University of Southern California

Machine Learning Center

D. Deng, C. Shahabi, U. Demiryurek, L. Zhu, R. Yu and Y. Liu. Latent Space Model for Road Networks to Predict Time-Varying Traffic. In * ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016)*, 2016

R. Ge, J. D. Lee, and T. Ma. Matrix Completion has No Spurious Local Minima. In * Advances in Neural Information Processing Systems (NIPS 2016)*, 2016

D. Cheng, R. Peng, I. Perros, and Y. Liu. SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. In * Advances in Neural Information Processing Systems (NIPS 2016)*, 2016

S. Gao, G. Ver Steeg, and A. Galstyan. Variational Information Maximization for Feature Selection. In * Advances in Neural Information Processing Systems (NIPS 2016)*, 2016

X. He, K. Xu, D. Kempe and Y. Liu. Learning Influence Functions from Incomplete Observations. In * Advances in Neural Information Processing Systems (NIPS)*, 2016

J. D. Lee, M. Simchowitz, M. I. Jordan, and B. Recht. Gradient Descent Converges to Minimizers. In * Conference on Learning Theory (COLT 2016)*, 2016

I. Diakonikolas, D. Kane, A. Stewart . Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables. In * Conference on Learning Theory (COLT 2016)*, 2016

I. Diakonikolas, G. Kamath, D. Kane, J. Li, A. Moitra, A. Stewart . Robust Estimators in High Dimensions without the Computational Intractability. In * IEEE Symposium on Foundations of Computer Science (FOCS 2016) *, 2016

I Diakonikolas, D. Kane. A New Approach for Testing Properties of Discrete Distributions. In * IEEE Symposium on Foundations of Computer Science (FOCS 2016) *, 2016

S. Tu, R. Boczar, M. Soltanolkotabi, and B. Recht. Low-rank Solutions of Linear Matrix Equations via Procrustes Flow.. In * International Conference on Machine Learning (ICML 2016)*, 2016

J. Acharya, I. Diakonikolas, J. Li, L. Schmidt . Fast Algorithms for Segmented Regression. In * International Conference on Machine Learning (ICML 2016)*, 2016

R. Yu and Y. Liu. Learning from Multiway Data: Simple and Efficient Tensor Regression. In * International Conference on Machine Learning (ICML 2016)*, 2016

Yoon Sik Cho, Emilio Ferrara, Greg Ver Steeg, and Aram Galstyan. Latent Space Models for Multimodal Social Data. In * World Wide Web Conference (WWW 2016)*, 2016

Z. Che, D. Kale, W. Li, M. T. Bahadori, and Y. Liu. Deep Computational Phenotyping. In * ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015)*, 2015

I. Diakonikolas, M. Hardt, L. Schmidt. Differentially Private Learning of Structured Discrete Distributions. In * Advances in Neural Information Processing Systems (NIPS 2015)*, 2015

M. Razaviyayn, F. Farnia, and D. Tse. Discrete Rènyi classifiers. In * Advances in Neural Information Processing Systems (NIPS 2016)*, 2015

S. Gao, G. Ver Steeg, and A. Galstyan. Efficient Estimation of Mutual Information for Strongly Dependent Variables . In * Artificial Intelligence and Statistics Conference (AISTATS 2015)*, 2015

D. Cheng, Y. Cheng, Y. Liu, R. Peng, and S.-H. Teng. Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. In * Conference on Learning Theory (COLT 15)*, 2015

R. Yu, D. Cheng and Y. Liu. Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams. In * International Conference on Machine Learning (ICML 2015)*, 2015

M. T. Bahadori, D. Kale, Y. Fan, and Y. Liu. Functional Subspace Clustering with Application to Time Series. In * International Conference on Machine Learning (ICML 2015)*, 2015

S. Chan, I. Diakonikolas, R. Servedio, X. Sun . Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms. In * Advances in Neural Information Processing Systems (NIPS 2014)*, 2014

G. Ver Steeg and A. Galstyan. Discovering Structure in High-Dimensional Data Through Correlation Explanation. In * Advances in Neural Information Processing Systems (NIPS 2014)*, 2014

S. Chan, I. Diakonikolas, R. Servedio, X. Sun . Efficient Density Estimation via Piecewise Polynomial Approximation. In * Annual ACM Symposium on Theory of Computing (STOC 2014)*, 2014

X. He, T. Rekatsinas, J. Foulds, L. Getoor, Y. Liu. HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. In * International Conference on Machine Learning (ICML 2015)*,

Jason D. Lee, Dennis L Sun, Yuekai Sun, and Jonathan Taylor.. Exact Post-Selection Inference with the Lasso. In * Annals of Statistics*, 2016

M. Hong, M. Razaviyayn, Z.-Q. Luo, J.-S. Pang. A unified algorithmic framework for block-structured optimization involving big data: With applications in machine learning and signal processing. In * IEEE Signal Processing Magazine*, 2016

N. Nayyar, D. Kalathil and R. Jain. On regret-optimal learning in decentralized multi-armed bandits. In * IEEE Trans. on Control of Networked Systems*, 2016

Kong, Y., Zheng, Z. and Lv, J.. The constrained Dantzig selector with enhanced consistency. In * Journal of Machine Learning Research*, 2016

Shang-Hua Teng. Foundations and Trends in Theoretical Computer Science . In * Scalable Algorithms for Data and Network Analysis*, 2016

S. Oymak, M. Soltanolkotabi, and B. Recht. Sharp Time-data tradeoffs for linear inverse problems. In * Submitted to IEEE Trans. on Information Theory*, 2016

Minsker, S.. Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries.. In * Submitted to the Annals of Statistics*, 2016

Fan, Y. and Lv, J.. Innovated scalable efficient estimation in ultra-large Gaussian graphical models. In * The Annals of Statistics*, 2016

Minsker, S.. Geometric median and robust estimation in Banach spaces. In * Bernoulli*, 2015

D. Kalathil, V. Borkar and R. Jain,. Empirical Q-Value Iteration. In * Submitted to Stochastic Systems*, 2015