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2020 – today
- 2024
[c39]Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne:
Effective Data Distillation for Tabular Datasets (Student Abstract). AAAI 2024: 23533-23534- [c38]Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen:
Enhancing In-context Learning via Linear Probe Calibration. AISTATS 2024: 307-315 - [i31]Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen:
Enhancing In-context Learning via Linear Probe Calibration. CoRR abs/2401.12406 (2024) - [i30]Parikshit Ram, Tim Klinger, Alexander G. Gray:
What makes Models Compositional? A Theoretical View: With Supplement. CoRR abs/2405.02350 (2024) - [i29]Takuya Ito, Luca Cocchi, Tim Klinger, Parikshit Ram, Murray Campbell, Luke J. Hearne:
The Importance of Positional Encoding Initialization in Transformers for Relational Reasoning. CoRR abs/2406.08272 (2024) - [i28]Md. Ibrahim Ibne Alam, Parikshit Ram, Soham Dan, Horst Samulowitz, Koushik Kar:
On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems. CoRR abs/2406.13720 (2024) - 2023
- [c37]Malgorzata Lazuka, Andreea Anghel, Parikshit Ram, Haralampos Pozidis, Thomas P. Parnell:
xCloudServing: Automated ML Serving Across Clouds. CLOUD 2023: 1-12 - [c36]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Searching for Fairer Machine Learning Ensembles. AutoML 2023: 17/1-19 - [c35]Parijat Dube, Theodoros Salonidis, Parikshit Ram, Ashish Verma:
Runtime Prediction of Machine Learning Algorithms in Automl Systems. ICASSP 2023: 1-5 - [c34]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR 2023 - [c33]Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng:
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. ICLR 2023 - [c32]Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu:
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. ICLR 2023 - [c31]Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram:
End-to-end Differentiable Clustering with Associative Memories. ICML 2023: 29649-29670 - [c30]Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu:
Model Sparsity Can Simplify Machine Unlearning. NeurIPS 2023 - [c29]Parikshit Ram, Alexander G. Gray, Horst C. Samulowitz, Gregory Bramble:
Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection. SDM 2023: 802-810 - [i27]Parikshit Ram, Alexander G. Gray, Horst C. Samulowitz, Gregory Bramble:
Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection. CoRR abs/2301.05131 (2023) - [i26]Yihua Zhang, Pranay Sharma
, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu:
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. CoRR abs/2303.02343 (2023) - [i25]Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu:
Model Sparsification Can Simplify Machine Unlearning. CoRR abs/2304.04934 (2023) - [i24]Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram:
End-to-end Differentiable Clustering with Associative Memories. CoRR abs/2306.03209 (2023) - [i23]Tim Klinger, Luke Liu, Soham Dan, Maxwell Crouse, Parikshit Ram, Alexander Gray:
Compositional Program Generation for Systematic Generalization. CoRR abs/2309.16467 (2023) - 2022
- [c28]Parikshit Ram, Kaushik Sinha:
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. AAAI 2022: 8036-8044 - [c27]Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI 2022: 10228-10237 - [c26]Parikshit Ram:
On the Optimality Gap of Warm-Started Hyperparameter Optimization. AutoML 2022: 12/1-14 - [c25]Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin, Sijia Liu:
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. IJCAI 2022: 1714-1720 - [c24]Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay:
Gradual AutoML using Lale. KDD 2022: 4794-4795 - [c23]Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu:
Advancing Model Pruning via Bi-level Optimization. NeurIPS 2022 - [c22]Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh:
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. ECML/PKDD (1) 2022: 675-692 - [i22]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
An Empirical Study of Modular Bias Mitigators and Ensembles. CoRR abs/2202.00751 (2022) - [i21]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. CoRR abs/2202.08338 (2022) - [i20]Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng:
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. CoRR abs/2203.01924 (2022) - [i19]Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu:
Advancing Model Pruning via Bi-level Optimization. CoRR abs/2210.04092 (2022) - [i18]Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Navigating Ensemble Configurations for Algorithmic Fairness. CoRR abs/2210.05594 (2022) - 2021
- [c21]Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI 2021: 8902-8911 - [c20]Aswin Kannan, Anamitra Roy Choudhury, Vaibhav Saxena, Saurabh Raje, Parikshit Ram, Ashish Verma, Yogish Sabharwal:
HyperASPO: Fusion of Model and Hyper Parameter Optimization for Multi-objective Machine Learning. IEEE BigData 2021: 790-800 - [c19]Kaushik Sinha, Parikshit Ram:
Fruit-fly Inspired Neighborhood Encoding for Classification. KDD 2021: 1470-1480 - [c18]Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay:
Pipeline Combinators for Gradual AutoML. NeurIPS 2021: 19705-19718 - [i17]Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, Elizabeth Daly:
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization. CoRR abs/2107.01253 (2021) - [i16]Chen Fan, Parikshit Ram, Sijia Liu:
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD. CoRR abs/2109.07497 (2021) - [i15]Parikshit Ram, Kaushik Sinha:
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement. CoRR abs/2112.07157 (2021) - [i14]Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. CoRR abs/2112.08524 (2021) - 2020
- [c17]Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander G. Gray:
An ADMM Based Framework for AutoML Pipeline Configuration. AAAI 2020: 4892-4899 - [c16]Michael Katz, Parikshit Ram, Shirin Sohrabi, Octavian Udrea:
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning. ICAPS 2020: 403-411 - [c15]Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray:
Survey on Automated End-to-End Data Science? IJCNN 2020: 1-9 - [c14]Dakuo Wang, Parikshit Ram, Daniel Karl I. Weidele, Sijia Liu, Michael J. Muller, Justin D. Weisz, Abel N. Valente, Arunima Chaudhary, Dustin Ramsey Torres, Horst Samulowitz, Lisa Amini:
AutoAI: Automating the End-to-End AI Lifecycle with Humans-in-the-Loop. IUI Companion 2020: 77-78 - [i13]Parikshit Ram, Sijia Liu, Deepak Vijaykeerthy, Dakuo Wang, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Alexander G. Gray:
Solving Constrained CASH Problems with ADMM. CoRR abs/2006.09635 (2020) - [i12]Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar:
Lale: Consistent Automated Machine Learning. CoRR abs/2007.01977 (2020) - [i11]Kaushik Sinha, Parikshit Ram:
Neural Neighborhood Encoding for Classification. CoRR abs/2008.08685 (2020) - [i10]Pu Zhao, Sijia Liu, Parikshit Ram, Songtao Lu, Djallel Bouneffouf, Xue Lin:
Learned Fine-Tuner for Incongruous Few-Shot Learning. CoRR abs/2009.13714 (2020)
2010 – 2019
- 2019
- [j5]Dakuo Wang, Justin D. Weisz, Michael J. Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla R. Tausczik, Horst Samulowitz, Alexander G. Gray:
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI. Proc. ACM Hum. Comput. Interact. 3(CSCW): 211:1-211:24 (2019) - [c13]Parikshit Ram, Kaushik Sinha:
Revisiting kd-tree for Nearest Neighbor Search. KDD 2019: 1378-1388 - [i9]Sijia Liu, Parikshit Ram, Djallel Bouneffouf, Gregory Bramble, Andrew R. Conn, Horst Samulowitz, Alexander G. Gray:
Automated Machine Learning via ADMM. CoRR abs/1905.00424 (2019) - [i8]Martin Hirzel, Kiran Kate, Avraham Shinnar, Subhrajit Roy, Parikshit Ram:
Type-Driven Automated Learning with Lale. CoRR abs/1906.03957 (2019) - [i7]Dakuo Wang, Justin D. Weisz, Michael J. Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla R. Tausczik, Horst Samulowitz, Alexander G. Gray:
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI. CoRR abs/1909.02309 (2019) - [i6]Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray:
How can AI Automate End-to-End Data Science? CoRR abs/1910.14436 (2019) - 2018
- [j4]Omid Keivani, Kaushik Sinha, Parikshit Ram:
Improved maximum inner product search with better theoretical guarantee using randomized partition trees. Mach. Learn. 107(6): 1069-1094 (2018) - 2017
- [c12]Omid Keivani, Kaushik Sinha, Parikshit Ram:
Improved maximum inner product search with better theoretical guarantees. IJCNN 2017: 2927-2934 - [c11]Parikshit Ram, Alexander G. Gray:
Fraud Detection with Density Estimation Trees. ADF@KDD 2017: 85-94 - 2015
- [j3]Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram:
Plug-and-play dual-tree algorithm runtime analysis. J. Mach. Learn. Res. 16: 3269-3297 (2015) - [i5]Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram:
Plug-and-play dual-tree algorithm runtime analysis. CoRR abs/1501.05222 (2015) - 2014
- [j2]Ryan R. Curtin, Parikshit Ram:
Dual-tree fast exact max-kernel search. Stat. Anal. Data Min. 7(4): 229-253 (2014) - 2013
- [b1]Parikshit Ram:
New paradigms for approximate nearest-neighbor search. Georgia Institute of Technology, Atlanta, GA, USA, 2013 - [j1]Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray:
MLPACK: a scalable C++ machine learning library. J. Mach. Learn. Res. 14(1): 801-805 (2013) - [c10]Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles L. Isbell Jr.:
Tree-Independent Dual-Tree Algorithms. ICML (3) 2013: 1435-1443 - [c9]Parikshit Ram, Alexander G. Gray:
Which Space Partitioning Tree to Use for Search? NIPS 2013: 656-664 - [c8]Ryan R. Curtin, Alexander G. Gray, Parikshit Ram:
Fast Exact Max-Kernel Search. SDM 2013: 1-9 - [i4]Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles Lee Isbell Jr.:
Tree-Independent Dual-Tree Algorithms. CoRR abs/1304.4327 (2013) - 2012
- [c7]Noam Koenigstein, Parikshit Ram, Yuval Shavitt:
Efficient retrieval of recommendations in a matrix factorization framework. CIKM 2012: 535-544 - [c6]Parikshit Ram, Alexander G. Gray:
Maximum inner-product search using cone trees. KDD 2012: 931-939 - [c5]Parikshit Ram, Dongryeol Lee, Alexander G. Gray:
Nearest-Neighbor Search on a Time Budget via Max-Margin Trees. SDM 2012: 1011-1022 - [i3]Parikshit Ram, Alexander G. Gray:
Maximum Inner-Product Search using Tree Data-structures. CoRR abs/1202.6101 (2012) - [i2]Ryan R. Curtin, Parikshit Ram, Alexander G. Gray:
Fast Exact Max-Kernel Search. CoRR abs/1210.6287 (2012) - [i1]Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray:
MLPACK: A Scalable C++ Machine Learning Library. CoRR abs/1210.6293 (2012) - 2011
- [c4]Parikshit Ram, Alexander G. Gray:
Density estimation trees. KDD 2011: 627-635 - 2010
- [c3]William B. March, Parikshit Ram, Alexander G. Gray:
Fast euclidean minimum spanning tree: algorithm, analysis, and applications. KDD 2010: 603-612
2000 – 2009
- 2009
- [c2]Parikshit Ram, Dongryeol Lee, William B. March, Alexander G. Gray:
Linear-time Algorithms for Pairwise Statistical Problems. NIPS 2009: 1527-1535 - [c1]Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray:
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions. NIPS 2009: 1536-1544
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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