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Publication search results
found 168 matches
- 2017
- Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein:
Adaptive ADMM with Spectral Penalty Parameter Selection. AISTATS 2017: 718-727 - Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS 2017: 479-488 - Bo Xie, Yingyu Liang, Le Song:
Diverse Neural Network Learns True Target Functions. AISTATS 2017: 1216-1224 - Benjamin Cowley, João D. Semedo, Amin Zandvakili, Matthew A. Smith, Adam Kohn, Byron M. Yu:
Distance Covariance Analysis. AISTATS 2017: 242-251 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek:
Hit-and-Run for Sampling and Planning in Non-Convex Spaces. AISTATS 2017: 888-895 - Marc Abeille, Alessandro Lazaric:
Linear Thompson Sampling Revisited. AISTATS 2017: 176-184 - Marc Abeille, Alessandro Lazaric:
Thompson Sampling for Linear-Quadratic Control Problems. AISTATS 2017: 1246-1254 - Roy J. Adams, Benjamin M. Marlin:
Learning Time Series Detection Models from Temporally Imprecise Labels. AISTATS 2017: 157-165 - Ibrahim M. Alabdulmohsin:
An Information-Theoretic Route from Generalization in Expectation to Generalization in Probability. AISTATS 2017: 92-100 - Alnur Ali, Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam:
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation. AISTATS 2017: 280-288 - Pierre Alquier, The Tien Mai, Massimiliano Pontil:
Regret Bounds for Lifelong Learning. AISTATS 2017: 261-269 - Ankit Anand, Ritesh Noothigattu, Parag Singla, Mausam:
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models. AISTATS 2017: 1541-1549 - Forough Arabshahi, Anima Anandkumar:
Spectral Methods for Correlated Topic Models. AISTATS 2017: 1439-1447 - Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama:
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS 2017: 537-546 - Sohail Bahmani, Justin Romberg:
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation. AISTATS 2017: 252-260 - Francois Belletti, Evan R. Sparks, Alexandre M. Bayen, Joseph Gonzalez:
Random projection design for scalable implicit smoothing of randomly observed stochastic processes. AISTATS 2017: 700-708 - Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi:
Local Perturb-and-MAP for Structured Prediction. AISTATS 2017: 585-594 - Justin Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts:
Distribution of Gaussian Process Arc Lengths. AISTATS 2017: 1412-1420 - Aniruddha Bhargava, Ravi Ganti, Robert D. Nowak:
Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications. AISTATS 2017: 1349-1357 - Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo:
Frequency Domain Predictive Modelling with Aggregated Data. AISTATS 2017: 971-980 - Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. AISTATS 2017: 111-120 - Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cédric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamás Sarlós, Jamal Atif:
Structured adaptive and random spinners for fast machine learning computations. AISTATS 2017: 1020-1029 - Aleksandar Botev, Bowen Zheng, David Barber:
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. AISTATS 2017: 1030-1038 - Thomas Brouwer, Pietro Liò:
Bayesian Hybrid Matrix Factorisation for Data Integration. AISTATS 2017: 557-566 - Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS 2017: 1523-1531 - Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Distributed Adaptive Sampling for Kernel Matrix Approximation. AISTATS 2017: 1421-1429 - Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy:
Clustering from Multiple Uncertain Experts. AISTATS 2017: 28-36 - Yuxin Chen, Seyed Hamed Hassani, Andreas Krause:
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests. AISTATS 2017: 223-231 - Lijie Chen, Jian Li, Mingda Qiao:
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. AISTATS 2017: 101-110 - Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon:
Rank Aggregation and Prediction with Item Features. AISTATS 2017: 748-756
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