Roy, Daniel M.

38 publications

COLT 2025 Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-Offs Alexander Ryabchenko, Idan Attias, Daniel M. Roy

ICML 2025 Leveraging Per-Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite

NeurIPS 2025 On Traceability in $\ell_p$ Stochastic Convex Optimization Sasha Voitovych, Mahdi Haghifam, Idan Attias, Gintare Karolina Dziugaite, Roi Livni, Daniel M. Roy

ICLR 2025 Selective Unlearning via Representation Erasure Using Domain Adversarial Training Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle, Doina Precup, James J. Clark, Daniel M. Roy, Gintare Karolina Dziugaite

AISTATS 2025 The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws Gintare Karolina Dziugaite, Daniel M. Roy

ICML 2024 Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations Around Unknown Marginals Ziyi Liu, Idan Attias, Daniel M. Roy

ICMLW 2024 Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations Around Unknown Marginals Ziyi Liu, Idan Attias, Daniel M. Roy

ICML 2024 Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

NeurIPS 2024 Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood Ziyi Liu, Idan Attias, Daniel M. Roy

ECML-PKDD 2024 Simultaneous Linear Connectivity of Neural Networks Modulo Permutation Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite

ICMLW 2024 The Minimax Regret of Sequential Probability Assignment, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood Ziyi Liu, Idan Attias, Daniel M. Roy

ALT 2023 Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization Mahdi Haghifam, Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite

COLT 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent Gergely Neu, Gintare Karolina Dziugaite, Mahdi Haghifam, Daniel M. Roy

JMLR 2021 NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy

NeurIPS 2020 Adaptive Gradient Quantization for Data-Parallel SGD Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya

NeurIPS 2020 Deep Learning Versus Kernel Learning: An Empirical Study of Loss Landscape Geometry and the Time Evolution of the Neural Tangent Kernel Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli

NeurIPS 2020 In Search of Robust Measures of Generalization Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy

NeurIPS 2020 Sharpened Generalization Bounds Based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite

NeurIPSW 2019 Approximations in Probabilistic Programs Ekansh Sharma, Daniel M. Roy

NeurIPS 2019 Fast-Rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes Jun Yang, Shengyang Sun, Daniel M. Roy

NeurIPS 2019 Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy

NeurIPS 2018 Data-Dependent PAC-Bayes Priors via Differential Privacy Gintare Karolina Dziugaite, Daniel M. Roy

UAI 2017 Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite, Daniel M. Roy

NeurIPS 2016 Measuring the Reliability of MCMC Inference with Bidirectional Monte Carlo Roger B Grosse, Siddharth Ancha, Daniel M. Roy

AISTATS 2016 Mondrian Forests for Large-Scale Regression When Uncertainty Matters Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

UAI 2016 The Mondrian Kernel Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

AISTATS 2015 Particle Gibbs for Bayesian Additive Regression Trees Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

UAI 2015 Training Generative Neural Networks via Maximum Mean Discrepancy Optimization Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani

NeurIPS 2014 Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

NeurIPS 2012 Random Function Priors for Exchangeable Arrays with Applications to Graphs and Relational Data James Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy

IJCAI 2011 Bayesian Policy Search with Policy Priors David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie Pack Kaelbling, Joshua B. Tenenbaum

UAI 2009 The Infinite Latent Events Model David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum

UAI 2008 Church: A Language for Generative Models Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum

NeurIPS 2008 The Mondrian Process Daniel M. Roy, Yee W. Teh

AISTATS 2007 AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Josh Tenenbaum

NeurIPS 2007 Bayesian Agglomerative Clustering with Coalescents Yee W. Teh, Hal Daume Iii, Daniel M. Roy

IJCAI 2007 Efficient Bayesian Task-Level Transfer Learning Daniel M. Roy, Leslie Pack Kaelbling

NeurIPS 2006 Learning Annotated Hierarchies from Relational Data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum