 2024
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
NeurIPS 2024
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
ALT 2023
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
COLT 2021
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
JMLR 2021
Adaptive Gradient Quantization for Data-Parallel SGD
NeurIPS 2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
NeurIPS 2020
In search of robust measures of generalization
NeurIPS 2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
NeurIPS 2020
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
NeurIPS 2019
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
NeurIPS 2019
Data-dependent PAC-Bayes priors via differential privacy
NeurIPS 2018
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
NeurIPS 2016
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
AISTATS 2016
Mondrian Forests: Efficient Online Random Forests
NeurIPS 2014
Random function priors for exchangeable arrays with applications to graphs and relational data
NeurIPS 2012
The Mondrian Process
NeurIPS 2008
Bayesian Agglomerative Clustering with Coalescents
NeurIPS 2007
Learning annotated hierarchies from relational data
NeurIPS 2006
AClass: A simple, online, parallelizable algorithm for probabilistic classification
AISTATS 1999