Daniel M. Roy - RankMe

130 papers A* 29A 13B 3C 1Journal 66Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees. CoRR Alexander Ryabchenko, Idan Attias, Daniel M. Roy
2026	J	`jnl`	Detoxifying LLMs via Representation Erasure-Based Preference Optimization. CoRR Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle, Doina Precup, Daniel M. Roy, Gintare Karolina Dziugaite
2025	A*	`conf`	Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. COLT Alexander Ryabchenko, Idan Attias, Daniel M. Roy
2025	J	`jnl`	Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. CoRR Alexander Ryabchenko, Idan Attias, Daniel M. Roy
2025	A*	`conf`	Leveraging Per-Instance Privacy for Machine Unlearning. ICML Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite
2025	J	`jnl`	Leveraging Per-Instance Privacy for Machine Unlearning. CoRR Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite
2025	J	`jnl`	On the Dichotomy Between Privacy and Traceability in ℓ CoRR Sasha Voitovych, Mahdi Haghifam, Idan Attias, Gintare Karolina Dziugaite, Roi Livni, Daniel M. Roy
2025	A*	`conf`	Selective Unlearning via Representation Erasure Using Domain Adversarial Training. ICLR Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle, Doina Precup, James J. Clark, Daniel M. Roy, Gintare Karolina Dziugaite
2025	A	`conf`	The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws. AISTATS Gintare Karolina Dziugaite, Daniel M. Roy
2024	A*	`conf`	Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals. ICML Ziyi Liu, Idan Attias, Daniel M. Roy
2024	J	`jnl`	Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals. CoRR Ziyi Liu, Idan Attias, Daniel M. Roy
2024	J	`jnl`	Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. CoRR Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
2024	A*	`conf`	Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. ICML Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
2024	J	`jnl`	Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets. Proc. ACM Program. Lang. Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang
2024	J	`jnl`	Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood. CoRR Ziyi Liu, Idan Attias, Daniel M. Roy
2024	—	`conf`	Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. ECML/PKDD (7) Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
2024	J	`jnl`	Simultaneous linear connectivity of neural networks modulo permutation. CoRR Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
2024	J	`jnl`	The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse. CoRR Ekansh Sharma, Daniel M. Roy, Gintare Karolina Dziugaite
2023	B	`conf`	Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. ALT Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
2023	J	`jnl`	Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets. CoRR Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang
2023	J	`jnl`	The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. CoRR Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Daniel M. Roy
2022	A*	`conf`	Adaptively Exploiting d-Separators with Causal Bandits. NeurIPS Blair L. Bilodeau, Linbo Wang, Daniel M. Roy
2022	J	`jnl`	Adaptively Exploiting d-Separators with Causal Bandits. CoRR Blair L. Bilodeau, Linbo Wang, Daniel M. Roy
2022	J	`jnl`	Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. CoRR Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
2022	A*	`conf`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Statistical Inference with Stochastic Gradient Algorithms. CoRR Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins
2022	A*	`conf`	The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. NeurIPS Mufan (Bill) Li, Mihai Nica, Daniel M. Roy
2022	J	`jnl`	The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. CoRR Mufan (Bill) Li, Mihai Nica, Daniel M. Roy
2022	B	`conf`	Understanding Generalization via Leave-One-Out Conditional Mutual Information. ISIT Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite
2022	J	`jnl`	Understanding Generalization via Leave-One-Out Conditional Mutual Information. CoRR Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite
2021	A*	`conf`	Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. NeurIPS Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy
2021	J	`jnl`	Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. CoRR Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy
2021	J	`jnl`	NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization. J. Mach. Learn. Res. Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy
2021	A	`conf`	On the role of data in PAC-Bayes. AISTATS Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy
2021	A*	`conf`	Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2021	J	`jnl`	The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization. CoRR Mufan (Bill) Li, Mihai Nica, Daniel M. Roy
2021	A*	`conf`	The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization. NeurIPS Mufan (Bill) Li, Mihai Nica, Daniel M. Roy
2021	A*	`conf`	Towards a Unified Information-Theoretic Framework for Generalization. NeurIPS Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy
2021	J	`jnl`	Towards a Unified Information-Theoretic Framework for Generalization. CoRR Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy
2020	A*	`conf`	Adaptive Gradient Quantization for Data-Parallel SGD. NeurIPS Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya
2020	J	`jnl`	Adaptive Gradient Quantization for Data-Parallel SGD. CoRR Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya
2020	A*	`conf`	Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
2020	J	`jnl`	Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
2020	J	`jnl`	Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. CoRR Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
2020	J	`jnl`	Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. CoRR Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy
2020	A*	`conf`	In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. ICML Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
2020	J	`jnl`	In Search of Robust Measures of Generalization. CoRR Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
2020	A*	`conf`	In search of robust measures of generalization. NeurIPS Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
2020	A*	`conf`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	—	`conf`	Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. NeurIPS (Competition and Demos) Yiding Jiang, Parth Natekar, Manik Sharma, Sumukh K. Aithal, Dhruva Kashyap, Natarajan Subramanyam, Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, Pierre Foret, Scott Yak, Hossein Mobahi, Behnam Neyshabur, Samy Bengio
2020	J	`jnl`	NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. CoRR Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur
2020	J	`jnl`	On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. CoRR Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy
2020	J	`jnl`	On the role of data in PAC-Bayes bounds. CoRR Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Daniel M. Roy
2020	J	`jnl`	Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	J	`jnl`	Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice. CoRR Blair L. Bilodeau, Jeffrey Negrea, Daniel M. Roy
2020	A*	`conf`	Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. NeurIPS Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite
2020	J	`jnl`	Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. CoRR Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite
2020	A*	`conf`	Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. ICML Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy
2019	A*	`conf`	Algorithmic barriers to representing conditional independence. LICS Nathanael L. Ackerman, Jeremy Avigad, Cameron E. Freer, Daniel M. Roy, Jason M. Rute
2019	J	`jnl`	Approximations in Probabilistic Programs. CoRR Ekansh Sharma, Daniel M. Roy
2019	A*	`conf`	Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. NeurIPS Jun Yang, Shengyang Sun, Daniel M. Roy
2019	J	`jnl`	Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. CoRR Jun Yang, Shengyang Sun, Daniel M. Roy
2019	J	`jnl`	In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors. CoRR Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
2019	A*	`conf`	Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. NeurIPS Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
2019	J	`jnl`	Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. CoRR Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
2019	J	`jnl`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2019	J	`jnl`	NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization. CoRR Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy
2019	J	`jnl`	On the Computability of Conditional Probability. J. ACM Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2019	J	`jnl`	The Lottery Ticket Hypothesis at Scale. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2018	A*	`conf`	Data-dependent PAC-Bayes priors via differential privacy. NeurIPS Gintare Karolina Dziugaite, Daniel M. Roy
2018	J	`jnl`	Data-dependent PAC-Bayes priors via differential privacy. CoRR Gintare Karolina Dziugaite, Daniel M. Roy
2018	A*	`conf`	Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. ICML Gintare Karolina Dziugaite, Daniel M. Roy
2018	J	`jnl`	On the computability of graphons. CoRR Nathanael L. Ackerman, Jeremy Avigad, Cameron E. Freer, Daniel M. Roy, Jason M. Rute
2018	A*	`conf`	The Beta-Bernoulli process and algebraic effects. ICALP Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2018	J	`jnl`	The Beta-Bernoulli process and algebraic effects. CoRR Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2017	A	`conf`	Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. UAI Gintare Karolina Dziugaite, Daniel M. Roy
2017	J	`jnl`	Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. CoRR Gintare Karolina Dziugaite, Daniel M. Roy
2017	J	`jnl`	Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy. CoRR Gintare Karolina Dziugaite, Daniel M. Roy
2017	J	`jnl`	On computability and disintegration. Math. Struct. Comput. Sci. Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2016	J	`jnl`	A study of the effect of JPG compression on adversarial images. CoRR Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy
2016	—	`conf`	Measuring the reliability of MCMC inference with bidirectional Monte Carlo. NIPS Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
2016	J	`jnl`	Measuring the reliability of MCMC inference with bidirectional Monte Carlo. CoRR Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
2016	A	`conf`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2016	J	`jnl`	Sampling and Estimation for (Sparse) Exchangeable Graphs. CoRR Victor Veitch, Daniel M. Roy
2016	A	`conf`	The Mondrian Kernel. UAI Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE Trans. Pattern Anal. Mach. Intell. Peter Orbanz, Daniel M. Roy
2015	J	`jnl`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Neural Network Matrix Factorization. CoRR Gintare Karolina Dziugaite, Daniel M. Roy
2015	J	`jnl`	On computability and disintegration. CoRR Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2015	A	`conf`	Particle Gibbs for Bayesian Additive Regression Trees. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Particle Gibbs for Bayesian Additive Regression Trees. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	The Class of Random Graphs Arising from Exchangeable Random Measures. CoRR Victor Veitch, Daniel M. Roy
2015	A	`conf`	Training generative neural networks via Maximum Mean Discrepancy optimization. UAI Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
2015	J	`jnl`	Training generative neural networks via Maximum Mean Discrepancy optimization. CoRR Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
2014	—	`conf`	Mondrian Forests: Efficient Online Random Forests. NIPS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2014	J	`jnl`	Mondrian Forests: Efficient Online Random Forests. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2014	—	`ch.`	Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence. Turing's Legacy Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum
2013	J	`jnl`	Top-down particle filtering for Bayesian decision trees CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2013	—	`conf`	Top-down particle filtering for Bayesian decision trees. ICML (3) Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2012	J	`jnl`	Church: a language for generative models CoRR Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
2012	J	`jnl`	Computable de Finetti measures. Ann. Pure Appl. Log. Cameron E. Freer, Daniel M. Roy
2012	—	`conf`	Random function priors for exchangeable arrays with applications to graphs and relational data. NIPS James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy
2012	J	`jnl`	The Infinite Latent Events Model CoRR David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum
2012	J	`jnl`	Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence CoRR Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum
2011	A*	`conf`	Bayesian Policy Search with Policy Priors. IJCAI David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie Pack Kaelbling, Joshua B. Tenenbaum
2011	—	`conf`	Complexity of Inference in Latent Dirichlet Allocation. NIPS David A. Sontag, Daniel M. Roy
2011	—	`—`	Computability, inference and modeling in probabilistic programming. Daniel M. Roy
2011	A*	`conf`	Noncomputable Conditional Distributions. LICS Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2011	B	`conf`	Probabilistically Accurate Program Transformations. SAS Sasa Misailovic, Daniel M. Roy, Martin C. Rinard
2010	J	`jnl`	On the computability of conditional probability. CoRR Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
2010	A	`conf`	Posterior distributions are computable from predictive distributions. AISTATS Cameron E. Freer, Daniel M. Roy
2009	C	`conf`	Computable Exchangeable Sequences Have Computable de Finetti Measures. CiE Cameron E. Freer, Daniel M. Roy
2009	J	`jnl`	Computable de Finetti measures CoRR Cameron E. Freer, Daniel M. Roy
2009	A	`conf`	Exact and Approximate Sampling by Systematic Stochastic Search. AISTATS Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum
2009	A	`conf`	The Infinite Latent Events Model. UAI David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum
2008	A	`conf`	Church: a language for generative models. UAI Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
2008	—	`conf`	The Mondrian Process. NIPS Daniel M. Roy, Yee Whye Teh
2007	A	`conf`	AClass: A simple, online, parallelizable algorithm for probabilistic classification. AISTATS Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum
2007	—	`conf`	Bayesian Agglomerative Clustering with Coalescents. NIPS Yee Whye Teh, Hal Daumé III, Daniel M. Roy
2007	A*	`conf`	Efficient Bayesian Task-Level Transfer Learning. IJCAI Daniel M. Roy, Leslie Pack Kaelbling
2006	—	`conf`	Learning annotated hierarchies from relational data. NIPS Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua B. Tenenbaum
2004	A	`conf`	A Dynamic Technique for Eliminating Buffer Overflow Vulnerabilities (and Other Memory Errors). ACSAC Martin C. Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, Tudor Leu
2004	A*	`conf`	Enhancing Server Availability and Security Through Failure-Oblivious Computing. OSDI Martin C. Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, Tudor Leu, William S. Beebee
1990	—	`conf`	PIWG analysis methodology. Working Group on Ada Performance Issues Daniel M. Roy, Lakshmi Gupta
1990	—	`conf`	PIWG measurement methodology. Working Group on Ada Performance Issues Daniel M. Roy
1990	—	`ed.`	Proceedings of the Working Group on Ada Performance Issues 1990, Baltimore, Maryland, USA, December 3-6, 1990 Working Group on Ada Performance Issues Daniel M. Roy
1990	—	`conf`	Recommendations and future trends. Working Group on Ada Performance Issues Russell M. Clapp, Trevor N. Mudge, Daniel M. Roy
1990	—	`conf`	Reporting test results. Working Group on Ada Performance Issues Dale J. Gaumer, Daniel M. Roy
1990	—	`conf`	Results introduction. Working Group on Ada Performance Issues Daniel M. Roy