XClusters: Explainability-first Clustering

We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is a difference of monotonic functions. We then propose an efficient branch-and-bound algorithm for finding the best parameters that lead to a balance of cluster distortion and decision tree explainability. Our experiments show that our method can improve the explainability of any clustering that fits in our framework.Comment: 11 page

MixRL: Data Mixing Augmentation for Regression using Reinforcement Learning

Data augmentation is becoming essential for improving regression accuracy in critical applications including manufacturing and finance. Existing techniques for data augmentation largely focus on classification tasks and do not readily apply to regression tasks. In particular, the recent Mixup techniques for classification rely on the key assumption that linearity holds among training examples, which is reasonable if the label space is discrete, but has limitations when the label space is continuous as in regression. We show that mixing examples that either have a large data or label distance may have an increasingly-negative effect on model performance. Hence, we use the stricter assumption that linearity only holds within certain data or label distances for regression where the degree may vary by each example. We then propose MixRL, a data augmentation meta learning framework for regression that learns for each example how many nearest neighbors it should be mixed with for the best model performance using a small validation set. MixRL achieves these objectives using Monte Carlo policy gradient reinforcement learning. Our experiments conducted both on synthetic and real datasets show that MixRL significantly outperforms state-of-the-art data augmentation baselines. MixRL can also be integrated with other classification Mixup techniques for better results.Comment: 15 pages, 9 figures, 7 table

Compensation of Outside Directors: An Empirical Analysis of Economic Determinants

Little is known about the economic environments and determinants of the compensation arrangements for outside board members. As delegated monitors of corporate management, board members act as shareholders' agents. Thus, a potential for misaligned interests exists, requiring in turn incentive arrangements that are incentive-compatible and individually rational. We study the economic determinants of both the levels and mix of compensation for outside board members. We also examine the effects of the existence of a director pension plan on the relation between director compensation and the hypothesized determinants. In sum, and contrary to criticism that the board of directors is often a passive, ineffective entity that dislikes conflict with incumbent management, we find that board compensation is structured to mitigate agency problems inherent in firms whose management control is separated from ownership

Many-body interactions in quasi-freestanding graphene

The Landau-Fermi liquid picture for quasiparticles assumes that charge carriers are dressed by many-body interactions, forming one of the fundamental theories of solids. Whether this picture still holds for a semimetal like graphene at the neutrality point, i.e., when the chemical potential coincides with the Dirac point energy, is one of the long-standing puzzles in this field. Here we present such a study in quasi-freestanding graphene by using high-resolution angle-resolved photoemission spectroscopy. We see the electron-electron and electron-phonon interactions go through substantial changes when the semimetallic regime is approached, including renormalizations due to strong electron-electron interactions with similarities to marginal Fermi liquid behavior. These findings set a new benchmark in our understanding of many-body physics in graphene and a variety of novel materials with Dirac fermions.Comment: PNAS 2011 ; published ahead of print June 27, 201

The Edmonds-Giles Conjecture and its Relaxations

Given a directed graph, a directed cut is a cut with all arcs oriented in the same direction, and a directed join is a set of arcs which intersects every directed cut at least once. Edmonds and Giles conjectured for all weighted directed graphs, the minimum weight of a directed cut is equal to the maximum size of a packing of directed joins. Unfortunately, the conjecture is false; a counterexample was first given by Schrijver. However its ”dual” statement, that the minimum weight of a dijoin is equal to the maximum number of dicuts in a packing, was shown to be true by Luchessi and Younger. Various relaxations of the conjecture have been considered; Woodall’s conjecture remains open, which asks the same question for unweighted directed graphs, and Edmond- Giles conjecture was shown to be true in the special case of source-sink connected directed graphs. Following these inquries, this thesis explores different relaxations of the Edmond- Giles conjecture

Multipath TCP: Analysis, Design, and Implementation

Multipath TCP (MP-TCP) has the potential to greatly improve application performance by using multiple paths transparently. We propose a fluid model for a large class of MP-TCP algorithms and identify design criteria that guarantee the existence, uniqueness, and stability of system equilibrium. We clarify how algorithm parameters impact TCP-friendliness, responsiveness, and window oscillation and demonstrate an inevitable tradeoff among these properties. We discuss the implications of these properties on the behavior of existing algorithms and motivate our algorithm Balia (balanced linked adaptation), which generalizes existing algorithms and strikes a good balance among TCP-friendliness, responsiveness, and window oscillation. We have implemented Balia in the Linux kernel. We use our prototype to compare the new algorithm to existing MP-TCP algorithms