Search Results for author:
Found 24 papers, 7 papers with code
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
This paper is the first to propose a generic min-max bilevel multi-objective optimization framework, highlighting applications in representation learning and hyperparameter optimization.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
An Empirical Study of Modular Bias Mitigators and Ensembles
A popular approach to train more stable models is ensemble learning.
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
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.
Pipeline Combinators for Gradual AutoML
Automated machine learning (AutoML) can make data scientists more productive.
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD
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.
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
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
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.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization
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.
Learned Fine-Tuner for Incongruous Few-Shot Adversarial Learning
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.
Neural Neighborhood Encoding for Classification
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.
Lale: Consistent Automated Machine Learning
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
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.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
Type-Driven Automated Learning with Lale
Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines.
An ADMM Based Framework for AutoML Pipeline Configuration
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
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.
Which Space Partitioning Tree to Use for Search?
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
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
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
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).
