PIWG measurement methodology. - Daniel M. Roy 001 (Working Group on Ada Performance Issues, 1990)

Results introduction. - Daniel M. Roy 001 (Working Group on Ada Performance Issues, 1990)

PIWG analysis methodology. - Daniel M. Roy 001; Lakshmi Gupta (Working Group on Ada Performance Issues, 1990)

Recommendations and future trends. - Russell M. Clapp; Trevor N. Mudge; Daniel M. Roy 001 (Working Group on Ada Performance Issues, 1990)

Reporting test results. - Dale J. Gaumer; Daniel M. Roy 001 (Working Group on Ada Performance Issues, 1990)

A Dynamic Technique for Eliminating Buffer Overflow Vulnerabilities (and Other Memory Errors). - Martin C. Rinard; Cristian Cadar; Daniel Dumitran; Daniel M. Roy 001; Tudor Leu (ACSAC, 2004)

Enhancing Server Availability and Security Through Failure-Oblivious Computing. - Martin C. Rinard; Cristian Cadar; Daniel Dumitran; Daniel M. Roy 001; Tudor Leu; William S. Beebee (OSDI, 2004)

Learning annotated hierarchies from relational data. - Daniel M. Roy 001; Charles Kemp; Vikash Mansinghka 001; Joshua B. Tenenbaum (NIPS, 2006)

Efficient Bayesian Task-Level Transfer Learning. - Daniel M. Roy 001; Leslie Pack Kaelbling (IJCAI, 2007)

Bayesian Agglomerative Clustering with Coalescents. - Yee Whye Teh; Hal Daumé III; Daniel M. Roy 001 (NIPS, 2007)

AClass: A simple, online, parallelizable algorithm for probabilistic classification. - Vikash K. Mansinghka; Daniel M. Roy 001; Ryan Rifkin; Joshua B. Tenenbaum (AISTATS, 2007)

The Mondrian Process. - Daniel M. Roy 001; Yee Whye Teh (NIPS, 2008)

Church: a language for generative models. - Noah D. Goodman; Vikash K. Mansinghka; Daniel M. Roy 001; Kallista A. Bonawitz; Joshua B. Tenenbaum (UAI, 2008)

Computable Exchangeable Sequences Have Computable de Finetti Measures. - Cameron E. Freer; Daniel M. Roy 001 (CiE, 2009)

The Infinite Latent Events Model. - David Wingate; Noah D. Goodman; Daniel M. Roy 001; Joshua B. Tenenbaum (UAI, 2009)

Exact and Approximate Sampling by Systematic Stochastic Search. - Vikash Mansinghka 001; Daniel M. Roy 001; Eric Jonas; Joshua B. Tenenbaum (AISTATS, 2009)

Posterior distributions are computable from predictive distributions. - Cameron E. Freer; Daniel M. Roy 001 (AISTATS, 2010)

Bayesian Policy Search with Policy Priors. - David Wingate; Noah D. Goodman; Daniel M. Roy 001; Leslie Pack Kaelbling; Joshua B. Tenenbaum (IJCAI, 2011)

Noncomputable Conditional Distributions. - Nathanael L. Ackerman; Cameron E. Freer; Daniel M. Roy 001 (LICS, 2011)

Complexity of Inference in Latent Dirichlet Allocation. - David A. Sontag; Daniel M. Roy 001 (NIPS, 2011)

Probabilistically Accurate Program Transformations. - Sasa Misailovic; Daniel M. Roy 001; Martin C. Rinard (SAS, 2011)

Computability, inference and modeling in probabilistic programming. - Daniel M. Roy 001 (2011)

Random function priors for exchangeable arrays with applications to graphs and relational data. - James Robert Lloyd; Peter Orbanz; Zoubin Ghahramani; Daniel M. Roy 001 (NIPS, 2012)

Computable de Finetti measures. - Cameron E. Freer; Daniel M. Roy 001 (Ann. Pure Appl. Log., 2012)

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence - Cameron E. Freer; Daniel M. Roy 001; Joshua B. Tenenbaum (CoRR, 2012)

Top-down particle filtering for Bayesian decision trees. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (ICML (3), 2013)

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence. - Cameron E. Freer; Daniel M. Roy 001; Joshua B. Tenenbaum (Turing's Legacy, 2014)

Mondrian Forests: Efficient Online Random Forests. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (NIPS, 2014)

Particle Gibbs for Bayesian Additive Regression Trees. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (AISTATS, 2015)

Training generative neural networks via Maximum Mean Discrepancy optimization. - Gintare Karolina Dziugaite; Daniel M. Roy 001; Zoubin Ghahramani (UAI, 2015)

Neural Network Matrix Factorization. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (CoRR, 2015)

The Class of Random Graphs Arising from Exchangeable Random Measures. - Victor Veitch; Daniel M. Roy 001 (CoRR, 2015)

Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. - Peter Orbanz; Daniel M. Roy 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Mondrian Forests for Large-Scale Regression when Uncertainty Matters. - Balaji Lakshminarayanan; Daniel M. Roy 001; Yee Whye Teh (AISTATS, 2016)

Measuring the reliability of MCMC inference with bidirectional Monte Carlo. - Roger B. Grosse; Siddharth Ancha; Daniel M. Roy 001 (NIPS, 2016)

The Mondrian Kernel. - Matej Balog; Balaji Lakshminarayanan; Zoubin Ghahramani; Daniel M. Roy 001; Yee Whye Teh (UAI, 2016)

A study of the effect of JPG compression on adversarial images. - Gintare Karolina Dziugaite; Zoubin Ghahramani; Daniel M. Roy 001 (CoRR, 2016)

Sampling and Estimation for (Sparse) Exchangeable Graphs. - Victor Veitch; Daniel M. Roy 001 (CoRR, 2016)

Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (UAI, 2017)

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (CoRR, 2017)

On computability and disintegration. - Nathanael L. Ackerman; Cameron E. Freer; Daniel M. Roy 001 (Math. Struct. Comput. Sci., 2017)

The Beta-Bernoulli process and algebraic effects. - Sam Staton; Dario Stein; Hongseok Yang; Nathanael L. Ackerman; Cameron E. Freer; Daniel M. Roy 001 (ICALP, 2018)

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (ICML, 2018)

Data-dependent PAC-Bayes priors via differential privacy. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (NeurIPS, 2018)

On the computability of graphons. - Nathanael L. Ackerman; Jeremy Avigad; Cameron E. Freer; Daniel M. Roy 001; Jason M. Rute (CoRR, 2018)

Algorithmic barriers to representing conditional independence. - Nathanael L. Ackerman; Jeremy Avigad; Cameron E. Freer; Daniel M. Roy 001; Jason M. Rute (LICS, 2019)

Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. - Jeffrey Negrea; Mahdi Haghifam; Gintare Karolina Dziugaite; Ashish Khisti; Daniel M. Roy 001 (NeurIPS, 2019)

Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. - Jun Yang; Shengyang Sun; Daniel M. Roy 001 (NeurIPS, 2019)

The Lottery Ticket Hypothesis at Scale. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (CoRR, 2019)

NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization. - Ali Ramezani-Kebrya; Fartash Faghri; Daniel M. Roy 001 (CoRR, 2019)

Approximations in Probabilistic Programs. - Ekansh Sharma; Daniel M. Roy 001 (CoRR, 2019)

On the Computability of Conditional Probability. - Nathanael L. Ackerman; Cameron E. Freer; Daniel M. Roy 001 (J. ACM, 2019)

Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (ICML, 2020)

Linear Mode Connectivity and the Lottery Ticket Hypothesis. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICML, 2020)

In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. - Jeffrey Negrea; Gintare Karolina Dziugaite; Daniel M. Roy 001 (ICML, 2020)

In search of robust measures of generalization. - Gintare Karolina Dziugaite; Alexandre Drouin; Brady Neal; Nitarshan Rajkumar; Ethan Caballero; Linbo Wang 003; Ioannis Mitliagkas; Daniel M. Roy 001 (NeurIPS, 2020)

Adaptive Gradient Quantization for Data-Parallel SGD. - Fartash Faghri; Iman Tabrizian; Ilia Markov; Dan Alistarh; Daniel M. Roy 001; 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 001; Surya Ganguli (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 001; Gintare Karolina Dziugaite (NeurIPS, 2020)

Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. - Yiding Jiang; Parth Natekar; Manik Sharma; Sumukh K. Aithal; Dhruva Kashyap; Natarajan Subramanyam; Carlos Lassance; Daniel M. Roy 001; Gintare Karolina Dziugaite; Suriya Gunasekar; Isabelle Guyon; Pierre Foret; et al. (NeurIPS (Competition and Demos), 2020)

On the role of data in PAC-Bayes bounds. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Daniel M. Roy 001 (CoRR, 2020)

Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (CoRR, 2020)

Relaxing the I.I.D. Assumption: Adaptive Minimax Optimal Sequential Prediction with Expert Advice. - Blair L. Bilodeau; Jeffrey Negrea; Daniel M. Roy 001 (CoRR, 2020)

Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. - Gintare Karolina Dziugaite; Shai Ben-David; Daniel M. Roy 001 (CoRR, 2020)

On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (CoRR, 2020)

NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. - Yiding Jiang; Pierre Foret; Scott Yak; Daniel M. Roy 001; Hossein Mobahi; Gintare Karolina Dziugaite; Samy Bengio; Suriya Gunasekar; Isabelle Guyon; Behnam Neyshabur (CoRR, 2020)

On the role of data in PAC-Bayes. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Gabriel Arpino; Daniel M. Roy 001 (AISTATS, 2021)

Pruning Neural Networks at Initialization: Why Are We Missing the Mark? - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICLR, 2021)

Towards a Unified Information-Theoretic Framework for Generalization. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (NeurIPS, 2021)

The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization. - Mufan (Bill) Li; Mihai Nica; Daniel M. Roy 001 (NeurIPS, 2021)

Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers. - Jeffrey Negrea; Blair L. Bilodeau; Nicolò Campolongo; Francesco Orabona; Daniel M. Roy 001 (NeurIPS, 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 001 (J. Mach. Learn. Res., 2021)

Understanding Generalization via Leave-One-Out Conditional Mutual Information. - Mahdi Haghifam; Shay Moran; Daniel M. Roy 001; Gintare Karolina Dziugaite (ISIT, 2022)

Adaptively Exploiting d-Separators with Causal Bandits. - Blair L. Bilodeau; Linbo Wang 003; Daniel M. Roy 001 (NeurIPS, 2022)

Pruning's Effect on Generalization Through the Lens of Training and Regularization. - Tian Jin; Michael Carbin; Daniel M. Roy 001; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization. - Mufan (Bill) Li; Mihai Nica; Daniel M. Roy 001 (NeurIPS, 2022)

Statistical Inference with Stochastic Gradient Algorithms. - Jeffrey Negrea; Jun Yang; Haoyue Feng; Daniel M. Roy 001; Jonathan H. Huggins (CoRR, 2022)

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 001; Gintare Karolina Dziugaite (ALT, 2023)

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. - Lorenzo Noci; Chuning Li; Mufan Bill Li; Bobby He; Thomas Hofmann 001; Chris J. Maddison; Daniel M. Roy 001 (CoRR, 2023)

Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. - Idan Attias; Gintare Karolina Dziugaite; Mahdi Haghifam; Roi Livni; Daniel M. Roy 001 (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 001 (ICML, 2024)

Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. - Ekansh Sharma; Devin Kwok; Tom Denton; Daniel M. Roy 001; David Rolnick; Gintare Karolina Dziugaite (ECML/PKDD (7), 2024)

Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. - Idan Attias; Gintare Karolina Dziugaite; Mahdi Haghifam; Roi Livni; Daniel M. Roy 001 (CoRR, 2024)

Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood. - Ziyi Liu; Idan Attias; Daniel M. Roy 001 (CoRR, 2024)

The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse. - Ekansh Sharma; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2024)

Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets. - Nathanael L. Ackerman; Cameron E. Freer; Younesse Kaddar; Jacek Karwowski; Sean K. Moss; Daniel M. Roy 001; Sam Staton; Hongseok Yang (Proc. ACM Program. Lang., 2024)

The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (AISTATS, 2025)

Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. - Alexander Ryabchenko; Idan Attias; Daniel M. Roy 001 (COLT, 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 001; Gintare Karolina Dziugaite (ICLR, 2025)

On the Dichotomy Between Privacy and Traceability in ℓp Stochastic Convex Optimization. - Sasha Voitovych; Mahdi Haghifam; Idan Attias; Gintare Karolina Dziugaite; Roi Livni; Daniel M. Roy 001 (CoRR, 2025)

Leveraging Per-Instance Privacy for Machine Unlearning. - Nazanin Mohammadi Sepahvand; Anvith Thudi; Berivan Isik; Ashmita Bhattacharyya; Nicolas Papernot; Eleni Triantafillou; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2025)