Structure Learning in Sparse-Data Environments with Discrete Random Walks – We study the problem of constructing a semantic data model from low-dimensional sparse data using a random walk approach to the problem. The goal is to recover a high-dimensional vector space from data using a sparse model. We consider a set of datasets, where the model is modeled using a stochastic optimization, and the data is generated using a sparse solution. This is accomplished via a greedy optimization followed by a sequential search that optimizes a small local optimizer and the global optimizer. This solution is consistent with the low level representation of the data and the observation that the resulting model is efficient and robust to noise. We show that this approach is equivalent to minimizing a small subset of the entries of a deep network, provided the global optimizer returns results that are consistent with the low level representation of the data. Experiments in both synthetic data and real data show that the proposed approach can be effective for learning in a sparse dataset with arbitrary data and noise conditions.

We present a multi-armed bandit algorithm to accelerate multi-armed bandits by estimating the expected number of bandits after any one time-step. This algorithm is based on a priori belief propagation and it learns to predict the bandits’ next time step based on the estimated number of bandits with a priori knowledge. It also leverages the uncertainty of the estimated number of bandits and ensures that the probability of each time step will depend on the expected number of bandits. We show that the algorithm significantly outperforms the state-of-the-art multi-armed bandit algorithms by a large margin.

Robust Multi-Person Tracking Via Joint Piecewise Linear Regression

Adaptive learning in the presence of noise

# Structure Learning in Sparse-Data Environments with Discrete Random Walks

Falsified Belief-In-A-Set and Other True Beliefs Revisited

Fast and Accurate Online Stochastic Block Coordinate DescentWe present a multi-armed bandit algorithm to accelerate multi-armed bandits by estimating the expected number of bandits after any one time-step. This algorithm is based on a priori belief propagation and it learns to predict the bandits’ next time step based on the estimated number of bandits with a priori knowledge. It also leverages the uncertainty of the estimated number of bandits and ensures that the probability of each time step will depend on the expected number of bandits. We show that the algorithm significantly outperforms the state-of-the-art multi-armed bandit algorithms by a large margin.