Shao-Lun Huang

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All published works

Action	Title	Year	Authors
+	The Second-Order Perspectives of Minimax Parameter Estimation in Restricted Space with Weighted Squared Error Loss	2024	Tianren Peng Xinyi Tong Shao-Lun Huang
+	PAC-Bayes Information Bottleneck	2021	Zifeng Wang Shao-Lun Huang Erçan E. Kuruoğlu Jimeng Sun Xi Chen Yefeng Zheng
+ PDF Chat	On the Sample Complexity of HGR Maximal Correlation Functions for Large Datasets	2020	Shao-Lun Huang Xiangxiang Xu
+	On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond	2020	Zifeng Wang Xi Chen Rui Wen Shao-Lun Huang
+	On the Sample Complexity of HGR Maximal Correlation Functions	2019	Shao-Lun Huang Xiangxiang Xu
+	An information-theoretic approach to universal feature selection in high-dimensional inference	2017	Shao-Lun Huang Anuran Makur Lizhong Zheng Gregory W. Wornell

Common Coauthors

Coauthor	Papers Together
Zifeng Wang	2
Xiangxiang Xu	2
Yefeng Zheng	1
Xi Chen	1
Rui Wen	1
Xinyi Tong	1
Gregory W. Wornell	1
Anuran Makur	1
Jimeng Sun	1
Xi Chen	1
Erçan E. Kuruoğlu	1
Lizhong Zheng	1
Tianren Peng	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	A Connection between Correlation and Contingency	1935	H. O. Hirschfeld	2
+	Estimating Optimal Transformations for Multiple Regression and Correlation	1985	Leo Breiman Jerome H. Friedman	2
+	Estimating Optimal Transformations for Multiple Regression and Correlation	1985	Leo Breiman Jerome H. Friedman	2
+ PDF Chat	Linear information coupling problems	2012	Shao–Lun Huang Lizhong Zheng	2
+	The information bottleneck method	2000	Naftali Tishby Fernando C. N. Pereira William Bialek	1
+	Doubly Robust Policy Evaluation and Learning	2011	Miroslav Dudík John Langford Lihong Li	1
+ PDF Chat	Mutual Information and Maximal Correlation as Measures of Dependence	1962	C. B. Bell	1
+ PDF Chat	Spreading of Sets in Product Spaces and Hypercontraction of the Markov Operator	1976	Rudolf Ahlswede Péter Gács	1
+	Diffusion for Global Optimization in $\mathbb{R}^n $	1987	Tzuu-Shuh Chiang Chii-Ruey Hwang Shuenn Jyi Sheu	1
+	Alan Turing and the Central Limit Theorem, S. L. Zabell	2009	S. L. Zabell	1
+	What Is Meant by “Missing at Random”?	2013	Shaun R. Seaman John C. Galati Dan Jackson John B. Carlin	1
+	The Randomized Dependence Coefficient	2013	David López-Paz Philipp Hennig Bernhard Schölkopf	1
+ PDF Chat	Bootstrap Methods: Another Look at the Jackknife	1979	B. Efron	1
+ PDF Chat	Residuals and Influence in Regression	1984	Robert F. Ling R. Dennis Cook Sanford Weisberg	1
+ PDF Chat	The central role of the propensity score in observational studies for causal effects	1983	Paul R. Rosenbaum Donald B. Rubin	1
+	Discrete rényi classifiers	2015	Meisam Razaviyayn Farzan Farnia David Tse	1
+ PDF Chat	Representation Learning: A Review and New Perspectives	2013	Yoshua Bengio Aaron Courville P. M. Durai Raj Vincent	1
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	1
+	On Sequences of Pairs of Dependent Random Variables	1975	H. S. Witsenhausen	1
+	Learning Representations for Counterfactual Inference	2016	Fredrik Johansson Uri Shalit David Sontag	1
+	Rawlsian Fairness for Machine Learning.	2016	Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel Aaron Roth	1
+	PERTURBATION THEORY FOR LINEAR OPERATORS	1968	D. E. Edmunds	1
+	Opening the Black Box of Deep Neural Networks via Information	2017	Ravid Shwartz-Ziv Naftali Tishby	1
+	Understanding Black-box Predictions via Influence Functions	2017	Pang Wei Koh Percy Liang	1
+ PDF Chat	Information Dropout: Learning Optimal Representations Through Noisy Computation	2018	Alessandro Achille Stefano Soatto	1
+	Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation	2017	Carolin Lawrence Artem Sokolov Stefan Riezler	1
+	An information-theoretic approach to universal feature selection in high-dimensional inference	2017	Shao-Lun Huang Anuran Makur Lizhong Zheng Gregory W. Wornell	1
+	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness	2017	Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu	1
+	Delayed Impact of Fair Machine Learning	2018	Lydia T. Liu Sarah Dean Esther Rolf Max Simchowitz Moritz Hardt	1
+	Understanding disentangling in $\beta$-VAE	2018	Christopher Burgess Irina Higgins Arka Pal Löıc Matthey Nick Watters Guillaume Desjardins Alexander Lerchner	1
+	An Information-Theoretic View for Deep Learning	2018	Jingwei Zhang Tongliang Liu Dacheng Tao	1
+	Understanding disentangling in $β$-VAE	2018	Christopher Burgess Irina Higgins Arka Pal Löıc Matthey Nick Watters Guillaume Desjardins Alexander Lerchner	1
+	Learning Models with Uniform Performance via Distributionally Robust Optimization	2018	John C. Duchi Hongseok Namkoong	1
+	Fairness in Recommendation Ranking through Pairwise Comparisons	2019	Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei Yi Wu Lukasz Heldt Zhe Zhao Lichan Hong Ed H.	1
+	Policy Learning for Fairness in Ranking	2019	Ashudeep Singh Thorsten Joachims	1
+ PDF Chat	An Information Theoretic Interpretation to Deep Neural Networks	2022	Xiangxiang Xu Shao–Lun Huang Lizhong Zheng Gregory W. Wornell	1
+	Average Individual Fairness: Algorithms, Generalization and Experiments	2019	Michael Kearns Aaron Roth Saeed Sharifi-Malvajerdi	1
+	Learning Optimal Fair Policies	2018	Razieh Nabi Daniel Malinsky Ilya Shpitser	1
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	1
+	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	2017	Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li Xiuqiang He	1
+	Recommendations as Treatments: Debiasing Learning and Evaluation	2016	Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak Thorsten Joachims	1
+	Estimating individual treatment effect: generalization bounds and algorithms	2016	Uri Shalit Fredrik Johansson David Sontag	1
+ PDF Chat	Generalization Error Bounds for Noisy, Iterative Algorithms	2018	Ankit Pensia Varun Jog Po‐Ling Loh	1
+	Doubly Robust Off-policy Value Evaluation for Reinforcement Learning	2015	Nan Jiang Lihong Li	1
+	Estimating Information Flow in Deep Neural Networks	2018	Ziv Goldfeld E. van den Berg Kristjan Greenewald Igor Melnyk Nam Nguyen Brian Kingsbury Yury Polyanskiy	1
+	Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis	2017	Maxim Raginsky Alexander Rakhlin Matus Telgarsky	1
+	In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning	2014	Behnam Neyshabur Ryota Tomioka Nathan Srebro	1
+ PDF Chat	An Efficient Approach to Informative Feature Extraction from Multimodal Data	2019	Lichen Wang Jiaxiang Wu Shao‐Lun Huang Lizhong Zheng Xiangxiang Xu Lin Zhang Junzhou Huang	1
+	Information-theoretic analysis of generalization capability of learning algorithms	2017	Aolin Xu Maxim Raginsky	1
+	Data-dependent PAC-Bayes priors via differential privacy	2018	Gintare Karolina Dziugaite Daniel M. Roy	1