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2020 – today
- 2022
[i75]Minghao Yan, Nicholas Meisburger, Tharun Medini, Anshumali Shrivastava:
Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity. CoRR abs/2201.12667 (2022)- [i74]Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki:
Learning to Retrieve Relevant Experiences for Motion Planning. CoRR abs/2204.08550 (2022) - 2021
- [j3]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi
, Zhiwei Zhang:
Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions. Artif. Intell. 299: 103559 (2021) - [c62]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. AAAI 2021: 3976-3983 - [c61]Benjamin Coleman, Anshumali Shrivastava:
A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale. CCS 2021: 3252-3265 - [c60]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. DATE 2021: 1426-1429 - [c59]Pedro Reviriego, José Alberto Hernández, Zhenwei Dai, Anshumali Shrivastava:
Learned Bloom Filters in Adversarial Environments: A Malicious URL Detection Use-Case. HPSR 2021: 1-6 - [c58]Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Ré:
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. ICLR 2021 - [c57]Tharun Medini, Beidi Chen, Anshumali Shrivastava:
SOLAR: Sparse Orthogonal Learned and Random Embeddings. ICLR 2021 - [c56]Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava:
A Tale of Two Efficient and Informative Negative Sampling Distributions. ICML 2021: 2319-2329 - [c55]Constantinos Chamzas, Zachary K. Kingston, Carlos Quintero-Peña, Anshumali Shrivastava, Lydia E. Kavraki:
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. ICRA 2021: 1283-1289 - [c54]Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava:
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. NeurIPS 2021: 5576-5589 - [c53]Aditya Desai, Zhaozhuo Xu, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava:
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler. NeurIPS 2021: 8740-8752 - [c52]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. NeurIPS 2021: 9950-9962 - [c51]Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava:
Locality Sensitive Teaching. NeurIPS 2021: 18049-18062 - [c50]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. ECML/PKDD (1) 2021: 772-787 - [c49]Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava:
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. SIGMOD Conference 2021: 352-364 - [c48]Gaurav Gupta, Minghao Yan, Benjamin Coleman, Bryce Kille, Ryan A. Leo Elworth, Tharun Medini, Todd J. Treangen, Anshumali Shrivastava:
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO). SIGMOD Conference 2021: 2226-2234 - [c47]Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava:
SDM-Net: A simple and effective model for generalized zero-shot learning. UAI 2021: 2103-2113 - [i73]Aditya Desai, Benjamin Coleman, Anshumali Shrivastava:
Density Sketches for Sampling and Estimation. CoRR abs/2102.12301 (2021) - [i72]Zhaozhuo Xu, Aditya Desai, Menal Gupta, Anu Chandran, Antoine Vial-Aussavy, Anshumali Shrivastava:
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion. CoRR abs/2102.13631 (2021) - [i71]Aditya Desai, Yanzhou Pan, Kuangyuan Sun, Li Chou, Anshumali Shrivastava:
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems. CoRR abs/2103.06124 (2021) - [i70]Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
IRLI: Iterative Re-partitioning for Learning to Index. CoRR abs/2103.09944 (2021) - [i69]Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Yong Wu, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava:
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More. CoRR abs/2103.10891 (2021) - [i68]Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava, Alex Smola:
Graph Reordering for Cache-Efficient Near Neighbor Search. CoRR abs/2104.03221 (2021) - [i67]Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu:
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing. CoRR abs/2105.08285 (2021) - [i66]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. CoRR abs/2106.06627 (2021) - [i65]Zhaozhuo Xu, Minghao Yan, Junyan Zhang, Anshumali Shrivastava:
PairConnect: A Compute-Efficient MLP Alternative to Attention. CoRR abs/2106.08235 (2021) - [i64]Zichang Liu, Benjamin Coleman, Anshumali Shrivastava:
Efficient Inference via Universal LSH Kernel. CoRR abs/2106.11426 (2021) - [i63]Joshua Engels, Benjamin Coleman, Anshumali Shrivastava:
Practical Near Neighbor Search via Group Testing. CoRR abs/2106.11565 (2021) - [i62]Aditya Desai, Li Chou, Anshumali Shrivastava:
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000× Compression and 2.7× Faster Inference. CoRR abs/2108.02191 (2021) - [i61]Zhenwei Dai, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, Anshumali Shrivastava:
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks. CoRR abs/2110.12292 (2021) - [i60]Zhaozhuo Xu, Alan Baonan Ji, Andrew Woods, Beidi Chen, Anshumali Shrivastava:
Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston. CoRR abs/2111.06562 (2021) - [i59]Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu:
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures. CoRR abs/2111.15139 (2021) - 2020
- [c46]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang:
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. AAAI 2020: 1552-1560 - [c45]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c44]Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava:
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. ICML 2020: 2089-2099 - [c43]Ryan Spring, Anshumali Shrivastava:
Mutual Information Estimation using LSH Sampling. IJCAI 2020: 2807-2815 - [c42]Beidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Tsung-Yuan Charlie Tai, Anshumali Shrivastava:
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. MLSys 2020 - [c41]Zhenwei Dai, Anshumali Shrivastava:
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web. NeurIPS 2020 - [c40]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. WWW 2020: 1739-1749 - [i58]Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong:
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. CoRR abs/2006.06535 (2020) - [i57]Benjamin Coleman, Anshumali Shrivastava:
A One-Pass Private Sketch for Most Machine Learning Tasks. CoRR abs/2006.09352 (2020) - [i56]Benjamin Coleman, Gaurav Gupta, John Chen, Anshumali Shrivastava:
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge. CoRR abs/2006.14554 (2020) - [i55]Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
Climbing the WOL: Training for Cheaper Inference. CoRR abs/2007.01230 (2020) - [i54]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020) - [i53]Nicholas Meisburger, Anshumali Shrivastava:
Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation. CoRR abs/2008.03260 (2020) - [i52]Tharun Medini, Beidi Chen, Anshumali Shrivastava:
SOLAR: Sparse Orthogonal Learned and Random Embeddings. CoRR abs/2008.13225 (2020) - [i51]Constantinos Chamzas, Zachary K. Kingston, Carlos Quintero-Peña, Anshumali Shrivastava, Lydia E. Kavraki:
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. CoRR abs/2010.15335 (2020) - [i50]Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava:
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. CoRR abs/2010.15951 (2020) - [i49]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. CoRR abs/2011.14954 (2020) - [i48]Shabnam Daghaghi, Tharun Medini, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava:
A Constant-time Adaptive Negative Sampling. CoRR abs/2012.15843 (2020)
2010 – 2019
- 2019
- [j2]Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong:
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4): 144:1-144:18 (2019) - [j1]Andrew M. Wells, Neil T. Dantam, Anshumali Shrivastava, Lydia E. Kavraki:
Learning Feasibility for Task and Motion Planning in Tabletop Environments. IEEE Robotics Autom. Lett. 4(2): 1255-1262 (2019) - [c39]Chen Luo, Anshumali Shrivastava:
Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS). AAAI 2019: 4464-4471 - [c38]Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava:
Compressing Gradient Optimizers via Count-Sketches. ICML 2019: 5946-5955 - [c37]Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki:
Using Local Experiences for Global Motion Planning. ICRA 2019: 8606-8612 - [c36]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Fast and Accurate Stochastic Gradient Estimation. NeurIPS 2019: 12339-12349 - [c35]Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava:
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. NeurIPS 2019: 13244-13254 - [i47]Shabnam Daghaghi, Anshumali Shrivastava, Tharun Medini:
Cross-Modal Mapping for Generalized Zero-Shot Learning by Soft-Labeling. ViGIL@NeurIPS 2019 - [i46]Sicong Liu, Anshumali Shrivastava, Junzhao Du, Lin Zhong:
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network. CoRR abs/1901.08730 (2019) - [i45]Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava:
Compressing Gradient Optimizers via Count-Sketches. CoRR abs/1902.00179 (2019) - [i44]Benjamin Coleman, Anshumali Shrivastava, Richard G. Baraniuk:
RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data. CoRR abs/1902.06687 (2019) - [i43]Beidi Chen, Tharun Medini, Anshumali Shrivastava:
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems. CoRR abs/1903.03129 (2019) - [i42]Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki:
Using Local Experiences for Global Motion Planning. CoRR abs/1903.08693 (2019) - [i41]John Chen, Benjamin Coleman, Anshumali Shrivastava:
Revisiting Consistent Hashing with Bounded Loads. CoRR abs/1908.08762 (2019) - [i40]Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava:
Semantic Similarity Based Softmax Classifier for Zero-Shot Learning. CoRR abs/1909.04790 (2019) - [i39]Gaurav Gupta, Benjamin Coleman, Tharun Medini, Vijai Mohan, Anshumali Shrivastava:
RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time. CoRR abs/1910.02611 (2019) - [i38]Zhenwei Dai, Anshumali Shrivastava:
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier. CoRR abs/1910.09131 (2019) - [i37]Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava:
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. CoRR abs/1910.13830 (2019) - [i36]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation. CoRR abs/1910.14162 (2019) - [i35]Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang:
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. CoRR abs/1912.01032 (2019) - [i34]Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar:
Angular Visual Hardness. CoRR abs/1912.02279 (2019) - [i33]Benjamin Coleman, Anshumali Shrivastava:
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data. CoRR abs/1912.02283 (2019) - [i32]M. Sadegh Riazi, Beidi Chen, Anshumali Shrivastava, Dan S. Wallach, Farinaz Koushanfar:
Sub-Linear Privacy-Preserving Near-Neighbor Search. IACR Cryptol. ePrint Arch. 2019: 1222 (2019) - 2018
- [c34]Chen Luo, Anshumali Shrivastava:
Jaccard Affiliation Graph (JAG) Model For Explaining Overlapping Community Behaviors. ASONAM 2018: 1-8 - [c33]Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Lsh-Sampling breaks the Computational chicken-and-egg Loop in adaptive stochastic Gradient estimation. ICLR (Workshop) 2018 - [c32]Ryan Spring, Anshumali Shrivastava:
Scalable Estimation via LSH Samplers (LSS). ICLR (Workshop) 2018 - [c31]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. ICML 2018: 80-88 - [c30]Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, Jieping Ye:
TINET: Learning Invariant Networks via Knowledge Transfer. KDD 2018: 1890-1899 - [c29]Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar:
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements. NeurIPS 2018: 10921-10931 - [c28]Rebecca C. Steorts, Anshumali Shrivastava:
Probabilistic Blocking with an Application to the Syrian Conflict. PSD 2018: 314-327 - [c27]Yiqiu Wang, Anshumali Shrivastava, Jonathan Wang, Junghee Ryu:
Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. SIGMOD Conference 2018: 889-903 - [c26]Beidi Chen, Anshumali Shrivastava:
Densified Winner Take All (WTA) Hashing for Sparse Datasets. UAI 2018: 906-916 - [c25]Chen Luo, Anshumali Shrivastava:
Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge. WWW 2018: 1439-1448 - [c24]Anshumali Shrivastava:
Training 100, 000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs. WWW (Companion Volume) 2018: 1895 - [i31]Chen Luo, Anshumali Shrivastava:
Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS). CoRR abs/1802.07444 (2018) - [i30]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. CoRR abs/1806.04310 (2018) - [i29]Chen Luo, Anshumali Shrivastava:
Want to bring a community together? Create more sub-communities. CoRR abs/1807.04911 (2018) - [i28]Qixuan Huang, Yiqiu Wang, Tharun Medini, Anshumali Shrivastava:
Extreme Classification in Log Memory. CoRR abs/1810.04254 (2018) - [i27]Rebecca C. Steorts, Anshumali Shrivastava:
Probabilistic Blocking with An Application to the Syrian Conflict. CoRR abs/1810.05497 (2018) - 2017
- [c23]E. J. Jose Gonzalez, Chen Luo, Anshumali Shrivastava, Krishna V. Palem, Yongshik Moon, Soonhyun Noh, Daedong Park, Seongsoo Hong:
Location detection for navigation using IMUs with a map through coarse-grained machine learning. DATE 2017: 500-505 - [c22]Anshumali Shrivastava:
Optimal Densification for Fast and Accurate Minwise Hashing. ICML 2017: 3154-3163 - [c21]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
RHash: Robust Hashing via L_infinity-norm Distortion. IJCAI 2017: 1386-1394 - [c20]Ryan Spring, Anshumali Shrivastava:
Scalable and Sustainable Deep Learning via Randomized Hashing. KDD 2017: 445-454 - [i26]Anshumali Shrivastava:
Optimal Densification for Fast and Accurate Minwise Hashing. CoRR abs/1703.04664 (2017) - [i25]Ryan Spring, Anshumali Shrivastava:
A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. CoRR abs/1703.05160 (2017) - [i24]Chen Luo, Anshumali Shrivastava:
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups. CoRR abs/1706.06664 (2017) - [i23]Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li:
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer. CoRR abs/1708.07867 (2017) - [i22]Yiqiu Wang, Anshumali Shrivastava, Junghee Ryu:
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search. CoRR abs/1709.01190 (2017) - [i21]Beidi Chen, Anshumali Shrivastava, Rebecca C. Steorts:
Unique Entity Estimation with Application to the Syrian Conflict. CoRR abs/1710.02690 (2017) - 2016
- [c19]Chen Luo, Anshumali Shrivastava:
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. NIPS Time Series Workshop 2016: 38-58 - [c18]Anshumali Shrivastava:
Simple and Efficient Weighted Minwise Hashing. NIPS 2016: 1498-1506 - [c17]Anshumali Shrivastava, Arnd Christian König, Mikhail Bilenko:
Time Adaptive Sketches (Ada-Sketches) for Summarizing Data Streams. SIGMOD Conference 2016: 1417-1432 - [c16]Yongshik Moon, Soonhyun Noh, Daedong Park, Chen Luo, Anshumali Shrivastava, Seongsoo Hong, Krishna V. Palem:
CaPSuLe: A camera-based positioning system using learning. SoCC 2016: 235-240 - [i20]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search. CoRR abs/1602.06577 (2016) - [i19]Ryan Spring, Anshumali Shrivastava:
Scalable and Sustainable Deep Learning via Randomized Hashing. CoRR abs/1602.08194 (2016) - [i18]Anshumali Shrivastava:
Exact Weighted Minwise Hashing in Constant Time. CoRR abs/1602.08393 (2016) - [i17]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
Near-Isometric Binary Hashing for Large-scale Datasets. CoRR abs/1603.03836 (2016) - [i16]Chen Luo, Anshumali Shrivastava:
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series. CoRR abs/1610.07328 (2016) - [i15]Beidi Chen, Anshumali Shrivastava:
Revisiting Winner Take All (WTA) Hashing for Sparse Datasets. CoRR abs/1612.01834 (2016) - [i14]M. Sadegh Riazi, Beidi Chen, Anshumali Shrivastava, Dan S. Wallach, Farinaz Koushanfar:
Sub-linear Privacy-preserving Search with Untrusted Server and Semi-honest Parties. CoRR abs/1612.01835 (2016) - 2015
- [b1]Anshumali Shrivastava:
Probabilistic Hashing Techniques for Big Data. Cornell University, USA, 2015 - [c15]Anshumali Shrivastava, Ping Li:
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS). UAI 2015: 812-821 - [c14]Anshumali Shrivastava, Ping Li:
Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment. WWW 2015: 981-991 - [i13]Peter Sadosky, Anshumali Shrivastava, Megan Price, Rebecca C. Steorts:
Blocking Methods Applied to Casualty Records from the Syrian Conflict. CoRR abs/1510.07714 (2015) - 2014
- [c13]Anshumali Shrivastava, Ping Li:
In Defense of Minhash over Simhash. AISTATS 2014: 886-894 - [c12]Anshumali Shrivastava, Ping Li:
A new space for comparing graphs. ASONAM 2014: 62-71 - [c11]Anshumali Shrivastava, Ping Li:
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search. ICML 2014: 557-565 - [c10]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections. ICML 2014: 676-684 - [c9]Anshumali Shrivastava, Ping Li:
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). NIPS 2014: 2321-2329 - [c8]Anshumali Shrivastava, Ping Li:
Improved Densification of One Permutation Hashing. UAI 2014: 732-741 - [i12]Ping Li, Michael Mitzenmacher, Anshumali Shrivastava:
Coding for Random Projections and Approximate Near Neighbor Search. CoRR abs/1403.8144 (2014) - [i11]Anshumali Shrivastava, Ping Li:
A New Space for Comparing Graphs. CoRR abs/1404.4644 (2014) - [i10]Anshumali Shrivastava, Ping Li:
Graph Kernels via Functional Embedding. CoRR abs/1404.5214 (2014) - [i9]