Daniel Hsu

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 70

Top Topics

Logistic Regression

Differential Privacy

Weakly Supervised

Top Venues

J. Mach. Learn. Res.

Publications

Samuel Deng, Daniel Hsu
Multi-group Learning for Hierarchical Groups. CoRR (2024)
Clayton Sanford, Daniel Hsu, Matus Telgarsky
Transformers, parallel computation, and logarithmic depth. CoRR (2024)
Daniel Hsu, Arya Mazumdar
On the sample complexity of parameter estimation in logistic regression with normal design. COLT (2024)
Samuel Deng, Daniel Hsu, Jingwen Liu
Group-wise oracle-efficient algorithms for online multi-group learning. CoRR (2024)
Daniel Hsu, Jizhou Huang, Brendan Juba
Distribution-Specific Auditing for Subgroup Fairness. FORC (2024)
Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano
Seasonality Patterns in 311-Reported Foodborne Illness Cases and Machine Learning-Identified Indications of Foodborne Illnesses from Yelp Reviews, New York City, 2022-2023. CoRR (2024)
Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products. CoRR (2023)
Daniel Hsu, Arya Mazumdar
On the sample complexity of estimation in logistic regression. CoRR (2023)
Samuel Deng, Navid Ardeshir, Daniel Hsu
Group conditional validity via multi-group learning. CoRR (2023)
Clayton Sanford, Daniel Hsu, Matus Telgarsky
Representational Strengths and Limitations of Transformers. CoRR (2023)
Christopher J. Tosh, Daniel Hsu
Simple and near-optimal algorithms for hidden stratification and multi-group learning. ICML (2022)
Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal
Learning Tensor Representations for Meta-Learning. CoRR (2022)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Unbiased estimators for random design regression. J. Mach. Learn. Res. 23 (2022)
Rishabh Dudeja, Daniel Hsu
Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity. CoRR (2022)
Clayton Sanford, Navid Ardeshir, Daniel Hsu
Intrinsic dimensionality and generalization properties of the R-norm inductive bias. CoRR (2022)
Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal
Learning Tensor Representations for Meta-Learning. AISTATS (2022)
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Generalization bounds via distillation. CoRR (2021)
Daniel Hsu, Vidya Muthukumar, Ji Xu
On the proliferation of support vectors in high dimensions. AISTATS (2021)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive Estimation Reveals Topic Posterior Information to Linear Models. J. Mach. Learn. Res. 22 (2021)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive learning, multi-view redundancy, and linear models. ALT (2021)
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR (2021)
Ivy Cao, Zizhou Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Quantifying the Effects of COVID-19 on Restaurant Reviews. SocialNLP@NAACL (2021)
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Generalization bounds via distillation. ICLR (2021)
Ji Xu, Arian Maleki, Kamiar Rahnama Rad, Daniel Hsu
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression. IEEE Trans. Inf. Theory 67 (9) (2021)
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai
Classification vs regression in overparameterized regimes: Does the loss function matter? J. Mach. Learn. Res. 22 (2021)
Rishabh Dudeja, Daniel Hsu
Statistical Query Lower Bounds for Tensor PCA. J. Mach. Learn. Res. 22 (2021)
Christopher Tosh, Daniel Hsu
Simple and near-optimal algorithms for hidden stratification and multi-group learning. CoRR (2021)
Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. EC (2020)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. EMNLP (Findings) (2020)
Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel Hsu
Ensuring Fairness Beyond the Training Data. CoRR (2020)
Arushi Gupta, Daniel Hsu
Parameter identification in Markov chain choice models. Theor. Comput. Sci. 808 (2020)
Daniel Hsu, Vidya Muthukumar, Ji Xu
On the proliferation of support vectors in high dimensions. CoRR (2020)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive learning, multi-view redundancy, and linear models. CoRR (2020)
Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. CoRR (2020)
Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only. CoRR (2020)
Mikhail Belkin, Daniel Hsu, Ji Xu
Two Models of Double Descent for Weak Features. SIAM J. Math. Data Sci. 2 (4) (2020)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. CoRR (2020)
Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only. LOUHI@EMNLP (2020)
Christopher Tosh, Daniel Hsu
Diameter-based Interactive Structure Discovery. AISTATS (2020)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive estimation reveals topic posterior information to linear models. CoRR (2020)
Mikhail Belkin, Daniel Hsu, Ji Xu
Two models of double descent for weak features. CoRR (2019)
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana
Certified Robustness to Adversarial Examples with Differential Privacy. IEEE Symposium on Security and Privacy (2019)
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization. CoRR (2019)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health. CoRR (2019)
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. ICML (2019)
Christopher Tosh, Daniel Hsu
Diameter-based Interactive Structure Search. CoRR (2019)
Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. CoRR (2019)
Kevin Shi, Daniel Hsu, Allison Bishop
A cryptographic approach to black box adversarial machine learning. CoRR (2019)
Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha
Kernel Approximation Methods for Speech Recognition. J. Mach. Learn. Res. 20 (2019)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health. W-NUT@EMNLP (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Correcting the bias in least squares regression with volume-rescaled sampling. AISTATS (2019)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. EMNLP/IJCNLP (1) (2019)
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu
Teaching a black-box learner. ICML (2019)
Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
Privacy accounting and quality control in the sage differentially private ML platform. SOSP (2019)
Ji Xu, Daniel Hsu
How many variables should be entered in a principal component regression equation? CoRR (2019)
Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. ACM SIGOPS Oper. Syst. Rev. 53 (1) (2019)
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. CoRR (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Unbiased estimators for random design regression. CoRR (2019)
Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli
Attribute-efficient learning of monomials over highly-correlated variables. ALT (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Tail bounds for volume sampled linear regression. CoRR (2018)
Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
Reconciling modern machine learning and the bias-variance trade-off. CoRR (2018)
Ji Xu, Daniel Hsu, Arian Maleki
Benefits of over-parameterization with EM. CoRR (2018)
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana
On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning. CoRR (2018)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Correcting the bias in least squares regression with volume-rescaled sampling. CoRR (2018)
Rishabh Dudeja, Daniel Hsu
Learning Single-Index Models in Gaussian Space. COLT (2018)
Mikhail Belkin, Daniel Hsu, Partha Mitra
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. CoRR (2018)
Thomas Effland, Anna Lawson, Sharon Balter, Katelynn Devinney, Vasudha Reddy, HaeNa Waechter, Luis Gravano, Daniel Hsu
Discovering foodborne illness in online restaurant reviews. J. Am. Medical Informatics Assoc. 25 (12) (2018)
Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman
Non-Gaussian information from weak lensing data via deep learning. CoRR (2018)
Arushi Gupta, Daniel Hsu
Parameter identification in Markov chain choice models. ALT (2017)
Arushi Gupta, Daniel Hsu
Parameter identification in Markov chain choice models. CoRR (2017)
Anind K. Dey, Raffay Hamid, Chris Beckmann, Ian Li, Daniel Hsu
a CAPpella: programming by demonstration of context-aware applications. CHI (2004)