United States

Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. We focus on materials science applications, where interpretable predictive modeling may provide principled understanding of materials systems and guide new materials discovery. It is often computationally prohibitive to exhaust all the potential relationships to construct and search the whole feature space to identify interpretable and predictive features. We develop and evaluate new AFE strategies by exploring a feature generation tree (FGT) with deep Q-network (DQN) for scalable and efficient exploration policies. The developed DQN-based AFE strategies are benchmarked with the existing AFE methods on several materials science datasets.

AAAI 2021

Physics-Constrained Automatic Feature Engineering for Predictive Modeling in Materials Science

#### Welcome to the 60th IEEE International Reliability Physics Symposium (IRPS)!

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**General Chair Welcome Message**

Welcome to IRPS 2022! When the IRPS Management Committee began making plans for Dallas, we recognized the strong desire by the reliability physics community to return to a face-to-face symposium that we last had in Monterey in 2019. But as time progressed, we recognized that travel restrictions were not being lifted as rapidly as we anticipated. Rather than leave out those of you who could not attend in person, we adopted a hybrid format allowing for both remote attendance and remote presentations. This is a first for IRPS and I am confident it will be a success for all.

With this new hybrid format, the symposium will return to the original five-day format with Tutorials and Year in Review on the first two days. The Technical Program, Keynote Talks, Workshops and Poster session will be held on the following three days. All of these (except Workshops and Posters) will be available via real-time streaming in the US Central Time zone with virtual attendees having Q&A access to the presenters. For those virtual attendees who cannot watch the live presentations, the material will be available on demand to all attendees for a month after the symposium with the ability to communicate with the authors.

In 2020, we were planning on co-hosting the International ESD Workshop (IEW) ESD workshop when the COVID-19 pandemic hit. For 2022, IEW will be seamlessly embedded into the IRPS Program and will sponsor three tutorials, an invited talk, two technical sessions and two evening workshops focused on ESD and latch-up. And we continue our
collaboration with our sister conference, European Symposium on Reliability of Electron Devices Failure Physics and Analysis (ESREF) by hosting their Best Paper and the International Integrated Reliability Workshop (IIRW) by hosting their Best Student Paper.

Our primary objective is to maintain the symposium’s leadership role in the reliability physics of microelectronic devices by attracting leading researchers to present their work and providing attendees the best place to access this information. With all the buzz in the media about whether Moore’s Law has hit its limit, IRPS has been promoting expertise developed in silicon-based reliability over the past sixty years and expanding into new material areas. This year, eight of the technical sessions are devoted to reliability in topics outside of traditional silicon, including wide bandgap GaN/SiC, emerging memory and neuromorphic computing. I hope our ability to draw these communities together will accelerate the cross-fertilization of ideas. 

The Technical Program consists of four keynote speakers, twenty-three invited speakers and eighty-seven oral presentations organized in thirty technical sessions on eighteen reliability topics. Over two evenings, there will be ten Workshops and sixty-six Poster presentations that are designed to allow in person attendees and experts to interact
with one another. With eighteen tutorials covering topics ranging from fundamental transistor and memory reliability all the way up to automotive and complex system-level reliability, this year’s symposium provides an excellent learning opportunity for engineers and scientists new to reliability as well as experienced individuals wanting to explore new
areas. The Year in Review, which serves as a summary of research in the past year, will cover 3D packaging, emerging memory and aging aware circuit designs.

Finally, I would like to express my extreme gratitude to our patrons and exhibitors, whose support makes IRPS possible.
And to all of you, our in person and virtual attendees, for supporting this symposium for sixty years!

**Charles W. Slayman, General Chair** 
**of 2022 IRPS Management Committee**
 
**About IRPS**

The IEEE International Reliability Physics Symposium (IRPS) is the premiere conference for engineers and scientists to present new and original work in the area of microelectronics reliability. Drawing participants from the United States, Europe, Asia, and all parts of the world, IRPS seeks to understand the reliability of semiconductor devices, integrated circuits, and microelectronic assemblies through an improved understanding of both the physics of failure as well as the application environment. IRPS provides numerous opportunities for attendees to increase their knowledge and understanding of all aspects of microelectronics reliability, and to meet and network with reliability colleagues from around the world.

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IRPS 2022 Main Conference 

IRPS provides numerous opportunities for attendees to increase their knowledge and understanding of all aspects of microelectronics reliability. It is also an outstanding chance to meet and network with reliability colleagues from around the world.

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Since the recent studies  (KDD'20) done by Krichene and Rendle on the sampling based top-k evaluation metric for recommendation,  there have been a lot of debate on the validity of using sampling for evaluating recommendation algorithms. Though their work and the recent work done by Li et. al. (KDD'20) have proposed some basic approach for mapping the sampling based metrics to their counter-part in the global evaluation which uses the entire dataset, there is still lack of understanding how sampling should be used for recommendation evaluation, and the proposed approaches either are rather ad-hoc or can only work on simple metrics, like Recall/Hit-Ratio. In this paper, we introduce some principled approach to derive the estimators of top-k metric based on sampling. Our approaches utilize the weighted MLE and maximal entropy approach to recover the global rank distribution and then utilize that for estimation. The experimental results shows significant advantages of using our approaches for evaluating recommendation algorithms based on top-k metrics.

On Estimating Recommendation Evaluation Metrics under Sampling

Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.

Robust Fairness under Covariate Shift

Multi-task learning has increased in importance due to its superior performance by learning multiple different tasks simultaneously and its ability to perform several different tasks using a single model. In medical phenotyping, task labels are costly to acquire and might contain a certain degree of label noise. This decreases the efficiency of using additional human labels as auxiliary tasks when applying multi-task learning to medical phenotyping. In this work, we proposed an effective multi-task learning framework, CO-TASK, to boost multi-task learning performance by generating auxiliary tasks through COmbination of TASK Labels. The proposed CO-TASK framework generates auxiliary tasks without additional labeling effort, is robust to a certain degree of label noise, and can be applied in parallel with various multi-task learning techniques.

We evaluated our performance using the CIFAR-MTL dataset and demonstrated its effectiveness in medical phenotyping using two large-scale ECG phenotyping datasets, an 18 diseases multi-label ECG-P18 dataset and an echocardiogram diagnostic from electrocardiogram dataset ECG-EchoLVH. On the CIFAR-MTL dataset, we doubled the average per-task performance gain of the multi-task learning model from 4.38% to 9.78%. With the proposed task-aware imbalance data sampler, the CO-TASK framework can effectively deal with the different imbalance ratios for the different tasks in electrocardiogram phenotyping datasets. The proposed framework combined with noisy annotations as minor tasks increased the sensitivity by 7.1% compared to the single-task model while maintaining the same specificity as the doctor annotations on the ECG-EchoLVH dataset.

Boosting Multi-task Learning through Combination of Task Labels - with Applications in ECG Phenotyping

Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.

Estimating Identifiable Causal Effects through Double Machine Learning

Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks. The initial impression driven by our experimental results suggests that data heterogeneity is the dominant factor in the effectiveness of attacks and it may be a redemption for defending against backdooring as it makes the attack less efficient, more challenging to design effective attack strategies, and the attack result also becomes less predictable. However, with further investigations, we found data heterogeneity is more of a curse than a redemption as the attack effectiveness can be significantly boosted by simply adjusting the client-side backdooring timing. More importantly, data heterogeneity may result in overfitting at the local training of benign clients, which can be utilized by attackers to disguise themselves and fool skewed-feature based defenses. In addition, effective attack strategies can be made by adjusting attack data distribution. Finally, we discuss the potential directions of defending the curses brought by data heterogeneity. The results and lessons learned from our extensive experiments and analysis offer new insights for designing robust federated learning methods and systems.

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our goal is to design a differentially private LP  algorithm, which trades off between privacy of the protected node-pairs and the link prediction accuracy. More specifically, we first propose a form of differential privacy on graphs, which models the privacy loss only of those node-pairs which are marked as protected. Next, we develop DPLP, a learning to rank algorithm, which applies a monotone transform to base scores from a non-private LP system, and then adds noise.  DPLP is trained with a privacy induced ranking loss, which optimizes the ranking utility for a given maximum allowed level of privacy leakage of the protected node-pairs. Under a recently introduced latent node embedding model, we present a formal trade-off between privacy and LP utility. Extensive experiments with several real-life graphs and several LP heuristics show that DPLP can trade off between privacy and predictive performance more effectively than several alternatives.

Differentially Private Link Prediction with Protected Connections

Learning causal models with latent variables from observational and experimental data is an important problem. In this paper we present a polynomial-time algorithm that PAC learns the structure and parameters of a rooted  tree-structured causal network of bounded degree where the internal nodes of the tree cannot be observed or manipulated. Our algorithm is the first of its kind to provably learn the structure and parameters of tree-structured causal models with latent internal variables from random examples and active experiments.

PAC Learning of Causal Trees with Latent Variables

Competing Risk Analysis (CRA) aims at the correct estimation of the marginal probability of occurrence of an event in the presence of competing events. Many of the statistical approaches developed for CRA are limited by strong assumptions about the underlying stochastic processes. To overcome these issues and to handle censoring, machine learning approaches for CRA have designed specialized cost functions. However, these approaches are not generalizable, and are computationally expensive. This paper formulates CRA as a cause-specific regression problem and proposes DeepPseudo models, which use simple and effective feed-forward deep neural networks, to predict the cumulative incidence function (CIF) using Aalen-Johansen estimator-based pseudo values. DeepPseudo models capture the time-varying covariate effect on CIF while handling the censored observations. We show how DeepPseudo models can address co-variate dependent censoring by using modified pseudo values. Experiments on real and synthetic datasets demonstrate that our proposed models obtain promising and statistically significant results compared to the state-of-the-art CRA approaches. Furthermore, we show that explainable methods such as Layer-wise Relevance Propagation can be used to interpret the predictions of our DeepPseudo models.

DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis

After observing a snapshot of a social network, a link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate a score for currently non-neighboring node pairs, and rank them by this score.   Recent LP systems compute this score by comparing dense, low dimensional vector representations of nodes.  Graph neural networks (GNNs), in particular graph convolutional networks (GCNs), are popular examples.  For two nodes to be meaningfully compared, their embeddings should be indifferent to reordering of their neighbors.  GNNs typically use simple, symmetric set aggregators to ensure this property, but this design decision has been shown to produce representations with limited expressive power.  Sequence encoders are more expressive, but are permutation sensitive by design.  Recent efforts to overcome this dilemma turn out to be unsatisfactory for LP tasks. In response, we propose PermGNN, which aggregates neighbor features using a recurrent, order-sensitive aggregator and directly minimizes an LP loss while it is `attacked' by adversarial generator of neighbor permutations.  PermGNN has superior expressive power compared to earlier GNNs.  Next, we devise an optimization framework to map PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds up reporting the top-K most likely edges for the LP task. Our experiments on diverse datasets show that PermGNN outperforms several state-of-the-art link predictors by a significant margin, and can predict the most likely edges fast.

Adversarial Permutation Guided Node Representations for Link Prediction

As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc., the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classification and segmentation, synthesizing point clouds remains challenging, especially from a single image. State-of-the-art (SOTA) approaches can generate a point cloud from a hidden vector, however, they treat 2D and 3D features equally and disregard the rich shape information within the 3D data. In this paper, we address this problem by integrating image features with 3D prototype features. Specifically, we propose to learn a set of 3D prototype features from a real point cloud dataset and dynamically adjust them through the training. These prototypes are then integrated with incoming image features to guide the point cloud generation process. Experimental results show that our proposed method outperforms SOTA methods on single image based 3D reconstruction tasks.

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