Daniel Hsu - RankMe

94 papers A* 16A 5B 4Journal 63Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Group-realizable multi-group learning by minimizing empirical risk. CoRR Navid Ardeshir, Samuel Deng, Daniel Hsu, Jingwen Liu
2025	J	`jnl`	Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products. SIAM J. Math. Data Sci. Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu
2025	J	`jnl`	Fast attention mechanisms: a tale of parallelism. CoRR Jingwen Liu, Hantao Yu, Clayton Sanford, Alexandr Andoni, Daniel Hsu
2025	J	`jnl`	Investigating the Association Between Text-Based Indications of Foodborne Illness from Yelp Reviews and New York City Health Inspection Outcomes (2023). CoRR Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano
2025	A*	`conf`	Learning Compositional Functions with Transformers from Easy-to-Hard Data. COLT Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu
2025	J	`jnl`	Learning Compositional Functions with Transformers from Easy-to-Hard Data. CoRR Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu
2025	A	`conf`	Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. AISTATS Berfin Simsek, Amire Bendjeddou, Daniel Hsu
2025	J	`jnl`	Survey on Algorithms for multi-index models. CoRR Joan Bruna, Daniel Hsu
2024	—	`conf`	Distribution-Specific Auditing for Subgroup Fairness. FORC Daniel Hsu, Jizhou Huang, Brendan Juba
2024	J	`jnl`	Group-wise oracle-efficient algorithms for online multi-group learning. CoRR Samuel Deng, Daniel Hsu, Jingwen Liu
2024	J	`jnl`	Interactive Machine Teaching by Labeling Rules and Instances. CoRR Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2024	J	`jnl`	Interactive Machine Teaching by Labeling Rules and Instances. Trans. Assoc. Comput. Linguistics Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2024	B	`ed.`	International Conference on Algorithmic Learning Theory, 25-28 February 2024, La Jolla, California, USA. ALT Claire Vernade, Daniel Hsu
2024	J	`jnl`	Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. CoRR Berfin Simsek, Amire Bendjeddou, Daniel Hsu
2024	A*	`conf`	Multi-group Learning for Hierarchical Groups. ICML Samuel Deng, Daniel Hsu
2024	J	`jnl`	Multi-group Learning for Hierarchical Groups. CoRR Samuel Deng, Daniel Hsu
2024	A*	`conf`	On the sample complexity of parameter estimation in logistic regression with normal design. COLT Daniel Hsu, Arya Mazumdar
2024	J	`jnl`	One-layer transformers fail to solve the induction heads task. CoRR Clayton Sanford, Daniel Hsu, Matus Telgarsky
2024	J	`jnl`	Polynomial time auditing of statistical subgroup fairness for Gaussian data. CoRR Daniel Hsu, Jizhou Huang, Brendan Juba
2024	J	`jnl`	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 Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano
2024	A*	`conf`	Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. ICML Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee
2024	J	`jnl`	Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. CoRR Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee
2024	A*	`conf`	Transformers, parallel computation, and logarithmic depth. ICML Clayton Sanford, Daniel Hsu, Matus Telgarsky
2024	J	`jnl`	Transformers, parallel computation, and logarithmic depth. CoRR Clayton Sanford, Daniel Hsu, Matus Telgarsky
2023	J	`jnl`	Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products. CoRR Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu
2023	J	`jnl`	On the sample complexity of estimation in logistic regression. CoRR Daniel Hsu, Arya Mazumdar
2023	J	`jnl`	Representational Strengths and Limitations of Transformers. CoRR Clayton Sanford, Daniel Hsu, Matus Telgarsky
2022	J	`jnl`	Intrinsic dimensionality and generalization properties of the R-norm inductive bias. CoRR Clayton Sanford, Navid Ardeshir, Daniel Hsu
2022	A	`conf`	Learning Tensor Representations for Meta-Learning. AISTATS Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal
2022	J	`jnl`	Learning Tensor Representations for Meta-Learning. CoRR Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal
2022	J	`jnl`	Masked prediction tasks: a parameter identifiability view. CoRR Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski
2022	J	`jnl`	Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. CoRR Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis
2022	A*	`conf`	Simple and near-optimal algorithms for hidden stratification and multi-group learning. ICML Christopher J. Tosh, Daniel Hsu
2022	J	`jnl`	Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity. CoRR Rishabh Dudeja, Daniel Hsu
2022	J	`jnl`	Unbiased estimators for random design regression. J. Mach. Learn. Res. Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
2021	J	`jnl`	Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
2021	J	`jnl`	Classification vs regression in overparameterized regimes: Does the loss function matter? J. Mach. Learn. Res. Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai
2021	J	`jnl`	Consistent Risk Estimation in Moderately High-Dimensional Linear Regression. IEEE Trans. Inf. Theory Ji Xu, Arian Maleki, Kamiar Rahnama Rad, Daniel Hsu
2021	J	`jnl`	Contrastive Estimation Reveals Topic Posterior Information to Linear Models. J. Mach. Learn. Res. Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
2021	B	`conf`	Contrastive learning, multi-view redundancy, and linear models. ALT Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
2021	A*	`conf`	Generalization bounds via distillation. ICLR Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
2021	J	`jnl`	Generalization bounds via distillation. CoRR Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
2021	A*	`conf`	On the Approximation Power of Two-Layer Networks of Random ReLUs. COLT Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis
2021	J	`jnl`	On the Approximation Power of Two-Layer Networks of Random ReLUs. CoRR Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis
2021	A	`conf`	On the proliferation of support vectors in high dimensions. AISTATS Daniel Hsu, Vidya Muthukumar, Ji Xu
2021	—	`conf`	Quantifying the Effects of COVID-19 on Restaurant Reviews. SocialNLP@NAACL Ivy Cao, Zizhou Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2021	J	`jnl`	Simple and near-optimal algorithms for hidden stratification and multi-group learning. CoRR Christopher Tosh, Daniel Hsu
2021	J	`jnl`	Statistical Query Lower Bounds for Tensor PCA. J. Mach. Learn. Res. Rishabh Dudeja, Daniel Hsu
2021	J	`jnl`	Support vector machines and linear regression coincide with very high-dimensional features. CoRR Navid Ardeshir, Clayton Sanford, Daniel Hsu
2020	A*	`conf`	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. EC Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau
2020	J	`jnl`	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. CoRR Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau
2020	J	`jnl`	Contrastive estimation reveals topic posterior information to linear models. CoRR Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
2020	J	`jnl`	Contrastive learning, multi-view redundancy, and linear models. CoRR Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
2020	—	`conf`	Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. EMNLP (Findings) Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2020	J	`jnl`	Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. CoRR Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2020	—	`conf`	Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only. LOUHI@EMNLP Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2020	J	`jnl`	Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only. CoRR Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2020	A	`conf`	Diameter-based Interactive Structure Discovery. AISTATS Christopher Tosh, Daniel Hsu
2020	J	`jnl`	Ensuring Fairness Beyond the Training Data. CoRR Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel Hsu
2020	J	`jnl`	On the proliferation of support vectors in high dimensions. CoRR Daniel Hsu, Vidya Muthukumar, Ji Xu
2020	J	`jnl`	Parameter identification in Markov chain choice models. Theor. Comput. Sci. Arushi Gupta, Daniel Hsu
2020	J	`jnl`	Two Models of Double Descent for Weak Features. SIAM J. Math. Data Sci. Mikhail Belkin, Daniel Hsu, Ji Xu
2019	A*	`conf`	A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. ICML Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
2019	J	`jnl`	A cryptographic approach to black box adversarial machine learning. CoRR Kevin Shi, Daniel Hsu, Allison Bishop
2019	J	`jnl`	A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization. CoRR Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
2019	B	`conf`	Attribute-efficient learning of monomials over highly-correlated variables. ALT Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli
2019	A*	`conf`	Certified Robustness to Adversarial Examples with Differential Privacy. IEEE Symposium on Security and Privacy Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana
2019	A*	`ed.`	Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA COLT Alina Beygelzimer, Daniel Hsu
2019	A	`conf`	Correcting the bias in least squares regression with volume-rescaled sampling. AISTATS Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
2019	J	`jnl`	Diameter-based Interactive Structure Search. CoRR Christopher Tosh, Daniel Hsu
2019	J	`jnl`	How many variables should be entered in a principal component regression equation? CoRR Ji Xu, Daniel Hsu
2019	J	`jnl`	Kernel Approximation Methods for Speech Recognition. J. Mach. Learn. Res. 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
2019	—	`conf`	Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. EMNLP/IJCNLP (1) Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2019	J	`jnl`	Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. CoRR Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2019	J	`jnl`	Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. CoRR Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
2019	J	`jnl`	Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. ACM SIGOPS Oper. Syst. Rev. Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
2019	A*	`conf`	Privacy accounting and quality control in the sage differentially private ML platform. SOSP Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu
2019	A*	`conf`	Teaching a black-box learner. ICML Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu
2019	J	`jnl`	Two models of double descent for weak features. CoRR Mikhail Belkin, Daniel Hsu, Ji Xu
2019	J	`jnl`	Unbiased estimators for random design regression. CoRR Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
2019	—	`conf`	Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health. W-NUT@EMNLP Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2019	J	`jnl`	Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health. CoRR Giannis Karamanolakis, Daniel Hsu, Luis Gravano
2018	J	`jnl`	Benefits of over-parameterization with EM. CoRR Ji Xu, Daniel Hsu, Arian Maleki
2018	J	`jnl`	Correcting the bias in least squares regression with volume-rescaled sampling. CoRR Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
2018	J	`jnl`	Discovering foodborne illness in online restaurant reviews. J. Am. Medical Informatics Assoc. Thomas Effland, Anna Lawson, Sharon Balter, Katelynn Devinney, Vasudha Reddy, HaeNa Waechter, Luis Gravano, Daniel Hsu
2018	A*	`conf`	Learning Single-Index Models in Gaussian Space. COLT Rishabh Dudeja, Daniel Hsu
2018	J	`jnl`	Non-Gaussian information from weak lensing data via deep learning. CoRR Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman
2018	J	`jnl`	On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning. CoRR Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana
2018	J	`jnl`	Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. CoRR Mikhail Belkin, Daniel Hsu, Partha Mitra
2018	J	`jnl`	Reconciling modern machine learning and the bias-variance trade-off. CoRR Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
2018	J	`jnl`	Tail bounds for volume sampled linear regression. CoRR Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
2017	B	`conf`	Parameter identification in Markov chain choice models. ALT Arushi Gupta, Daniel Hsu
2017	J	`jnl`	Parameter identification in Markov chain choice models. CoRR Arushi Gupta, Daniel Hsu
2004	A*	`conf`	a CAPpella: programming by demonstration of context-aware applications. CHI Anind K. Dey, Raffay Hamid, Chris Beckmann, Ian Li, Daniel Hsu