Safe Exploration for Optimization with Gaussian Processes

We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation

Systematic Testing for Detecting Concurrency Errors in Erlang Programs

We present the techniques used in Concuerror, a systematic testing tool able to find and reproduce a wide class of concurrency errors in Erlang programs. We describe how we take advantage of the characteristics of Erlang's actor model of concurrency to selectively instrument the program under test and how we subsequently employ a stateless search strategy to systematically explore the state space of process interleaving sequences triggered by unit tests. To ameliorate the problem of combinatorial explosion, we propose a novel technique for avoiding process blocks and describe how we can effectively combine it with preemption bounding, a heuristic algorithm for reducing the number of explored interleaving sequences. We also briefly discuss issues related to soundness, completeness and effectiveness of techniques used by Concuerror

Test-Driven Development of Concurrent Programs using Concuerror

This paper advocates the test-driven development of concurrent Erlang programs in order to detect early and eliminate the vast majority of concurrency-related errors that may occur in their execution. To facilitate this task we have developed a tool, called Concuerror, that exhaustively explores process interleaving (possibly up to some preemption bound) and presents detailed interleaving information of any errors that occur. We describe in detail the use of Concuerror on a non-trivial concurrent Erlang program that we develop step by step in a test-driven fashion

Safe Exploration for Optimization with Gaussian Processes

We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation

ON THE EXISTENCE OF UNIVERSAL LOTTERY TICKETS

The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Recent work has experimentally observed that some of these tickets can be practically reused across a variety of tasks, hinting at some form of universality. We formalize this concept and theoretically prove that not only do such universal tickets exist but they also do not require further training. Our proofs introduce a couple of technical innovations related to pruning for strong lottery tickets, including extensions of subset sum results and a strategy to leverage higher amounts of depth. Our explicit sparse constructions of universal function families might be of independent interest, as they highlight representational benefits induced by univariate convolutional architectures

Along the Spectrum of Women\u27s Rights Advocacy: A Cross-Cultural Comparison of Sexual Harassment Law in the United States and India

This Comment compares the development of sexual harassment law in the United States and India. It strives to contribute to this global feminist debate by highlighting the successes and failures of each country\u27s respective anti-harassment protections. It also compares the United States\u27 and India\u27s legal approaches to the problem of workplace sexual harassment. The Comment also discusses the successes and failures of the U.S. and Indian protections in a manner that attempts to minimize the problems present in cross-cultural studies

Sampling from Probabilistic Submodular Models

Practical problems of discrete nature are very common in machine learning; application domains include computer vision (e.g., image segmentation), sequential decision making (e.g., active learning), social network analysis (e.g., influence maximization), and natural language processing (e.g., document summarization). Submodular set functions have found wide applicability in such problems for their ability to capture notions of coverage, diversity, or exclusivity; analogously, supermodular set functions have been used to capture notions of regularity, smoothness, or co-occurrence. While the topic of submodular optimization has received much attention, these functions can also be used to define expressive discrete probabilistic models, called probabilistic submodular models. Going beyond optimization, these models allow us to quantify predictive uncertainty, and suggest a maximum likelihood approach for learning such functions from noisy data. Prominent examples of probabilistic submodular models include Ising and Potts models, as well as determinantal point processes, but the general class is much richer and little studied. It is well known, though, that performing probabilistic inference in such models is computationally intractable in general. In this thesis, we investigate the use of Markov chain Monte Carlo sampling as a means of performing approximate inference in probabilistic submodular models. We start with analyzing the Gibbs sampler, and establish theoretical conditions that guarantee efficient convergence of this sampler in probabilistic submodular models. We next propose a novel sampling procedure that makes use of discrete semigradients to perform efficient global moves, so as to avoid so-called state-space bottlenecks, and thus lead to improved convergence behavior. Finally, we employ the aforementioned sampling methods to approximate the likelihood gradients, and learn such models from data. We apply our learning procedure to the problem of modeling interactions between genetic mutations in cancer patients, and demonstrate considerable improvement over the state of the art in many of our experimental results on both synthetic and real cancer data