Collaborating Authors


Fine-Tuning ML Hyperparameters


"Just as electricity transformed almost every industry 100 years ago, today I actually have hard time thinking of an industry that I don't think AI (Artificial Intelligence) will transform in the next several years" -- Andrew NG I have long been fascinated with these algorithms, capable of something that we can as humans barely begin to comprehend. However, even with all these resources one of the biggest setbacks any ML practitioner has ever faced would be tuning the model's hyperparameters. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can be trained on different constraints, learning rates or kernels and other such parameters to generalize to different datasets, and hence these instructions have to be tuned so that the model can optimally solve the machine learning problem.

Moment-Based Domain Adaptation: Learning Bounds and Algorithms Machine Learning

This thesis contributes to the mathematical foundation of domain adaptation as emerging field in machine learning. In contrast to classical statistical learning, the framework of domain adaptation takes into account deviations between probability distributions in the training and application setting. Domain adaptation applies for a wider range of applications as future samples often follow a distribution that differs from the ones of the training samples. A decisive point is the generality of the assumptions about the similarity of the distributions. Therefore, in this thesis we study domain adaptation problems under as weak similarity assumptions as can be modelled by finitely many moments.

Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection Machine Learning

Anomaly detection is a method for discovering unusual and suspicious behavior. In many real-world scenarios, the examined events can be directly linked to the actions of an adversary, such as attacks on computer networks or frauds in financial operations. While the defender wants to discover such malicious behavior, the attacker seeks to accomplish their goal (e.g., exfiltrating data) while avoiding the detection. To this end, anomaly detectors have been used in a game-theoretic framework that captures these goals of a two-player competition. We extend the existing models to more realistic settings by (1) allowing both players to have continuous action spaces and by assuming that (2) the defender cannot perfectly observe the action of the attacker. We propose two algorithms for solving such games -- a direct extension of existing algorithms based on discretizing the feature space and linear programming and the second algorithm based on constrained learning. Experiments show that both algorithms are applicable for cases with low feature space dimensions but the learning-based method produces less exploitable strategies and it is scalable to higher dimensions. Moreover, we use real-world data to compare our approaches with existing classifiers in a data-exfiltration scenario via the DNS channel. The results show that our models are significantly less exploitable by an informed attacker.

AutoAI set to make it easy to create machine learning algorithms


Artificial intelligence has the potential to greatly simplify our lives – but not everyone is a data scientist and not all data scientists are experts in machine learning. Enter AutoAI – a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail. Most AutoAI research currently focuses on three areas: automatically determining the best models for each step of the desired machine learning and data science pipeline (model selection), automatically finding the best architecture of a deep learning-based AI model, and automatically finding the best hyperparameters (parameters for the model training process) for AI models and algorithms.

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder Machine Learning

Learning disentanglement aims at finding a low dimensional representation, which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder is commonly used to disentangle independent factors from observations. However, in real scenarios, the factors with semantic meanings are not necessarily independent. Instead, there might be an underlying causal structure due to physics laws. We thus propose a new VAE based framework named CausalVAE, which includes causal layers to transform independent factors into causal factors that correspond to causally related concepts in data. We analyze the model identifiabitily of CausalVAE, showing that the generative model learned from the observational data recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic datasets consisting of pictures with multiple causally related objects abstracted from physical world, and a benchmark face dataset CelebA. The results show that the causal representations by CausalVAE are semantically interpretable, and lead to better results on downstream tasks. The new framework allows causal intervention, by which we can intervene any causal concepts to generate artificial data.

Geometry-Aware Gradient Algorithms for Neural Architecture Search Machine Learning

Many recent state-of-the-art methods for neural architecture search (NAS) relax the NAS problem into a joint continuous optimization over architecture parameters and their shared-weights, enabling the application of standard gradient-based optimizers. However, this training process remains poorly understood, as evidenced by the multitude of gradient-based heuristics that have been recently proposed. Invoking the theory of mirror descent, we present a unifying framework for designing and analyzing gradient-based NAS methods that exploit the underlying problem structure to quickly find high-performance architectures. Our geometry-aware framework leads to simple yet novel algorithms that (1) enjoy faster convergence guarantees than existing gradient-based methods and (2) achieve state-of-the-art accuracy on the latest NAS benchmarks in computer vision. Notably, we exceed the best published results for both CIFAR and ImageNet on both the DARTS search space and NAS-Bench-201; on the latter benchmark we achieve close to oracle-optimal performance on CIFAR-10 and CIFAR-100. Together, our theory and experiments demonstrate a principled way to co-design optimizers and continuous parameterizations of discrete NAS search spaces.

Local Model Feature Transformations Machine Learning

Local learning methods are a popular class of machine learning algorithms. The basic idea for the entire cadre is to choose some non-local model family, to train many of them on small sections of neighboring data, and then to `stitch' the resulting models together in some way. Due to the limits of constraining a training dataset to a small neighborhood, research on locally-learned models has largely been restricted to simple model families. Also, since simple model families have no complex structure by design, this has limited use of the individual local models to predictive tasks. We hypothesize that, using a sufficiently complex local model family, various properties of the individual local models, such as their learned parameters, can be used as features for further learning. This dissertation improves upon the current state of research and works toward establishing this hypothesis by investigating algorithms for localization of more complex model families and by studying their applications beyond predictions as a feature extraction mechanism. We summarize this generic technique of using local models as a feature extraction step with the term ``local model feature transformations.'' In this document, we extend the local modeling paradigm to Gaussian processes, orthogonal quadric models and word embedding models, and extend the existing theory for localized linear classifiers. We then demonstrate applications of local model feature transformations to epileptic event classification from EEG readings, activity monitoring via chest accelerometry, 3D surface reconstruction, 3D point cloud segmentation, handwritten digit classification and event detection from Twitter feeds.

Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization Machine Learning

We consider the least squares regression problem, penalized with a combination of the $\ell_{0}$ and $\ell_{2}$ norms (a.k.a. $\ell_0 \ell_2$ regularization). Recent work presents strong evidence that the resulting $\ell_0$-based estimators can outperform popular sparse learning methods, under many important high-dimensional settings. However, exact computation of $\ell_0$-based estimators remains a major challenge. Indeed, state-of-the-art mixed integer programming (MIP) methods for $\ell_0 \ell_2$-regularized regression face difficulties in solving many statistically interesting instances when the number of features $p \sim 10^4$. In this work, we present a new exact MIP framework for $\ell_0\ell_2$-regularized regression that can scale to $p \sim 10^7$, achieving over $3600$x speed-ups compared to the fastest exact methods. Unlike recent work, which relies on modern MIP solvers, we design a specialized nonlinear BnB framework, by critically exploiting the problem structure. A key distinguishing component in our algorithm lies in efficiently solving the node relaxations using specialized first-order methods, based on coordinate descent (CD). Our CD-based method effectively leverages information across the BnB nodes, through using warm starts, active sets, and gradient screening. In addition, we design a novel method for obtaining dual bounds from primal solutions, which certifiably works in high dimensions. Experiments on synthetic and real high-dimensional datasets demonstrate that our method is not only significantly faster than the state of the art, but can also deliver certifiably optimal solutions to statistically challenging instances that cannot be handled with existing methods. We open source the implementation through our toolkit L0BnB.

Global Expanding, Local Shrinking: Discriminant Multi-label Learning with Missing Labels Machine Learning

In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. However, these methods just utilize global low-rank label structure, ignore both local low-rank label structures and label discriminant information to some extent, leaving room for further performance improvement. In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predictions of instances from the same labels (local shrinking of rank), and a maximally separated structure (high-rank structure) on the predictions of instances from different labels (global expanding of rank). In this way, these imposed low-rank structures can help modeling both local and global low-rank label structures, while the imposed high-rank structure can help providing more underlying discriminability. Our subsequent theoretical analysis also supports these intuitions. In addition, we provide a nonlinear extension via using kernel trick to enhance DM2L and establish a concave-convex objective to learn these models. Compared to the other methods, our method involves the fewest assumptions and only one hyper-parameter. Even so, extensive experiments show that our method still outperforms the state-of-the-art methods.

GeneCAI: Genetic Evolution for Acquiring Compact AI Machine Learning

In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy such compute-intensive architectures on resource-limited mobile devices. Such methods comprise various hyper-parameters that require per-layer customization to ensure high accuracy. Choosing such hyper-parameters is cumbersome as the pertinent search space grows exponentially with model layers. This paper introduces GeneCAI, a novel optimization method that automatically learns how to tune per-layer compression hyper-parameters. We devise a bijective translation scheme that encodes compressed DNNs to the genotype space. The optimality of each genotype is measured using a multi-objective score based on accuracy and number of floating point operations. We develop customized genetic operations to iteratively evolve the non-dominated solutions towards the optimal Pareto front, thus, capturing the optimal trade-off between model accuracy and complexity. GeneCAI optimization method is highly scalable and can achieve a near-linear performance boost on distributed multi-GPU platforms. Our extensive evaluations demonstrate that GeneCAI outperforms existing rule-based and reinforcement learning methods in DNN compression by finding models that lie on a better accuracy-complexity Pareto curve.