default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDaniel M. Roy 0001
Person information
- affiliation: University of Toronto, Department of Statistical Sciences
- affiliation: University of Toronto, Department of Computer Science (cross-appointment)
- affiliation (former): University of Cambridge, Department of Engineering
- affiliation (former): Massachusetts Institute of Technology, Computer Science and Articial Intelligence Laboratory
Other persons with the same name
- Daniel M. Roy 0002 — Century Computing Inc., Laurel, MD, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j6]Nathanael L. Ackerman
, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang:
Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets. Proc. ACM Program. Lang. 8(POPL): 1819-1849 (2024)- [c57]Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. ICML 2024 - [c56]Ziyi Liu, Idan Attias, Daniel M. Roy:
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals. ICML 2024 - [c55]Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite:
Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. ECML/PKDD (7) 2024: 262-279 - [i53]Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy:
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. CoRR abs/2402.09327 (2024) - [i52]Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite:
Simultaneous linear connectivity of neural networks modulo permutation. CoRR abs/2404.06498 (2024) - [i51]Ziyi Liu, Idan Attias, Daniel M. Roy:
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals. CoRR abs/2407.00950 (2024) - 2023
- [c54]Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite:
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. ALT 2023: 663-706 - [i50]Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Daniel M. Roy:
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. CoRR abs/2306.17759 (2023) - [i49]Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang:
Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets. CoRR abs/2312.17127 (2023) - 2022
- [c53]Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite:
Understanding Generalization via Leave-One-Out Conditional Mutual Information. ISIT 2022: 2487-2492 - [c52]Blair L. Bilodeau, Linbo Wang, Daniel M. Roy:
Adaptively Exploiting d-Separators with Causal Bandits. NeurIPS 2022 - [c51]Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite:
Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS 2022 - [c50]Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. NeurIPS 2022 - [i48]Blair L. Bilodeau, Linbo Wang, Daniel M. Roy:
Adaptively Exploiting d-Separators with Causal Bandits. CoRR abs/2202.05100 (2022) - [i47]Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. CoRR abs/2206.02768 (2022) - [i46]Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite:
Understanding Generalization via Leave-One-Out Conditional Mutual Information. CoRR abs/2206.14800 (2022) - [i45]Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins:
Statistical Inference with Stochastic Gradient Algorithms. CoRR abs/2207.12395 (2022) - [i44]Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite:
Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR abs/2210.13738 (2022) - [i43]Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite:
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. CoRR abs/2212.13556 (2022) - 2021
- [j5]Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy:
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization. J. Mach. Learn. Res. 22: 114:1-114:43 (2021) - [c49]Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy:
On the role of data in PAC-Bayes. AISTATS 2021: 604-612 - [c48]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR 2021 - [c47]Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization. NeurIPS 2021: 7852-7864 - [c46]Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy:
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. NeurIPS 2021: 26237-26249 - [c45]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
Towards a Unified Information-Theoretic Framework for Generalization. NeurIPS 2021: 26370-26381 - [i42]Mufan (Bill) Li, Mihai Nica, Daniel M. Roy:
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization. CoRR abs/2106.04013 (2021) - [i41]Jeffrey Negrea, Blair L. Bilodeau, Nicolò Campolongo, Francesco Orabona, Daniel M. Roy:
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. CoRR abs/2110.14804 (2021) - [i40]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
Towards a Unified Information-Theoretic Framework for Generalization. CoRR abs/2111.05275 (2021) - 2020
- [c44]Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy:
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. ICML 2020: 919-929 - [c43]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML 2020: 3259-3269 - [c42]Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy:
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. ICML 2020: 7263-7272 - [c41]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In search of robust measures of generalization. NeurIPS 2020 - [c40]Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. NeurIPS 2020 - [c39]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS 2020 - [c38]Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite:
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. NeurIPS 2020 - [c37]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:
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. NeurIPS (Competition and Demos) 2020: 170-190 - [i39]Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite:
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. CoRR abs/2004.12983 (2020) - [i38]Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Daniel M. Roy:
On the role of data in PAC-Bayes bounds. CoRR abs/2006.10929 (2020) - [i37]Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy:
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. CoRR abs/2007.01160 (2020) - [i36]Blair L. Bilodeau, Jeffrey Negrea, Daniel M. Roy:
Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice. CoRR abs/2007.06552 (2020) - [i35]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR abs/2009.08576 (2020) - [i34]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In Search of Robust Measures of Generalization. CoRR abs/2010.11924 (2020) - [i33]Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. CoRR abs/2010.12460 (2020) - [i32]Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy:
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. CoRR abs/2010.13764 (2020) - [i31]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR abs/2010.15110 (2020) - [i30]Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy:
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. CoRR abs/2011.02970 (2020) - [i29]Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur:
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. CoRR abs/2012.07976 (2020)
2010 – 2019
- 2019
- [j4]Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
On the Computability of Conditional Probability. J. ACM 66(3): 23:1-23:40 (2019) - [c36]Nathanael L. Ackerman, Jeremy Avigad, Cameron E. Freer, Daniel M. Roy, Jason M. Rute:
Algorithmic barriers to representing conditional independence. LICS 2019: 1-13 - [c35]Jun Yang, Shengyang Sun, Daniel M. Roy:
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. NeurIPS 2019: 10802-10812 - [c34]Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy:
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. NeurIPS 2019: 11013-11023 - [i28]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
The Lottery Ticket Hypothesis at Scale. CoRR abs/1903.01611 (2019) - [i27]Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy:
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization. CoRR abs/1908.06077 (2019) - [i26]Jun Yang, Shengyang Sun, Daniel M. Roy:
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. CoRR abs/1908.07585 (2019) - [i25]Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy:
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. CoRR abs/1911.02151 (2019) - [i24]Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy:
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors. CoRR abs/1912.04265 (2019) - [i23]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR abs/1912.05671 (2019) - [i22]Ekansh Sharma, Daniel M. Roy:
Approximations in Probabilistic Programs. CoRR abs/1912.06791 (2019) - 2018
- [c33]Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
The Beta-Bernoulli process and algebraic effects. ICALP 2018: 141:1-141:15 - [c32]Gintare Karolina Dziugaite, Daniel M. Roy:
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. ICML 2018: 1376-1385 - [c31]Gintare Karolina Dziugaite, Daniel M. Roy:
Data-dependent PAC-Bayes priors via differential privacy. NeurIPS 2018: 8440-8450 - [i21]Nathanael L. Ackerman, Jeremy Avigad, Cameron E. Freer, Daniel M. Roy, Jason M. Rute:
On the computability of graphons. CoRR abs/1801.10387 (2018) - [i20]Gintare Karolina Dziugaite, Daniel M. Roy:
Data-dependent PAC-Bayes priors via differential privacy. CoRR abs/1802.09583 (2018) - [i19]Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
The Beta-Bernoulli process and algebraic effects. CoRR abs/1802.09598 (2018) - 2017
- [j3]Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
On computability and disintegration. Math. Struct. Comput. Sci. 27(8): 1287-1314 (2017) - [c30]Gintare Karolina Dziugaite, Daniel M. Roy:
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. UAI 2017 - [i18]Gintare Karolina Dziugaite, Daniel M. Roy:
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. CoRR abs/1703.11008 (2017) - [i17]Gintare Karolina Dziugaite, Daniel M. Roy:
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy. CoRR abs/1712.09376 (2017) - 2016
- [c29]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS 2016: 1478-1487 - [c28]Roger B. Grosse, Siddharth Ancha, Daniel M. Roy:
Measuring the reliability of MCMC inference with bidirectional Monte Carlo. NIPS 2016: 2451-2459 - [c27]Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh:
The Mondrian Kernel. UAI 2016 - [i16]Roger B. Grosse, Siddharth Ancha, Daniel M. Roy:
Measuring the reliability of MCMC inference with bidirectional Monte Carlo. CoRR abs/1606.02275 (2016) - [i15]Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy:
A study of the effect of JPG compression on adversarial images. CoRR abs/1608.00853 (2016) - [i14]Victor Veitch, Daniel M. Roy:
Sampling and Estimation for (Sparse) Exchangeable Graphs. CoRR abs/1611.00843 (2016) - 2015
- [j2]Peter Orbanz, Daniel M. Roy:
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 437-461 (2015) - [c26]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Particle Gibbs for Bayesian Additive Regression Trees. AISTATS 2015 - [c25]Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani:
Training generative neural networks via Maximum Mean Discrepancy optimization. UAI 2015: 258-267 - [i13]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Particle Gibbs for Bayesian Additive Regression Trees. CoRR abs/1502.04622 (2015) - [i12]Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani:
Training generative neural networks via Maximum Mean Discrepancy optimization. CoRR abs/1505.03906 (2015) - [i11]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR abs/1506.03805 (2015) - [i10]Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
On computability and disintegration. CoRR abs/1509.02992 (2015) - [i9]Gintare Karolina Dziugaite, Daniel M. Roy:
Neural Network Matrix Factorization. CoRR abs/1511.06443 (2015) - [i8]Victor Veitch, Daniel M. Roy:
The Class of Random Graphs Arising from Exchangeable Random Measures. CoRR abs/1512.03099 (2015) - 2014
- [c24]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests: Efficient Online Random Forests. NIPS 2014: 3140-3148 - [p1]Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum:
Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence. Turing's Legacy 2014: 195-252 - [i7]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests: Efficient Online Random Forests. CoRR abs/1406.2673 (2014) - 2013
- [c23]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Top-down particle filtering for Bayesian decision trees. ICML (3) 2013: 280-288 - [i6]Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Top-down particle filtering for Bayesian decision trees. CoRR abs/1303.0561 (2013) - 2012
- [j1]Cameron E. Freer, Daniel M. Roy:
Computable de Finetti measures. Ann. Pure Appl. Log. 163(5): 530-546 (2012) - [c22]James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy:
Random function priors for exchangeable arrays with applications to graphs and relational data. NIPS 2012: 1007-1015 - [i5]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. CoRR abs/1205.2604 (2012) - [i4]Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. CoRR abs/1206.3255 (2012) - [i3]Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum:
Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence. CoRR abs/1212.4799 (2012) - 2011
- [b1]Daniel M. Roy:
Computability, inference and modeling in probabilistic programming. Massachusetts Institute of Technology, Cambridge, MA, USA, 2011 - [c21]David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie Pack Kaelbling, Joshua B. Tenenbaum:
Bayesian Policy Search with Policy Priors. IJCAI 2011: 1565-1570 - [c20]Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
Noncomputable Conditional Distributions. LICS 2011: 107-116 - [c19]David A. Sontag, Daniel M. Roy:
Complexity of Inference in Latent Dirichlet Allocation. NIPS 2011: 1008-1016 - [c18]Sasa Misailovic, Daniel M. Roy, Martin C. Rinard:
Probabilistically Accurate Program Transformations. SAS 2011: 316-333 - 2010
- [c17]Cameron E. Freer, Daniel M. Roy:
Posterior distributions are computable from predictive distributions. AISTATS 2010: 233-240 - [i2]Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
On the computability of conditional probability. CoRR abs/1005.3014 (2010)
2000 – 2009
- 2009
- [c16]Cameron E. Freer, Daniel M. Roy:
Computable Exchangeable Sequences Have Computable de Finetti Measures. CiE 2009: 218-231 - [c15]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. UAI 2009: 607-614 - [c14]Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum:
Exact and Approximate Sampling by Systematic Stochastic Search. AISTATS 2009: 400-407 - [i1]Cameron E. Freer, Daniel M. Roy:
Computable de Finetti measures. CoRR abs/0912.1072 (2009) - 2008
- [c13]Daniel M. Roy, Yee Whye Teh:
The Mondrian Process. NIPS 2008: 1377-1384 - [c12]Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. UAI 2008: 220-229 - 2007
- [c11]Daniel M. Roy, Leslie Pack Kaelbling:
Efficient Bayesian Task-Level Transfer Learning. IJCAI 2007: 2599-2604 - [c10]Yee Whye Teh, Hal Daumé III, Daniel M. Roy:
Bayesian Agglomerative Clustering with Coalescents. NIPS 2007: 1473-1480 - [c9]Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum:
AClass: A simple, online, parallelizable algorithm for probabilistic classification. AISTATS 2007: 315-322 - 2006
- [c8]Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua B. Tenenbaum:
Learning annotated hierarchies from relational data. NIPS 2006: 1185-1192 - 2004
- [c7]Martin C. Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, Tudor Leu:
A Dynamic Technique for Eliminating Buffer Overflow Vulnerabilities (and Other Memory Errors). ACSAC 2004: 82-90 - [c6]Martin C. Rinard, Cristian Cadar, Daniel Dumitran, Daniel M. Roy, Tudor Leu, William S. Beebee:
Enhancing Server Availability and Security Through Failure-Oblivious Computing. OSDI 2004: 303-316
1990 – 1999
- 1990
- [c5]Daniel M. Roy:
PIWG measurement methodology. Working Group on Ada Performance Issues 1990: 72-90 - [c4]Russell M. Clapp, Trevor N. Mudge, Daniel M. Roy:
Recommendations and future trends. Working Group on Ada Performance Issues 1990: 98-110 - [c3]Daniel M. Roy:
Results introduction. Working Group on Ada Performance Issues 1990: 138 - [c2]Dale J. Gaumer, Daniel M. Roy:
Reporting test results. Working Group on Ada Performance Issues 1990: 211-216 - [c1]Daniel M. Roy, Lakshmi Gupta:
PIWG analysis methodology. Working Group on Ada Performance Issues 1990: 217-229 - [e1]Daniel M. Roy:
Proceedings of the Working Group on Ada Performance Issues 1990, Baltimore, Maryland, USA, December 3-6, 1990. ACM 1990, ISBN 978-0-89791-354-6 [contents]
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-02 20:41 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
