Search Results for author: Parikshit Ram

Found 24 papers, 7 papers with code

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning

no code implementations3 Mar 2022 Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng

This paper is the first to propose a generic min-max bilevel multi-objective optimization framework, highlighting applications in representation learning and hyperparameter optimization.

Bilevel Optimization Hyperparameter Optimization +3

FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning

no code implementations15 Dec 2021 Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig

We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).

Federated Learning

Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement

1 code implementation14 Dec 2021 Parikshit Ram, Kaushik Sinha

The mathematical formalization of a neurological mechanism in the olfactory circuit of a fruit-fly as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various machine learning tasks such as similarity search, outlier detection and text embeddings.

Federated Learning Outlier Detection

Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD

1 code implementation15 Sep 2021 Chen Fan, Parikshit Ram, Sijia Liu

The key enabling technique is to interpret MAML as a bilevel optimization (BLO) problem and leverage the sign-based SGD(signSGD) as a lower-level optimizer of BLO.

Bilevel Optimization Few-Shot Image Classification +1

Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization

1 code implementation2 Jul 2021 Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, Elizabeth Daly

The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.


Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance

no code implementations ICML Workshop AutoML 2021 Parikshit Ram, Alexander G. Gray, Horst Samulowitz

The tradeoffs in the excess risk incurred from data-driven learning of a single model has been studied by decomposing the excess risk into approximation, estimation and optimization errors.

Hyperparameter Optimization

Overcoming Catastrophic Forgetting via Direction-Constrained Optimization

1 code implementation25 Nov 2020 Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh

We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions.

Continual Learning

Learned Fine-Tuner for Incongruous Few-Shot Adversarial Learning

no code implementations29 Sep 2020 Pu Zhao, Sijia Liu, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin

As novel contributions, we show that the use of LFT within MAML (i) offers the capability to tackle few-shot learning tasks by meta-learning across incongruous yet related problems and (ii) can efficiently work with first-order and derivative-free few-shot learning problems.

Adversarial Attack Few-Shot Learning

Neural Neighborhood Encoding for Classification

no code implementations19 Aug 2020 Kaushik Sinha, Parikshit Ram

Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [Dasgupta et al., 2018] is able to efficiently summarize the data with a single pass and has been used for novelty detection.

Classification General Classification +1

Lale: Consistent Automated Machine Learning

1 code implementation4 Jul 2020 Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies.

Solving Constrained CASH Problems with ADMM

no code implementations17 Jun 2020 Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray

The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.


Type-Driven Automated Learning with Lale

2 code implementations24 May 2019 Martin Hirzel, Kiran Kate, Avraham Shinnar, Subhrajit Roy, Parikshit Ram

Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines.

Time Series

An ADMM Based Framework for AutoML Pipeline Configuration

no code implementations1 May 2019 Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.


Plug-and-play dual-tree algorithm runtime analysis

no code implementations21 Jan 2015 Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram

In this paper, we present a problem-independent runtime guarantee for any dual-tree algorithm using the cover tree, separating out the problem-dependent and the problem-independent elements.

Density Estimation

Which Space Partitioning Tree to Use for Search?

no code implementations NeurIPS 2013 Parikshit Ram, Alexander Gray

We consider the task of nearest-neighbor search with the class of binary-space-partitioning trees, which includes kd-trees, principal axis trees and random projection trees, and try to rigorously answer the question which tree to use for nearest-neighbor search?''


Maximum Inner-Product Search using Tree Data-structures

1 code implementation28 Feb 2012 Parikshit Ram, Alexander G. Gray

Finally we present a new data structure for increasing the efficiency of the dual-tree algorithm.

Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions

no code implementations NeurIPS 2009 Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray

The long-standing problem of efficient nearest-neighbor (NN) search has ubiquitous applications ranging from astrophysics to MP3 fingerprinting to bioinformatics to movie recommendations.

Linear-time Algorithms for Pairwise Statistical Problems

no code implementations NeurIPS 2009 Parikshit Ram, Dongryeol Lee, William March, Alexander G. Gray

Several key computational bottlenecks in machine learning involve pairwise distance computations, including all-nearest-neighbors (finding the nearest neighbor(s) for each point, e. g. in manifold learning) and kernel summations (e. g. in kernel density estimation or kernel machines).

Density Estimation

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