Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. Many kinds of research have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is K-Means clustering algorithm. Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is.

I implemented a Softmax Regression classifier using batch gradient descent and early stopping using just NumPy. I tried to follow Scikit-learn's interface approach where possible, with 2 methods available to the user: fit and predict, with options for various levels of regularization and various learning parameters (such as the step size). The ReadMe explains how to use it properly, and the Jupyter notebook demonstrates how to make predictions with it. Let me know what you think and if you've any feedback please share it, thanks:)

Our linear regression has only two predictors (a and b), thus X is a n x 2 matrix (where n is the number of observations and 2 the number of predictors). As you can see, to solve the equation we need to calculate the matrix (X T X) then invert it. In machine learning, the number of observations is often very high as well as the number of predictors. Consequently, this operation is very expensive in terms of calculation and memory. Gradient descent algorithm is an iterative optimization algorithm that allows us to find the solution while keeping the computational complexity low.

Chengrun Yang, Jicong Fan, Ziyang Wu, and Madeleine Udell This is an extended version of AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space (DOI: 10.1145/3394486.3403197) at the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020. Abstract--Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system to address this challenge: an automated system to design a supervised learning pipeline. Our system uses matrix and tensor factorization as surrogate models to model the combinatorial pipeline search space.

In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.

Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.

We address the problem of Federated Learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their own objectives (learning tasks) but by aggregating their data with others in the same cluster (same learning task), they can leverage the strength in numbers in order to perform more efficient Federated Learning. We propose a new framework dubbed the Iterative Federated Clustering Algorithm (IFCA), which alternately estimates the cluster identities of the users and optimizes model parameters for the user clusters via gradient descent. We analyze the convergence rate of this algorithm first in a linear model with squared loss and then for generic strongly convex and smooth loss functions. We show that in both settings, with good initialization, IFCA converges at an exponential rate, and discuss the optimality of the statistical error rate. In the experiments, we show that our algorithm can succeed even if we relax the requirements on initialization with random initialization and multiple restarts. We also present experimental results showing that our algorithm is efficient in non-convex problems such as neural networks and outperforms the baselines on several clustered FL benchmarks created based on the MNIST and CIFAR-10 datasets by $5\sim 8\%$.

Ride-hailing demand prediction is an important prediction task in traffic prediction. An accurate prediction model can help the platform pre-allocate resources in advance to improve vehicle utilization and reduce the wait-time. This task is challenging due to the complicated spatial-temporal relationships among regions. Most existing methods mainly focus on Euclidean correlations among regions. Though there are some methods that use Graph Convolutional Networks (GCN) to capture the non-Euclidean pair-wise correlations, they only rely on the static topological structure among regions. Besides, they only consider fixed graph structures at different time intervals. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Graph Attention Network (STDGAT) to predict the taxi demand of multiple connected regions in the near future. The method uses Graph Attention Network (GAT), which achieves the adaptive allocation of weights for other regions, to capture the spatial information. Furthermore, we implement a Dynamic Graph Attention mode to capture the different spatial relationships at different time intervals based on the actual commuting relationships. Extensive experiments are conducted on a real-world large scale ride-hailing demand dataset, the results demonstrate the superiority of our method over existing methods.

Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers. For example, the reward of a sub-optimal action can be stochastic and sometimes exceeds that of the optimal action, which is common in games or real-world. Trainers are likely to provide positive feedback to sub-optimal actions, negative feedback to the optimal actions and even do not provide feedback in some confusing situations. Existing works, which utilize the Expectation Maximization (EM) algorithm and treat the feedback model as hidden parameters, do not consider uncertainties in the learning environment and human feedback. To address this challenge, we introduce a novel feedback model that considers the uncertainty of human feedback. However, this incurs intractable calculus in the EM algorithm. To this end, we propose a novel approximate EM algorithm, in which we approximate the expectation step with the Gradient Descent method. Experimental results in both synthetic scenarios and two real-world scenarios with human participants demonstrate the superior performance of our proposed approach.

We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This is achieved by introducing a general discrepancy functional that rigorously quantifies violations of this criterion. This differentiable functional is used as a penalty driving the model parameters towards equalized odds. To rigorously evaluate fitted models, we develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature. Both the model fitting and hypothesis testing leverage a resampled version of the sensitive attribute obeying equalized odds, by construction. We demonstrate the applicability and validity of the proposed framework both in regression and multi-class classification problems, reporting improved performance over state-of-the-art methods. Lastly, we show how to incorporate techniques for equitable uncertainty quantification---unbiased for each group under study---to communicate the results of the data analysis in exact terms.