Search Results for author:
Found 13 papers, 2 papers with code
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention.
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
In this paper, we design efficient gradient computation algorithms for two broad families of structured prediction loss functions: rational and tropical losses.
Semi-automated Annotation of Signal Events in Clinical EEG Data
In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus.
An Analysis of Two Common Reference Points for EEGs
In this study, we explore the impact this variability has on machine learning performance.
Online Learning with Transductive Regret
A by-product of our study is an algorithm for swap regret, which, under mild assumptions, is more efficient than existing ones, and a substantially more efficient algorithm for time selection swap regret.
Discrepancy-Based Algorithms for Non-Stationary Rested Bandits
We show that the notion of discrepancy can be used to design very general algorithms and a unified framework for the analysis of multi-armed rested bandit problems with non-stationary rewards.
Online Learning with Automata-based Expert Sequences
We consider a general framework of online learning with expert advice where regret is defined with respect to sequences of experts accepted by a weighted automaton.
Online Learning with Abstention
In the stochastic setting, we first point out a bias problem that limits the straightforward extension of algorithms such as UCB-N to time-varying feedback graphs, as needed in this context.
Optimistic Bandit Convex Optimization
We introduce the general and powerful scheme of predicting information re-use in optimization algorithms.
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
We present new algorithms for adaptively learning artificial neural networks.
Structured Prediction Theory Based on Factor Graph Complexity
We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition.
Accelerating Optimization via Adaptive Prediction
We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problem-dependent randomization.
Conditional Swap Regret and Conditional Correlated Equilibrium
We introduce a natural extension of the notion of swap regret, conditional swap regret, that allows for action modifications conditioned on the player’s action history.
