Many machine learning tasks require fitting probabilistic models over structured objects, such as pixel grids, matchings, and graph edges. Maximum likelihood estimation (MLE) for such domains is challenging due to the intractability of computing partition functions. One can resort to approximate marginal inference in conjunction with gradient descent, but such algorithms require careful tuning. Alternatively, in frameworks such as the structured support vector machine (SVM-Struct), discriminative functions are learned by iteratively applying efficient maximum a posteriori (MAP) decoders. We introduce MLE-Struct, a method for learning discrete exponential family models using the Bethe approximation to the partition function. Remarkably, this problem can also be reduced to iterative (MAP) decoding. This connection emerges by combining the Bethe approximation with the Frank-Wolfe (FW) algorithm on a convex dual objective, which circumvents the intractable partition function. Our method can learn both generative and conditional models and is substantially faster and easier to implement than existing MLE approaches while still relying on the same black-box interface to MAP decoding as SVM-Struct. We perform competitively on problems in denoising, segmentation, matching, and new datasets of roommate assignments and news and financial time series.

The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy. This problem leads to a generalization of $k$-anonymity to the $b$-matching setting. Novel algorithms and theory are provided to implement this type of anonymity. The relaxation achieves better utility, admits theoretical privacy guarantees that are as strong, and, most importantly, accommodates a variable level of anonymity for each individual. Empirical results confirm improved utility on benchmark and social data-sets.

As software scalability lags behind hardware parallelism, understanding scaling behavior is more important than ever. This paper demonstrates how to use Parallel Block Vector (PBV) profiles to measure the scaling properties of multithreaded programs from a new perspective: the basic block's view. Through this lens, we guide users through quick and simple methods to produce high-resolution application scaling analyses. This method requires no manual program modification, new hardware, or lengthy simulations, and captures the impact of architecture, operating systems, threading models, and inputs. We apply these techniques to a set of parallel benchmarks, and, as an example, demonstrate that when it comes to scaling, functions in an application do not behave monolithically.

Efficient execution of well-parallelized applications is central to performance in the multicore era. Program analysis tools support the hardware and software sides of this effort by exposing relevant features of multithreaded applications. This paper describes parallel block vectors, which uncover previously unseen characteristics of parallel programs. Parallel block vectors provide block execution profiles per concurrency phase (e.g., the block execution profile of all serial regions of a program). This information provides a direct and fine-grained mapping between an application's runtime parallel phases and the static code that makes up those phases. This paper also demonstrates how to collect parallel block vectors with minimal application perturbation using Harmony. Harmony is an instrumentation pass for the LLVM compiler that introduces just 16-21% overhead on average across eight Parsec benchmarks. We apply parallel block vectors to uncover several novel insights about parallel applications with direct consequences for architectural design. First, that the serial and parallel phases of execution used in Amdahl's Law are often composed of many of the same basic blocks. Second, that program features, such as instruction mix, vary based on the degree of parallelism, with serial phases in particular displaying different instruction mixes from the program as a whole. Third, that dynamic execution frequencies do not necessarily correlate with a block's parallelism.