| Improved Sample Complexity Bounds for Branch-And-Cut | 0 | 0.34 | 2022 |
| Robustly-reliable learners under poisoning attacks. | 0 | 0.34 | 2022 |
| Learning Within an Instance for Designing High-Revenue Combinatorial Auctions. | 0 | 0.34 | 2021 |
| Geometry-Aware Gradient Algorithms for Neural Architecture Search | 0 | 0.34 | 2021 |
| Lifelong learning in costly feature spaces | 0 | 0.34 | 2020 |
| Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs | 0 | 0.34 | 2020 |
| Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. | 0 | 0.34 | 2020 |
| K-Center Clustering Under Perturbation Resilience | 1 | 0.36 | 2020 |
| Learning to Link | 0 | 0.34 | 2020 |
| Semi-bandit Optimization in the Dispersed Setting. | 0 | 0.34 | 2020 |
| Estimating Approximate Incentive Compatibility. | 0 | 0.34 | 2019 |
| Non-Convex Matrix Completion and Related Problems via Strong Duality | 0 | 0.34 | 2019 |
| Semi-bandit Optimization in the Dispersed Setting. | 0 | 0.34 | 2019 |
| Provable Guarantees for Gradient-Based Meta-Learning. | 0 | 0.34 | 2019 |
| Adaptive Gradient-Based Meta-Learning Methods. | 0 | 0.34 | 2019 |
| Envy-Free Classification. | 0 | 0.34 | 2019 |
| A General Theory of Sample Complexity for Multi-Item Profit Maximization. | 3 | 0.38 | 2018 |
| Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization | 2 | 0.36 | 2018 |
| Data-Driven Clustering via Parameterized Lloyd's Families. | 1 | 0.35 | 2018 |
| Learning to Branch. | 0 | 0.34 | 2018 |
| Differentially Private Clustering in High-Dimensional Euclidean Spaces. | 0 | 0.34 | 2017 |
| Nash Equilibria in Perturbation-Stable Games. | 0 | 0.34 | 2017 |
| General and Robust Communication-Efficient Algorithms for Distributed Clustering. | 2 | 0.36 | 2017 |
| Label Efficient Learning by Exploiting Multi-Class Output Codes. | 0 | 0.34 | 2017 |
| Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions. | 0 | 0.34 | 2017 |
| Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s2-Regularization. | 0 | 0.34 | 2017 |
| Performance guarantees for transferring representations. | 0 | 0.34 | 2017 |
| Private and Online Optimization of Piecewise Lipschitz Functions. | 0 | 0.34 | 2017 |
| Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks. | 1 | 0.37 | 2017 |
| S-Concave Distributions: Towards Broader Distributions for Noise-Tolerant and Sample-Efficient Learning Algorithms. | 0 | 0.34 | 2017 |
| Sample Complexity of Automated Mechanism Design. | 5 | 0.54 | 2016 |
| Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). | 0 | 0.34 | 2016 |
| k-center Clustering under Perturbation Resilience | 0 | 0.34 | 2016 |
| Learning Combinatorial Functions from Pairwise Comparisons. | 2 | 0.38 | 2016 |
| Learning the best algorithm for max-cut, clustering, and other partitioning problems. | 0 | 0.34 | 2016 |
| Communication Efficient Distributed Kernel Principal Component Analysis. | 1 | 0.40 | 2016 |
| An Improved Gap-Dependency Analysis of the Noisy Power Method. | 9 | 0.51 | 2016 |
| Symmetric and Asymmetric $k$-center Clustering under Stability. | 1 | 0.41 | 2015 |
| Distributed Kernel Principal Component Analysis. | 0 | 0.34 | 2015 |
| Learning Cooperative Games | 5 | 0.45 | 2015 |
| Learning Submodular Functions with Applications to Multi-Agent Systems | 3 | 0.38 | 2015 |
| Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy | 5 | 0.42 | 2015 |
| Influence Function Learning in Information Diffusion Networks. | 6 | 0.43 | 2014 |
| The power of localization for efficiently learning linear separators with noise | 11 | 0.51 | 2014 |
| Learning Time-Varying Coverage Functions. | 2 | 0.35 | 2014 |
| Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning. | 3 | 0.41 | 2014 |
| Active Learning and Best-Response Dynamics. | 1 | 0.34 | 2014 |
| Scalable Kernel Methods via Doubly Stochastic Gradients. | 55 | 1.63 | 2014 |
| Improved Distributed Principal Component Analysis. | 32 | 1.11 | 2014 |
| Improved Distributed Principal Component Analysis. | 0 | 0.34 | 2014 |
