Publications by Group Members

(2022). The Price of Tolerance in Distribution Testing. Proceedings of the 35th Annual Conference on Learning Theory (COLT 2022).

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(2022). Robust Estimation for Random Graphs. Proceedings of the 35th Annual Conference on Learning Theory (COLT 2022).

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(2022). Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. Proceedings of the 39th International Conference on Machine Learning (ICML 2022).

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(2022). A Private and Computationally-Efficient Estimator for Unbounded Gaussians. Proceedings of the 35th Annual Conference on Learning Theory (COLT 2022).

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(2022). Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. arXiv preprint arXiv:2206.02617.

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(2022). Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism. Proceedings of the 54th Annual ACM Symposium on the Theory of Computing (STOC 2022).

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(2022). Calibration with Privacy in Peer Review. Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT 2022).

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(2022). New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. arXiv preprint arXiv:2205.08532.

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(2022). Indiscriminate Data Poisoning Attacks on Neural Networks. arXiv preprint arXiv:2204.09092.

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(2022). Differentially Private Fine-tuning of Language Models. Proceedings of the 10th International Conference on Learning Representations (ICLR 2022).

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(2022). The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022).

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(2021). Remember What You Want to Forget: Algorithms for Machine Unlearning. Advances in Neural Information Processing Systems 34 (NeurIPS 2021).

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(2021). Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. Advances in Neural Information Processing Systems 34 (NeurIPS 2021).

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(2021). Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence. Advances in Neural Information Processing Systems 34 (NeurIPS 2021).

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(2021). Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy. arXiv preprint arXiv:2110.14465.

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(2021). PAPRIKA: Private Online False Discovery Rate Control. Proceedings of the 38th International Conference on Machine Learning (ICML 2021).

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(2021). On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. Proceedings of the 32nd International Conference on Algorithmic Learning Theory (ALT 2021).

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(2021). Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. Proceedings of the 32nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2021).

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(2021). Private Hypothesis Selection. IEEE Transactions on Information Theory.

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(2020). The Discrete Gaussian for Differential Privacy. Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

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(2020). Private Identity Testing for High-Dimensional Distributions. Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

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(2020). CoinPress: Practical Private Mean and Covariance Estimation. Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

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(2020). Privately Learning Markov Random Fields. Proceedings of the 37th International Conference on Machine Learning (ICML 2020).

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(2020). Private Mean Estimation of Heavy-Tailed Distributions. Proceedings of the 33rd Annual Conference on Learning Theory (COLT 2020).

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(2020). Locally Private Hypothesis Selection. Proceedings of the 33rd Annual Conference on Learning Theory (COLT 2020).

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(2020). INSPECTRE: Privately Estimating the Unseen. Journal of Privacy and Confidentiality.

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(2020). A Primer on Private Statistics. arXiv preprint arXiv:2005.00010.

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(2019). Private Hypothesis Selection. Advances in Neural Information Processing Systems 32 (NeurIPS 2019).

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(2019). Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. Advances in Neural Information Processing Systems 32 (NeurIPS 2019).

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(2019). Testing Ising Models. IEEE Transactions on Information Theory.

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(2019). The Structure of Optimal Private Tests for Simple Hypotheses. Proceedings of the 51st Annual ACM Symposium on the Theory of Computing (STOC 2019).

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(2019). Sever: A Robust Meta-Algorithm for Stochastic Optimization. Proceedings of the 36th International Conference on Machine Learning (ICML 2019).

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(2019). Privately Learning High-Dimensional Distributions. Proceedings of the 32nd Annual Conference on Learning Theory (COLT 2019).

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(2019). Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM Journal on Computing.

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(2019). Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. Proceedings of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2019).

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(2018). A Chasm Between Identity and Equivalence Testing with Conditional Queries. Theory of Computing.

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(2018). INSPECTRE: Privately Estimating the Unseen. Proceedings of the 35th International Conference on Machine Learning (ICML 2018).

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(2018). Actively Avoiding Nonsense in Generative Models. Proceedings of the 31st Annual Conference on Learning Theory (COLT 2018).

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(2018). Which Distribution Distances are Sublinearly Testable?. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018).

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(2018). Testing Ising Models. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018).

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(2018). Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018).

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(2017). Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. Advances in Neural Information Processing Systems 30 (NIPS 2017).

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(2017). Priv'IT: Private and Sample Efficient Identity Testing. Proceedings of the 34th International Conference on Machine Learning (ICML 2017).

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(2017). Being Robust (in High Dimensions) Can Be Practical. Proceedings of the 34th International Conference on Machine Learning (ICML 2017).

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(2016). Robust Estimators in High Dimensions without the Computational Intractability. Proceedings of the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2016).

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(2016). A Size-Free CLT for Poisson Multinomials and its Applications. Proceedings of the 48th Annual ACM Symposium on the Theory of Computing (STOC 2016).

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(2015). Optimal Testing for Properties of Distributions (NIPS 2015). Advances in Neural Information Processing Systems 28.

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(2015). On the Structure, Covering, and Learning of Poisson Multinomial Distributions. Proceedings of the 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2015).

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(2015). A Chasm Between Identity and Equivalence Testing with Conditional Queries. Proceedings of the 19th International Workshop of Randomization and Computation (RANDOM 2015).

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(2015). Adaptive Estimation in Weighted Group Testing. Proceedings of the 2015 IEEE International Symposium on Information Theory (ISIT 2015).

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(2014). Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. Proceedings of the 27th Annual Conference on Learning Theory (COLT 2014).

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(2012). An Analysis of One-Dimensional Schelling Segregation. Proceedings of the 44th Annual ACM Symposium on the Theory of Computing (STOC 2012).

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