Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values to efficiently explore a software component's input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.

In my previous article we discussed how to solve the Travelling Salesman Problem (TSP) using the meta-heuristic optimisation algorithm of Simulated Annealing. The TSP is a famous combinatorial optimisation and operations research problem. Its objective is to find the shortest distance a salesman can travel through n cities by visiting each city once and ending in the original/starting city. The problem sounds simple, however as we add more cities the number of possible routes is subject to a combinatorial explosion. For example, with 4 cities the number of possible routes is 3, 6 cities it is 60, however for 20 cities its a gigantic 60,822,550,200,000,000!

With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads. Existing single-vehicle planning algorithms on board struggle to handle sophisticated social interactions in the real world. Decisions made by these methods are difficult to understand for humans, raising the risk of crashes and making them unlikely to be applied in practice. Moreover, vehicle flows produced by open-source traffic simulators suffer from being overly conservative and lacking behavioral diversity. We propose a hierarchical multi-vehicle decision-making and planning framework with several advantages. The framework jointly makes decisions for all vehicles within the flow and reacts promptly to the dynamic environment through a high-frequency planning module. The decision module produces interpretable action sequences that can explicitly communicate self-intent to the surrounding HVs. We also present the cooperation factor and trajectory weight set, bringing diversity to autonomous vehicles in traffic at both the social and individual levels. The superiority of our proposed framework is validated through experiments with multiple scenarios, and the diverse behaviors in the generated vehicle trajectories are demonstrated through closed-loop simulations.

Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understanding in 3D environments and whether they implicitly acquire 3D object knowledge, e.g. about different views of an object. In this paper, we investigate whether a state-of-the-art language and vision model, CLIP, is able to ground perspective descriptions of a 3D object and identify canonical views of common objects based on text queries. We present an evaluation framework that uses a circling camera around a 3D object to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions. We find that a pre-trained CLIP model performs poorly on most canonical views and that fine-tuning using hard negative sampling and random contrasting yields good results even under conditions with little available training data.

Abstract--This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device-and topology-heterogeneous ML clusters. We novelly combine both the DNN computation graph and the device topology graph as input to a graph neural network (GNN), and join the GNN with a search-based method to quickly identify optimized distributed training strategies. To reduce communication in a heterogeneous cluster, we further explore a lossless gradient compression technique and solve a combinatorial optimization problem to automatically apply the technique for training time minimization. We evaluate TAG with various representative DNN models and device topologies, showing that it can achieve up to 4.56x training speed-up as compared to existing schemes. TAG can produce efficient deployment strategies for both unseen DNN models and unseen device topologies, without heavy fine-tuning. These Deep learning (DL) has powered a wide range of applications decisions jointly form an exponentially large strategy space. in various areas including computer vision [1], [2], natural Current practice often falls back to heuristics that consider language processing [3], [4], recommendation systems [5], one aspect of the strategy space at a time [17], [18], resulting etc. Recent deep neural network (DNN) models feature a in less efficient or even infeasible solutions. BERT [6] with more than Pioneering works on deploying DNN models onto heterogeneous 340M parameters) to achieve superior performance [3], [6]. However, their models do not generalize these models. This makes them homogeneous cluster, e.g., training Bert using 8 NVIDIA impractical for AI clouds, where new resource configurations V100 GPUs [7].

A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization. Bayesian optimization (BO) is a powerful tool that models and optimizes such expensive "black-box" functions. However, at the beginning of optimization, vanilla Bayesian optimization methods often suffer from slow convergence issue due to inaccurate modeling based on few trials. To address this issue, researchers in the BO community propose to incorporate the spirit of transfer learning to accelerate optimization process, which could borrow strength from the past tasks (source tasks) to accelerate the current optimization problem (target task). This survey paper first summarizes transfer learning methods for Bayesian optimization from four perspectives: initial points design, search space design, surrogate model, and acquisition function. Then it highlights its methodological aspects and technical details for each approach. Finally, it showcases a wide range of applications and proposes promising future directions.

Consider a weather forecaster who announces each day a probability p that there will be rain tomorrow. The forecaster is said to be calibrated if, for each forecast p that is used, the relative frequency of rainy days out of those days in which the forecast was p is equal to p in the long run. The surprising result of Foster and Vohra (1998) is that calibration can be guaranteed, no matter what the weather will be. There are various proofs of this result, and there is a large literature on calibration and its uses; see the survey of Olszewski (2015) and the more recent paper of Foster and Hart (2021). A simple proof of the existence of calibrated forecasts, based on the minimax theorem, was provided by the author in 1995.

In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. Recently, many flexible machine learning methods have been developed for instrumental variable estimation. However, these methods have at least one of the following limitations: (1) restricting the IV regression to be uniquely identified; (2) only obtaining estimation error rates in terms of pseudometrics (\emph{e.g.,} projected norm) rather than valid metrics (\emph{e.g.,} $L_2$ norm); or (3) imposing the so-called closedness condition that requires a certain conditional expectation operator to be sufficiently smooth. In this paper, we present the first method and analysis that can avoid all three limitations, while still permitting general function approximation. Specifically, we propose a new penalized minimax estimator that can converge to a fixed IV solution even when there are multiple solutions, and we derive a strong $L_2$ error rate for our estimator under lax conditions. Notably, this guarantee only needs a widely-used source condition and realizability assumptions, but not the so-called closedness condition. We argue that the source condition and the closedness condition are inherently conflicting, so relaxing the latter significantly improves upon the existing literature that requires both conditions. Our estimator can achieve this improvement because it builds on a novel formulation of the IV estimation problem as a constrained optimization problem.

The field of Automatic Machine Learning (AutoML) has recently attained impressive results, including the discovery of state-of-the-art machine learning solutions, such as neural image classifiers. This is often done by applying an evolutionary search method, which samples multiple candidate solutions from a large space and evaluates the quality of each candidate through a long training process. As a result, the search tends to be slow. In this paper, we show that large efficiency gains can be obtained by employing a fast unified functional hash, especially through the functional equivalence caching technique, which we also present. The central idea is to detect by hashing when the search method produces equivalent candidates, which occurs very frequently, and this way avoid their costly re-evaluation. Our hash is "functional" in that it identifies equivalent candidates even if they were represented or coded differently, and it is "unified" in that the same algorithm can hash arbitrary representations; e.g. compute graphs, imperative code, or lambda functions. As evidence, we show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery. Finally, we consider the effect of hash collisions, evaluation noise, and search distribution through empirical analysis. Altogether, we hope this paper may serve as a guide to hashing techniques in AutoML.

Inductive programming frequently relies on some form of search in order to identify candidate solutions. However, the size of the search space limits the use of inductive programming to the production of relatively small programs. If we could somehow correctly predict the subset of instructions required for a given problem then inductive programming would be more tractable. We will show that this can be achieved in a high percentage of cases. This paper presents a novel model of programming language instruction co-occurrence that was built to support search space partitioning in the Zoea distributed inductive programming system. This consists of a collection of intersecting instruction subsets derived from a large sample of open source code. Using the approach different parts of the search space can be explored in parallel. The number of subsets required does not grow linearly with the quantity of code used to produce them and a manageable number of subsets is sufficient to cover a high percentage of unseen code. This approach also significantly reduces the overall size of the search space - often by many orders of magnitude.