Daniel M. Roy
24 papers · 2006–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (17) π£ Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(17)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π€
Dynamic Duo
(11)
π
Keyword Champion
π
Trend Setter
π
Conference Pioneer
ποΈ
Keyword Collector
(110)
π
Century Club
(24)
Conferences
NIPS (14)
ICML (3)
AISTATS (2)
COLT (2)
ALT (1)
ICLR (1)
JMLR (1)
Top co-authors
Research topics
Keywords
generalization bound
(6)
relational data
(3)
posterior distribution
(3)
stochastic gradient descent
(3)
relational datum
(3)
data-parallel training
(2)
gradient quantization
(2)
mondrian forest
(2)
online learning
(2)
exchangeable arrays
(2)
stochastic gradient langevin dynamics
(2)
information-theoretic bound
(2)
communication efficiency
(2)
mutual information
(2)
markov chain monte carlo
(2)
bayesian inference
(2)
bayesian nonparametrics
(2)
mondrian process
(2)
pac-bayes bound
(2)
ensemble learning
(1)
Papers
Leveraging Per-Instance Privacy for Machine Unlearning
ICML 2025
The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws
AISTATS 2025
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
ICLR 2025
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
COLT 2025
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
ICML 2024
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
ICML 2024
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
NIPS 2024
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
ALT 2023
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
JMLR 2021
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
COLT 2021
In search of robust measures of generalization
NIPS 2020
Adaptive Gradient Quantization for Data-Parallel SGD
NIPS 2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
NIPS 2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
NIPS 2020
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
NIPS 2019
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
NIPS 2019
Data-dependent PAC-Bayes priors via differential privacy
NIPS 2018
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
AISTATS 2016
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
NIPS 2016
Mondrian Forests: Efficient Online Random Forests
NIPS 2014
Random function priors for exchangeable arrays with applications to graphs and relational data
NIPS 2012
The Mondrian Process
NIPS 2008
Bayesian Agglomerative Clustering with Coalescents
NIPS 2007
Learning annotated hierarchies from relational data
NIPS 2006