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Found 13 papers, 0 papers with code
Information-Theoretic Bayes Risk Lower Bounds for Realizable Models
For models that are (roughly) lower Lipschitz in their parameters, we bound the rate distortion function from below, whereas for VC classes, the mutual information is bounded above by $d_\mathrm{vc}\log(n)$.
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete.
Scaling-up Distributed Processing of Data Streams for Machine Learning
This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates.
Learning Furniture Compatibility with Graph Neural Networks
We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score.
An Effective Label Noise Model for DNN Text Classification
In this paper, we propose an approach to training deep networks that is robust to label noise.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.
Information Bottleneck Methods for Distributed Learning
We consider this problem for unsupervised learning for batch and sequential data.
Tensor Matched Kronecker-Structured Subspace Detection for Missing Information
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace.
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.
Learning Deep Networks from Noisy Labels with Dropout Regularization
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance.
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms
We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal.
Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks
Motivated by machine learning applications in networks of sensors, internet-of-things (IoT) devices, and autonomous agents, we propose techniques for distributed stochastic convex learning from high-rate data streams.
Rate-Distortion Bounds on Bayes Risk in Supervised Learning
We provide lower and upper bounds on the rate-distortion function, using $L_p$ loss as the distortion measure, of a maximum a priori classifier in terms of the differential entropy of the posterior distribution and a quantity called the interpolation dimension, which characterizes the complexity of the parametric distribution family.
