For the vast majority of queries (for example, navigation, simple fact lookup, and others), search engines do extremely well. Their ability to quickly provide answers to queries is a remarkable testament to the power of many of the fundamental methods of AI. They also highlight many of the issues that are common to sophisticated AI question-answering systems. It has become clear that people think of search programs in ways that are very different from traditional information sources. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front-end to the Internet.

We study derivative-free optimization for convex functions where we further assume that function evaluations are unavailable. Instead, one only has access to a comparison oracle, which, given two points $x$ and $y$, and returns a single bit of information indicating which point has larger function value, $f(x)$ or $f(y)$, with some probability of being incorrect. This probability may be constant or it may depend on $|f(x)-f(y)|$. Previous algorithms for this problem have been hampered by a query complexity which is polynomially dependent on the problem dimension, $d$. We propose a novel algorithm that breaks this dependence: it has query complexity only logarithmically dependent on $d$ if the function in addition has low dimensional structure that can be exploited. Numerical experiments on synthetic data and the MuJoCo dataset show that our algorithm outperforms state-of-the-art methods for comparison based optimization, and is even competitive with other derivative-free algorithms that require explicit function evaluations.

The decision boundary of a learning algorithm applied to a given task can be viewed as the outcome of a random process: (i) generate a training set and, (ii) apply to it the, potentially randomized, learning algorithm. Recall, see Definitions 4 and 5, that a query-bounded adversary does not know the sample on which the model was trained nor the randomness used by the learner. This means that if the decision boundary has high entropy then the adversary needs to ask many questions to recover the boundary to a high degree of precision. This suggest that high-entropy decision boundaries are robust against query-bounded adversaries since intuitively it is clear that an approximate knowledge of the decision boundary is a prerequisite for a successful attack. Following this reasoning, we present two instances where high entropy of the decision boundary leads to security.

Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams. For the first time, this paper taps on the potential of transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges this task entails. Rather than training a language-visual transformer from scratch we rely on pre-trained transformers, fine-tuning and ensembling. We add bottom-up and top-down attention to identify regions of interest corresponding to diagram constituents and their relationships, improving the selection of relevant visual information for each question and answer options. Our system ISAAQ reports unprecedented success in all TQA question types, with accuracies of 81.36%, 71.11% and 55.12% on true/false, text-only and diagram multiple choice questions. ISAAQ also demonstrates its broad applicability, obtaining state-of-the-art results in other demanding datasets.

This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles. We extract five concept types: Cancer, Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts were annotated with these concepts following standard double-annotation procedures. We then experiment with BERT-based models for concept extraction. The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1. Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.

In this paper, we propose a multi-task learning-based framework that utilizes a combination of self-supervised and supervised pre-training tasks to learn a generic document representation. We design the network architecture and the pre-training tasks to incorporate the multi-modal document information across text, layout, and image dimensions and allow the network to work with multi-page documents. We showcase the applicability of our pre-training framework on a variety of different real-world document tasks such as document classification, document information extraction, and document retrieval. We conduct exhaustive experiments to compare performance against different ablations of our framework and state-of-the-art baselines. We discuss the current limitations and next steps for our work.

Transformer models have shown impressive performance on a variety of NLP tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP classification tasks, reducing the need for large amounts of additional training data. However, little research has addressed how much data is required to accurately fine-tune such pre-trained transformer models, and how much data is needed for accurate prediction. This paper explores the usability of BERT (a Transformer model for word embedding) for gender prediction on social media. Forensic applications include detecting gender obfuscation, e.g. males posing as females in chat rooms. A Dutch BERT model is fine-tuned on different samples of a Dutch Twitter dataset labeled for gender, varying in the number of tweets used per person. The results show that finetuning BERT contributes to good gender classification performance (80% F1) when finetuned on only 200 tweets per person. But when using just 20 tweets per person, the performance of our classifier deteriorates non-steeply (to 70% F1). These results show that even with relatively small amounts of data, BERT can be fine-tuned to accurately help predict the gender of Twitter users, and, consequently, that it is possible to determine gender on the basis of just a low volume of tweets. This opens up an operational perspective on the swift detection of gender.

Tackling the information retrieval gap between non-technical database end-users and those with the knowledge of formal query languages has been an interesting area of data management and analytics research. The use of natural language interfaces to query information from databases offers the opportunity to bridge the communication challenges between end-users and systems that use formal query languages. Previous research efforts mainly focused on developing structured query interfaces to relational databases. However, the evolution of unstructured big data such as text, images, and video has exposed the limitations of traditional structured query interfaces. While the existing web search tools prove the popularity and usability of natural language query, they return complete documents and web pages instead of focused query responses and are not applicable to database systems. This paper reports our study on the design and development of a natural language query interface to a backend relational database. The novelty in the study lies in defining a graph database as a middle layer to store necessary metadata needed to transform a natural language query into structured query language that can be executed on backend databases. We implemented and evaluated our approach using a restaurant dataset. The translation results for some sample queries yielded a 90% accuracy rate.

The theory of computation is one of the crown jewels of the computer science curriculum. It stretches from the discovery of mathematical problems, such as the halting problem, that cannot be solved by computers, to the most celebrated open problem in computer science today: the P vs. NP question. Since the founding of our discipline by Church and Turing in the 1930s, the theory of computation has addressed some of the most fundamental questions about computers: What does it mean to compute the solution to a problem? Which problems can be solved by computers? Which problems can be solved efficiently, in theory and in practice?

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.