Investment Analysis and Portfolio Management 3 Questions and problems.90 References and further. Accepted Papers . Although cheap and fast to obtain, crowdsourced labels suffer from significant amounts of error, thereby degrading the performance of downstream machine learning tasks. With the goal of improving the quality of the labeled data, we seek to mitigate the many errors that occur due to silly mistakes or inadvertent errors by crowdsourcing workers. We propose a two- stage setting for crowdsourcing where the worker first answers the questions, and is then allowed to change her answers after looking at a (noisy) reference answer. We mathematically formulate this process and develop mechanisms to incentivize workers to act appropriately. Our mathematical guarantees show that our mechanism incentivizes the workers to answer honestly in both stages, and refrain from answering randomly in the first stage or simply copying in the second. Numerical experiments reveal a significant boost in performance that such “self- correction” can provide when using crowdsourcing to train machine learning algorithms. Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. Nihar Shah UC Berkeley, Sivaraman Balakrishnan CMU, Aditya Guntuboyina UC Berkeley, Martin Wainwright UC Berkeley. Paper . In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are required only to satisfy a natural form of stochastic transitivity. This class includes parametric models including the BTL and Thurstone models as special cases, but is considerably more general. We provide various examples of models in this broader stochastically transitive class for which classical parametric models provide poor fits. Despite this greater flexibility, we show that the matrix of probabilities can be estimated at the same rate as in standard parametric models. On the other hand, unlike in the BTL and Thurstone models, computing the minimax- optimal estimator in the stochastically transitive model is non- trivial, and we explore various computationally tractable alternatives. We show that a simple singular value thresholding algorithm is statistically consistent but does not achieve the minimax rate. We then propose and study algorithms that achieve the minimax rate over interesting sub- classes of the full stochastically transitive class. We complement our theoretical results with thorough numerical simulations. Numerical Reasoning Tests: Basic & Advanced Preparation, Guides, and Videos. Many employers require candidates to take a numerical reasoning test as part of the. Uprooting and Rerooting Graphical Models. Adrian Weller University of Cambridge. Paper . The new model is essentially equivalent to the original model, with the same partition function and allowing recovery of the original marginals or a MAP con. This meta- approach deepens our understanding, may be applied to any existing algorithm to yield improved methods in practice, generalizes earlier theoretical results, and reveals a remarkable interpretation of the triplet- consistent polytope. A Deep Learning Approach to Unsupervised Ensemble Learning. Uri Shaham Yale University, Xiuyuan Cheng , Omer Dror , Ariel Jaffe , Boaz Nadler , Joseph Chang , Yuval Kluger Paper . Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models. Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. Chelsea Finn UC Berkeley, Sergey Levine , Pieter Abbeel Berkeley. Paper . However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high- dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high- dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample- based approximation for Max. Ent IOC. We evaluate our method on a series of simulated tasks and real- world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency. Diversity- Promoting Bayesian Learning of Latent Variable Models. Pengtao Xie Carnegie Mellon University, Jun Zhu Tsinghua, Eric Xing CMUPaper . Various studies have been done to “diversify” a LVM, which aim to learn a diverse set of latent components in LVMs. Most existing studies fall into a frequentist- style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to “diversify” LVMs in the paradigm of Bayesian learning, which has advantages complementary to point estimation, such as alleviating overfitting via model averaging and quantifying uncertainty. Scott drew on iconography from Fritz Lang’s Metropolis (1927) and George Orwell’s classic 1984 novel to produce a stunning dystopic metaphor of what life would be.We propose two approaches that have complementary advantages. One is to define diversity- promoting mutual angular priors which assign larger density to components with larger mutual angles based on Bayesian network and von Mises- Fisher distribution and use these priors to affect the posterior via Bayes rule. We develop two efficient approximate posterior inference algorithms based on variational inference and Markov chain Monte Carlo sampling. The other approach is to impose diversity- promoting regularization directly over the post- data distribution of components.
![]() ![]() These two methods are applied to the Bayesian mixture of experts model to encourage the “experts” to be diverse and experimental results demonstrate the effectiveness and efficiency of our methods. Additive Approximations in High Dimensional Nonparametric Regression via the SALSAKirthevasan Kandasamy Carnegie Mellon University, Yaoliang Yu Paper . ![]() Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics. Data- driven Rank Breaking for Efficient Rank Aggregation. Ashish Khetan UIUC, Sewoong Oh UIUCPaper . To reduce the computational complexity of learning the global ranking, a common practice is to use rank- breaking. Individuals’ preferences are broken into pairwise comparisons and then applied to efficient algorithms tailored for independent pairwise comparisons. However, due to the ignored dependencies, naive rank- breaking approaches can result in inconsistent estimates. The key idea to produce unbiased and accurate estimates is to treat the paired comparisons outcomes unequally, depending on the topology of the collected data. In this paper, we provide the optimal rank- breaking estimator, which not only achieves consistency but also achieves the best error bound. This allows us to characterize the fundamental tradeoff between accuracy and complexity in some canonical scenarios. Further, we identify how the accuracy depends on the spectral gap of a corresponding comparison graph. Dropout distillation. Samuel Rota Bul. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be averaged at test time to deliver the final prediction. A typical workaround for this intractable averaging operation consists in scaling the layers undergoing dropout randomization. This simple rule called . In this work we introduce a novel approach, coined . We are thus able to construct models that are as efficient as standard dropout, or even more efficient, while being more accurate. Experiments on standard benchmark datasets demonstrate the validity of our method, yielding consistent improvements over conventional dropout. Metadata- conscious anonymous messaging. Giulia Fanti UIUC, Peter Kairouz UIUC, Sewoong Oh UIUC, Kannan Ramchandran UC Berkeley, Pramod Viswanath UIUCPaper . The spread of messages on these platforms can be modeled by a diffusion process over a graph. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to propagate anonymous messages over a graph to make it difficult for adversaries to infer the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion introduced in (Fanti et al., 2. We prove that when the adversary has access to metadata at a fraction of corrupted graph nodes, adaptive diffusion achieves asymptotically optimal source- hiding and significantly outperforms standard diffusion.
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