Parikshit Ram

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 59

Top Venues

CoRR

AAAI

KDD

ICLR

Georgia Institute of Technology, Atlanta, GA, USA (PHD)

Education History

Georgia Institute of Technology, Atlanta, GA, USA

PHD

Publications

Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
Enhancing In-context Learning via Linear Probe Calibration. AISTATS (2024)
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 (2024)
Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
Enhancing In-context Learning via Linear Probe Calibration. CoRR (2024)
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 (2024)
Parikshit Ram, Tim Klinger, Alexander G. Gray
What makes Models Compositional? A Theoretical View: With Supplement. CoRR (2024)
Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne
Effective Data Distillation for Tabular Datasets (Student Abstract). AAAI (2024)
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)
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 (2023)
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
Model Sparsification Can Simplify Machine Unlearning. CoRR (2023)
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 (2023)
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram
End-to-end Differentiable Clustering with Associative Memories. CoRR (2023)
Parijat Dube, Theodoros Salonidis, Parikshit Ram, Ashish Verma
Runtime Prediction of Machine Learning Algorithms in Automl Systems. ICASSP (2023)
Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
Searching for Fairer Machine Learning Ensembles. AutoML (2023)
Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR (2023)
Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. ICLR (2023)
Tim Klinger, Luke Liu, Soham Dan, Maxwell Crouse, Parikshit Ram, Alexander Gray
Compositional Program Generation for Systematic Generalization. CoRR (2023)
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram
End-to-end Differentiable Clustering with Associative Memories. ICML (2023)
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)
Malgorzata Lazuka, Andreea Anghel, Parikshit Ram, Haralampos Pozidis, Thomas P. Parnell
xCloudServing: Automated ML Serving Across Clouds. CLOUD (2023)
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
Model Sparsity Can Simplify Machine Unlearning. NeurIPS (2023)
Parikshit Ram, Kaushik Sinha
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. AAAI (2022)
Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu
Advancing Model Pruning via Bi-level Optimization. CoRR (2022)
Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
An Empirical Study of Modular Bias Mitigators and Ensembles. CoRR (2022)
Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
Navigating Ensemble Configurations for Algorithmic Fairness. CoRR (2022)
Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. ECML/PKDD (1) (2022)
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)
Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay
Gradual AutoML using Lale. KDD (2022)
Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. CoRR (2022)
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 (2022)
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)
Parikshit Ram
On the Optimality Gap of Warm-Started Hyperparameter Optimization. AutoML (2022)
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)
Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay
Pipeline Combinators for Gradual AutoML. NeurIPS (2021)
Kaushik Sinha, Parikshit Ram
Fruit-fly Inspired Neighborhood Encoding for Classification. KDD (2021)
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)
Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. CoRR (2021)
Chen Fan, Parikshit Ram, Sijia Liu
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD. CoRR (2021)
Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, Elizabeth Daly
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization. CoRR (2021)
Parikshit Ram, Kaushik Sinha
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement. CoRR (2021)
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)
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)
Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar
Lale: Consistent Automated Machine Learning. CoRR (2020)
Pu Zhao, Sijia Liu, Parikshit Ram, Songtao Lu, Djallel Bouneffouf, Xue Lin
Learned Fine-Tuner for Incongruous Few-Shot Learning. CoRR (2020)
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)
Kaushik Sinha, Parikshit Ram
Neural Neighborhood Encoding for Classification. CoRR (2020)
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)
Michael Katz, Parikshit Ram, Shirin Sohrabi, Octavian Udrea
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning. ICAPS (2020)
Parikshit Ram, Sijia Liu, Deepak Vijaykeerthy, Dakuo Wang, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Alexander G. Gray
Solving Constrained CASH Problems with ADMM. CoRR (2020)
Sijia Liu, Parikshit Ram, Djallel Bouneffouf, Gregory Bramble, Andrew R. Conn, Horst Samulowitz, Alexander G. Gray
Automated Machine Learning via ADMM. CoRR (2019)
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 (2019)
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) (2019)
Martin Hirzel, Kiran Kate, Avraham Shinnar, Subhrajit Roy, Parikshit Ram
Type-Driven Automated Learning with Lale. CoRR (2019)
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 (2019)
Parikshit Ram, Kaushik Sinha
Revisiting kd-tree for Nearest Neighbor Search. KDD (2019)
Omid Keivani, Kaushik Sinha, Parikshit Ram
Improved maximum inner product search with better theoretical guarantee using randomized partition trees. Mach. Learn. 107 (6) (2018)
Omid Keivani, Kaushik Sinha, Parikshit Ram
Improved maximum inner product search with better theoretical guarantees. IJCNN (2017)
Parikshit Ram, Alexander G. Gray
Fraud Detection with Density Estimation Trees. ADF@KDD (2017)
Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram
Plug-and-play dual-tree algorithm runtime analysis. CoRR (2015)
Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram
Plug-and-play dual-tree algorithm runtime analysis. J. Mach. Learn. Res. 16 (2015)
Ryan R. Curtin, Parikshit Ram
Dual-tree fast exact max-kernel search. Stat. Anal. Data Min. 7 (4) (2014)
Ryan R. Curtin, William B. March, Parikshit Ram, David V. Anderson, Alexander G. Gray, Charles Lee Isbell Jr.
Tree-Independent Dual-Tree Algorithms CoRR (2013)
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)
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) (2013)
Ryan R. Curtin, Alexander G. Gray, Parikshit Ram
Fast Exact Max-Kernel Search. SDM (2013)
Parikshit Ram, Alexander G. Gray
Which Space Partitioning Tree to Use for Search? NIPS (2013)
Parikshit Ram, Dongryeol Lee, Alexander G. Gray
Nearest-Neighbor Search on a Time Budget via Max-Margin Trees. SDM (2012)
Ryan R. Curtin, Parikshit Ram, Alexander G. Gray
Fast Exact Max-Kernel Search CoRR (2012)
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 (2012)
Parikshit Ram, Alexander G. Gray
Maximum Inner-Product Search using Tree Data-structures CoRR (2012)
Parikshit Ram, Alexander G. Gray
Maximum inner-product search using cone trees. KDD (2012)
Noam Koenigstein, Parikshit Ram, Yuval Shavitt
Efficient retrieval of recommendations in a matrix factorization framework. CIKM (2012)
Parikshit Ram, Alexander G. Gray
Density estimation trees. KDD (2011)
William B. March, Parikshit Ram, Alexander G. Gray
Fast euclidean minimum spanning tree: algorithm, analysis, and applications. KDD (2010)
Parikshit Ram, Dongryeol Lee, William B. March, Alexander G. Gray
Linear-time Algorithms for Pairwise Statistical Problems. NIPS (2009)
Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions. NIPS (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Parikshit Ram