Search Results for author:
Found 30 papers, 3 papers with code
Online Learning with Dependent Stochastic Feedback Graphs
A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner.
Differentially Private Domain Adaptation with Theoretical Guarantees
This is the modern problem of supervised domain adaptation from a public source to a private target domain.
Best-Effort Adaptation
We show how these bounds can guide the design of learning algorithms that we discuss in detail.
Boosting with Multiple Sources
We study the problem of learning accurate ensemble predictors, in particular boosting, in the presence of multiple source domains.
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment
Further, with seven years passing since the experiment we find that for \emph{accepted} papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count.
Agnostic Learning with Multiple Objectives
We further implement the algorithm in a popular symbolic gradient computation framework and empirically demonstrate on a number of datasets the benefits of $\almo$ framework versus learning with a fixed mixture weights distribution.
A Discriminative Technique for Multiple-Source Adaptation
We present a new discriminative technique for the multiple-source adaptation, MSA, problem.
Beyond Individual and Group Fairness
In the adversarial setting, we design efficient algorithms with competitive ratio guarantees.
Relative Deviation Margin Bounds
We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor.
Adaptive Region-Based Active Learning
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels.
Regularized Gradient Boosting
We fill this gap by deriving data-dependent learning guarantees for \GB\ used with \emph{regularization}, expressed in terms of the Rademacher complexities of the constrained families of base predictors.
Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions.
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.
Online Non-Additive Path Learning under Full and Partial Information
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction.
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 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.
Boosting with Abstention
We present a new boosting algorithm for the key scenario of binary classification with abstention where the algorithm can abstain from predicting the label of a point, at the price of a fixed cost.
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.
Voted Kernel Regularization
This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from strong learning guarantees.
Adaptation Algorithm and Theory Based on Generalized Discrepancy
We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm previously shown to outperform a number of algorithms for this task.
Learning Kernels Using Local Rademacher Complexity
We use the notion of local Rademacher complexity to design new algorithms for learning kernels.
Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions
We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications.
Accuracy at the Top
We introduce a new notion of classification accuracy based on the top $\tau$-quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines.
Algorithms for Learning Kernels Based on Centered Alignment
Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment.
Learning Bounds for Importance Weighting
This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results.
Polynomial Semantic Indexing
We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score.
Learning Non-Linear Combinations of Kernels
This paper studies the general problem of learning kernels based on a polynomial combination of base kernels.
