Linear-time Algorithms for Pairwise Statistical Problems. - Parikshit Ram; Dongryeol Lee; William B. March; Alexander G. Gray (NIPS, 2009)

Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions. - Parikshit Ram; Dongryeol Lee; Hua Ouyang; Alexander G. Gray (NIPS, 2009)

Fast euclidean minimum spanning tree: algorithm, analysis, and applications. - William B. March; Parikshit Ram; Alexander G. Gray (KDD, 2010)

Density estimation trees. - Parikshit Ram; Alexander G. Gray (KDD, 2011)

Efficient retrieval of recommendations in a matrix factorization framework. - Noam Koenigstein; Parikshit Ram; Yuval Shavitt (CIKM, 2012)

Maximum inner-product search using cone trees. - Parikshit Ram; Alexander G. Gray (KDD, 2012)

Nearest-Neighbor Search on a Time Budget via Max-Margin Trees. - Parikshit Ram; Dongryeol Lee; Alexander G. Gray (SDM, 2012)

Maximum Inner-Product Search using Tree Data-structures - Parikshit Ram; Alexander G. Gray (CoRR, 2012)

Tree-Independent Dual-Tree Algorithms. - Ryan R. Curtin; William B. March; Parikshit Ram; David V. Anderson; Alexander G. Gray; Charles L. Isbell Jr. (ICML (3), 2013)

Which Space Partitioning Tree to Use for Search? - Parikshit Ram; Alexander G. Gray (NIPS, 2013)

Fast Exact Max-Kernel Search. - Ryan R. Curtin; Alexander G. Gray; Parikshit Ram (SDM, 2013)

MLPACK: a scalable C++ machine learning library. - Ryan R. Curtin; James R. Cline; N. P. Slagle; William B. March; Parikshit Ram; Nishant A. Mehta; Alexander G. Gray (J. Mach. Learn. Res., 2013)

New paradigms for approximate nearest-neighbor search. - Parikshit Ram (2013)

Dual-tree fast exact max-kernel search. - Ryan R. Curtin; Parikshit Ram (Stat. Anal. Data Min., 2014)

Plug-and-play dual-tree algorithm runtime analysis. - Ryan R. Curtin; Dongryeol Lee; William B. March; Parikshit Ram (J. Mach. Learn. Res., 2015)

Improved maximum inner product search with better theoretical guarantees. - Omid Keivani; Kaushik Sinha; Parikshit Ram (IJCNN, 2017)

Fraud Detection with Density Estimation Trees. - Parikshit Ram; Alexander G. Gray (ADF@KDD, 2017)

Improved maximum inner product search with better theoretical guarantee using randomized partition trees. - Omid Keivani; Kaushik Sinha; Parikshit Ram (Mach. Learn., 2018)

Revisiting kd-tree for Nearest Neighbor Search. - Parikshit Ram; Kaushik Sinha (KDD, 2019)

Automated Machine Learning via ADMM. - Sijia Liu 001; Parikshit Ram; Djallel Bouneffouf 001; Gregory Bramble; Andrew R. Conn; Horst Samulowitz; Alexander G. Gray (CoRR, 2019)

Type-Driven Automated Learning with Lale. - Martin Hirzel; Kiran Kate; Avraham Shinnar; Subhrajit Roy; Parikshit Ram (CoRR, 2019)

How can AI Automate End-to-End Data Science? - Charu C. Aggarwal; Djallel Bouneffouf 001; Horst Samulowitz; Beat Buesser; Thanh Hoang; Udayan Khurana; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (CoRR, 2019)

Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI. - Dakuo Wang; Justin D. Weisz; Michael J. Muller; Parikshit Ram; Werner Geyer; Casey Dugan; Yla R. Tausczik; Horst Samulowitz; Alexander G. Gray (Proc. ACM Hum. Comput. Interact., 2019)

An ADMM Based Framework for AutoML Pipeline Configuration. - Sijia Liu 001; Parikshit Ram; Deepak Vijaykeerthy; Djallel Bouneffouf 001; Gregory Bramble; Horst Samulowitz; Dakuo Wang; Andrew Conn 001; Alexander G. Gray (AAAI, 2020)

Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning. - Michael Katz 001; Parikshit Ram; Shirin Sohrabi; Octavian Udrea (ICAPS, 2020)

Survey on Automated End-to-End Data Science? - Djallel Bouneffouf 001; Charu C. Aggarwal; Thanh Hoang; Udayan Khurana; Horst Samulowitz; Beat Buesser; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (IJCNN, 2020)

AutoAI: Automating the End-to-End AI Lifecycle with Humans-in-the-Loop. - Dakuo Wang; Parikshit Ram; Daniel Karl I. Weidele; Sijia Liu 001; Michael J. Muller; Justin D. Weisz; Abel N. Valente; Arunima Chaudhary; Dustin Ramsey Torres; Horst Samulowitz; Lisa Amini (IUI Companion, 2020)

Solving Constrained CASH Problems with ADMM. - Parikshit Ram; Sijia Liu 001; Deepak Vijaykeerthy; Dakuo Wang; Djallel Bouneffouf 001; Gregory Bramble; Horst Samulowitz; Alexander G. Gray (CoRR, 2020)

Lale: Consistent Automated Machine Learning. - Guillaume Baudart; Martin Hirzel; Kiran Kate; Parikshit Ram; Avraham Shinnar (CoRR, 2020)

Neural Neighborhood Encoding for Classification. - Kaushik Sinha; Parikshit Ram (CoRR, 2020)

Learned Fine-Tuner for Incongruous Few-Shot Learning. - Pu Zhao 001; Sijia Liu 001; Parikshit Ram; Songtao Lu; Djallel Bouneffouf 001; Xue Lin 001 (CoRR, 2020)

Searching for Machine Learning Pipelines Using a Context-Free Grammar. - Radu Marinescu 002; Akihiro Kishimoto; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 2021)

HyperASPO: Fusion of Model and Hyper Parameter Optimization for Multi-objective Machine Learning. - Aswin Kannan; Anamitra Roy Choudhury; Vaibhav Saxena; Saurabh Raje; Parikshit Ram; Ashish Verma 001; Yogish Sabharwal (IEEE BigData, 2021)

Fruit-fly Inspired Neighborhood Encoding for Classification. - Kaushik Sinha; Parikshit Ram (KDD, 2021)

Pipeline Combinators for Gradual AutoML. - Guillaume Baudart; Martin Hirzel; Kiran Kate; Parikshit Ram; Avraham Shinnar; Jason Tsay (NeurIPS, 2021)

Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization. - Paulito P. Palmes; Akihiro Kishimoto; Radu Marinescu 002; Parikshit Ram; Elizabeth Daly (CoRR, 2021)

Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD. - Chen Fan; Parikshit Ram; Sijia Liu 001 (CoRR, 2021)

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement. - Parikshit Ram; Kaushik Sinha (CoRR, 2021)

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2021)

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. - Akihiro Kishimoto; Djallel Bouneffouf 001; Radu Marinescu 002; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 2022)

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. - Parikshit Ram; Kaushik Sinha (AAAI, 2022)

On the Optimality Gap of Warm-Started Hyperparameter Optimization. - Parikshit Ram (AutoML, 2022)

Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. - Pu Zhao 001; Parikshit Ram; Songtao Lu; Yuguang Yao; Djallel Bouneffouf 001; Xue Lin 001; Sijia Liu 001 (IJCAI, 2022)

Gradual AutoML using Lale. - Martin Hirzel; Kiran Kate; Parikshit Ram; Avraham Shinnar; Jason Tsay (KDD, 2022)

Advancing Model Pruning via Bi-level Optimization. - Yihua Zhang; Yuguang Yao; Parikshit Ram; Pu Zhao 001; Tianlong Chen 001; Mingyi Hong 001; Yanzhi Wang; Sijia Liu 001 (NeurIPS, 2022)

Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. - Yunfei Teng; Anna Choromanska; Murray Campbell; Songtao Lu; Parikshit Ram; Lior Horesh (ECML/PKDD (1), 2022)

An Empirical Study of Modular Bias Mitigators and Ensembles. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (CoRR, 2022)

Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (CoRR, 2022)

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Lily Weng (CoRR, 2022)

Navigating Ensemble Configurations for Algorithmic Fairness. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (CoRR, 2022)

xCloudServing: Automated ML Serving Across Clouds. - Malgorzata Lazuka; Andreea Anghel; Parikshit Ram; Haralampos Pozidis; Thomas P. Parnell (CLOUD, 2023)

Searching for Fairer Machine Learning Ensembles. - Michael Feffer; Martin Hirzel; Samuel C. Hoffman; Kiran Kate; Parikshit Ram; Avraham Shinnar (AutoML, 2023)

Runtime Prediction of Machine Learning Algorithms in Automl Systems. - Parijat Dube; Theodoros Salonidis; Parikshit Ram; Ashish Verma 001 (ICASSP, 2023)

Single-shot General Hyper-parameter Optimization for Federated Learning. - Yi Zhou 015; Parikshit Ram; Theodoros Salonidis; Nathalie Baracaldo; Horst Samulowitz; Heiko Ludwig (ICLR, 2023)

Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Tsui-Wei Weng (ICLR, 2023)

What Is Missing in IRM Training and Evaluation? Challenges and Solutions. - Yihua Zhang; Pranay Sharma; Parikshit Ram; Mingyi Hong 001; Kush R. Varshney; Sijia Liu 001 (ICLR, 2023)

End-to-end Differentiable Clustering with Associative Memories. - Bishwajit Saha; Dmitry Krotov; Mohammed J. Zaki; Parikshit Ram (ICML, 2023)

Model Sparsity Can Simplify Machine Unlearning. - Jinghan Jia; Jiancheng Liu; Parikshit Ram; Yuguang Yao; Gaowen Liu; Yang Liu 018; Pranay Sharma; Sijia Liu 001 (NeurIPS, 2023)

Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection. - Parikshit Ram; Alexander G. Gray; Horst C. Samulowitz; Gregory Bramble (SDM, 2023)

Model Sparsification Can Simplify Machine Unlearning. - Jinghan Jia; Jiancheng Liu; Parikshit Ram; Yuguang Yao; Gaowen Liu; Yang Liu 018; Pranay Sharma; Sijia Liu 001 (CoRR, 2023)

Compositional Program Generation for Systematic Generalization. - Tim Klinger; Luke Liu; Soham Dan; Maxwell Crouse; Parikshit Ram; Alexander Gray 001 (CoRR, 2023)

Effective Data Distillation for Tabular Datasets (Student Abstract). - Inwon Kang; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Oshani Seneviratne (AAAI, 2024)

Enhancing In-context Learning via Linear Probe Calibration. - Momin Abbas; Yi Zhou 015; Parikshit Ram; Nathalie Baracaldo; Horst Samulowitz; Theodoros Salonidis; Tianyi Chen (AISTATS, 2024)

Byzantine-Resilient Bilevel Federated Learning. - Momin Abbas; Yi Zhou 015; Nathalie Baracaldo; Horst Samulowitz; Parikshit Ram; Theodoros Salonidis (SAM, 2024)

What Makes Models Compositional? A Theoretical View. - Parikshit Ram; Tim Klinger; Alexander G. Gray (IJCAI, 2024)

What makes Models Compositional? A Neuro-Symbolic Theoretical View (Extended Abstract). - Parikshit Ram; Tim Klinger; Alexander G. Gray (LNSAI@IJCAI, 2024)

Dense Associative Memory Through the Lens of Random Features. - Benjamin Hoover; Duen Horng Chau; Hendrik Strobelt; Parikshit Ram; Dmitry Krotov (NeurIPS, 2024)

WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. - Jinghan Jia; Jiancheng Liu; Yihua Zhang; Parikshit Ram; Nathalie Baracaldo; Sijia Liu 001 (NeurIPS, 2024)

What makes Models Compositional? A Theoretical View: With Supplement. - Parikshit Ram; Tim Klinger; Alexander G. Gray (CoRR, 2024)

The Importance of Positional Encoding Initialization in Transformers for Relational Reasoning. - Takuya Ito; Luca Cocchi; Tim Klinger; Parikshit Ram; Murray Campbell; Luke J. Hearne (CoRR, 2024)

On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems. - Md. Ibrahim Ibne Alam; Parikshit Ram; Soham Dan; Horst Samulowitz; Koushik Kar (CoRR, 2024)

Mitigating Forgetting in LLM Supervised Fine-Tuning and Preference Learning. - Heshan Devaka Fernando; Han Shen; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Nathalie Baracaldo; Tianyi Chen (CoRR, 2024)

Quantifying artificial intelligence through algebraic generalization. - Takuya Ito; Murray Campbell; Lior Horesh; Tim Klinger; Parikshit Ram (CoRR, 2024)

Neural Reasoning Networks: Efficient Interpretable Neural Networks with Automatic Textual Explanations. - Stephen Carrow; Kyle Erwin; Olga Vilenskaia; Parikshit Ram; Tim Klinger; Naweed Khan; Ndivhuwo Makondo; Alexander G. Gray (AAAI, 2025)

On the Utility of Existing Fine-Tuned Models on Data-Scarce Domains. - Md. Ibrahim Ibne Alam; Parikshit Ram; Soham Dan; Horst Samulowitz; Koushik Kar (Trans. Mach. Learn. Res., 2025)

On Learning Representations for Tabular Data Distillation. - Inwon Kang; Parikshit Ram; Yi Zhou 015; Horst Samulowitz; Oshani Seneviratne (Trans. Mach. Learn. Res., 2025)

Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. - Changsheng Wang; Yihua Zhang; Jinghan Jia; Parikshit Ram; Dennis Wei; Yuguang Yao; Soumyadeep Pal; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

Dense Associative Memory with Epanechnikov Energy. - Benjamin Hoover; Zhaoyang Shi; Krishnakumar Balasubramanian 002; Dmitry Krotov; Parikshit Ram (CoRR, 2025)

Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills. - Changsheng Wang; Chongyu Fan; Yihua Zhang; Jinghan Jia; Dennis Wei; Parikshit Ram; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

Transformers Learn Faster with Semantic Focus. - Parikshit Ram; Kenneth L. Clarkson; Tim Klinger; Shashanka Ubaru; Alexander G. Gray (CoRR, 2025)

Finding Clustering Algorithms in the Transformer Architecture. - Kenneth L. Clarkson; Lior Horesh; Takuya Ito; Charlotte Park; Parikshit Ram (CoRR, 2025)

Modern Methods in Associative Memory. - Dmitry Krotov; Benjamin Hoover; Parikshit Ram; Bao Pham (CoRR, 2025)