| On Learning Mixture of Linear Regressions in the Non-Realizable Setting. | 0 | 0.34 | 2022 |
| Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing | 0 | 0.34 | 2022 |
| On Learning Mixture Models with Sparse Parameters | 0 | 0.34 | 2022 |
| vqSGD: Vector Quantized Stochastic Gradient Descent | 0 | 0.34 | 2022 |
| Support Recovery in Universal One-Bit Compressed Sensing. | 0 | 0.34 | 2022 |
| Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians | 0 | 0.34 | 2022 |
| Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback | 0 | 0.34 | 2021 |
| Probabilistic Group Testing With A Linear Number Of Tests | 0 | 0.34 | 2021 |
| Byzantine Resilient Distributed Clustering With Redundant Data Assignment | 0 | 0.34 | 2021 |
| Vqsgd: Vector Quantized Stochastic Gradient Descent | 0 | 0.34 | 2021 |
| Reliable Distributed Clustering with Redundant Data Assignment | 0 | 0.34 | 2020 |
| Recovery of sparse linear classifiers from mixture of responses | 0 | 0.34 | 2020 |
| Recovery of Sparse Signals from a Mixture of Linear Samples. | 0 | 0.34 | 2020 |
| High Dimensional Discrete Integration over the Hypergrid. | 0 | 0.34 | 2020 |
| Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping | 0 | 0.34 | 2020 |
| Communication-Efficient and Byzantine-Robust Distributed Learning | 1 | 0.39 | 2020 |
| Distributed Newton Can Communicate Less and Resist Byzantine Workers | 0 | 0.34 | 2020 |
| A workload-adaptive mechanism for linear queries under local differential privacy | 0 | 0.34 | 2020 |
| A workload-adaptive mechanism for linear queries under local differential privacy. | 0 | 0.34 | 2020 |
| Algebraic and Analytic Approaches for Parameter Learning in Mixture Models | 0 | 0.34 | 2020 |
| Robust Gradient Descent Via Moment Encoding And Ldpc Codes | 1 | 0.36 | 2019 |
| Superset Technique for Approximate Recovery in One-Bit Compressed Sensing | 0 | 0.34 | 2019 |
| Sample Complexity of Learning Mixture of Sparse Linear Regressions | 0 | 0.34 | 2019 |
| Trace Reconstruction: Generalized and Parameterized | 1 | 0.36 | 2019 |
| Storage Capacity as an Information-Theoretic Vertex Cover and the Index Coding Rate | 0 | 0.34 | 2019 |
| Semisupervised Clustering by Queries and Locally Encodable Source Coding | 0 | 0.34 | 2019 |
| Same-Cluster Querying for Overlapping Clusters | 0 | 0.34 | 2019 |
| Learning and Recovery in the ReLU Model | 0 | 0.34 | 2019 |
| Combinatorial Alphabet-Dependent Bounds for Locally Recoverable Codes. | 7 | 0.42 | 2018 |
| Connectivity in Random Annulus Graphs and the Geometric Block Model. | 1 | 0.35 | 2018 |
| High Dimensional Discrete Integration by Hashing and Optimization. | 0 | 0.34 | 2018 |
| The Geometric Block Model and Applications. | 0 | 0.34 | 2018 |
| Linear Programming Approximations for Index Coding. | 0 | 0.34 | 2018 |
| The Geometric Block Model | 1 | 0.36 | 2018 |
| Representation Learning and Recovery in the ReLU Model. | 0 | 0.34 | 2018 |
| Novel Impossibility Results For Group-Testing | 4 | 0.46 | 2018 |
| Coding Theory for Inference, Learning and Optimization (Dagstuhl Seminar 18112). | 0 | 0.34 | 2018 |
| Multilabel Classification with Group Testing and Codes. | 1 | 0.34 | 2017 |
| Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding | 0 | 0.34 | 2017 |
| Group Testing Schemes From Codes and Designs. | 0 | 0.34 | 2017 |
| Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis. | 5 | 0.42 | 2017 |
| Query Complexity of Clustering with Side Information. | 4 | 0.41 | 2017 |
| A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution. | 3 | 0.38 | 2017 |
| Estimation of sparsity via simple measurements | 0 | 0.34 | 2017 |
| Associative Memory Using Dictionary Learning and Expander Decoding | 0 | 0.34 | 2017 |
| Clustering with Noisy Queries. | 1 | 0.35 | 2017 |
| Clustering with an oracle. | 0 | 0.34 | 2016 |
| Local Partial Clique Covers for Index Coding. | 2 | 0.39 | 2016 |
| Nonadaptive Group Testing With Random Set of Defectives. | 9 | 0.58 | 2016 |
| Clustering Via Crowdsourcing. | 6 | 0.46 | 2016 |
