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In online applications with streaming data, awareness of how far the empirical training or test data has shifted away from its original data distribution can be crucial to the performance of the model. However, historical samples in the data stream may not be kept either due to space requirements or for regulatory reasons. To cope with such situations, we propose Continual Density Ratio Estimation (CDRE

Recommending sets of items that include both personalized and compatible items is crucial to personalized styling programs such as Amazon’s Personal Shopper. There is both an extensive literature on learning generic fashion compatibility and also on personalization in fashion. However, recommending pairs of items that the customer would like to wear together is still less studied as it involves learning

We present a privacy preserving machine learning method for images that separates task-relevant information from task-irrelevant information. Our primary hypothesis is that by revealing the minimal number of pixels required for a task we can provide the most privacy preserving guarantees. Specifically, we propose an adversarial method that masks out task-irrelevant information from an image for preserving

Due to intense competition and lack of real estate on the front page of large e-commerce platforms, sellers are sometimes motivated to garner non-genuine signals (clicks, add-to-carts, purchases) on their products, to make them appear more appealing to customers. This hurts customers’ trust on the platform, and also hurts genuine sellers who sell their items without looking to game the system. While it

Batch-mode active learning iteratively selects a batch of unlabeled samples for labelling to maximize model performance and reduce total runtime. To select the most informative and diverse batch, existing methods usually calculate the correlation between samples within a batch, leading to combinatorial optimization problems which are inefficient, complex, and limited to linear models for approximated solutions

Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts. This paper presents LUMINOUS, the first research framework that employs stateof-the-art indoor scene

Graph neural networks (GNNs) are a new class of powerful machine learning models, but easy programming and efficient computing is often at odds. Current GNN frameworks are based on a message passing paradigm, and allow the concise expression of GNN models using built-in primitives and user defined functions (UDFs). While built-in primitives offer high performance, they are limited in expressiveness; UDFs

We consider the framework of non-stationary Online Convex Optimization where a learner seeks to control its dynamic regret against an arbitrary sequence of comparators. When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of path variation VT of the comparator sequence. Specifically

We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime. We show that, for local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution p_(noisy) of a generic noisy circuit instance and the output distribution pideal of the corresponding noiseless instance shrink exponentially

The rise of programmable quantum devices has motivated the exploration of circuit models which could realize novel physics. A promising candidate is a class of hybrid circuits, where entangling unitary dynamics compete with disentangling measurements. Novel phase transitions between different entanglement regimes have been identified in their dynamical states, with universal properties hinting at unexplored

The Eigenstate Thermalization Hypothesis (ETH) has played a major role in explaining thermodynamic phenomena in quantum systems. However, so far, no connection has been known between ETH and the timescale of thermalization. In this paper, we rigorously show that ETH indeed implies fast thermalization to the global Gibbs state. We show fast convergence for two models of thermalization. In the first, the

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. The Alexa Prize Socialbot Grand Challenge was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the fourth iteration of the competition, university teams have incorporated semantic parsing, common

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review

Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs). We show how this problem can be approached using tools from the goodness-of-fit (GoF) testing literature

Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions(e.g.,M4andM5). This practical success

Many complex time series can be effectively subdivided into distinct regimes that exhibit persistent dynamics. Discovering the switching behavior and the statistical patterns in these regimes is important for understanding the underlying dynamical system. We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent

Probabilistic inference of Neural Network parameters is challenging due to the highly multi-modal likelihood functions. Most importantly, the permutation invariance of the neurons of the hidden layers renders the likelihood function unidentifiable with a factorial number of equivalent (symmetric) modes, independent of the data. We show that variational Bayesian methods that approximate the (multi-modal)

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature

In top-k ranked retrieval the goal is to efficiently compute an ordered list of the highest scoring k documents according to some stipulated similarity function such as the well-known BM25 approach. In most implementation techniques a min-heap of size k is used to track the top scoring candidates. In this work we consider the question of how best to retrieve the second page of search results, given that

As cloud computing resources become more adopted, the infrastructures in which they are used naturally grow in the amount of resources and overall complexity, becoming harder to manage. Infrastructure-as-Code (IaC) is presented as a solution to this problem, allowing developers to manage and provision these cloud resources programmatically. The infrastructure is then maintained through a code base, allowing