Search Results for author:
Found 39 papers, 15 papers with code
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain
This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet.
Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision.
Bellman Optimal Stepsize Straightening of Flow-Matching Models
This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint.
Ranked #1 on
Image Generation
on LSUN Churches 256 x 256
(clean-FID metric)
Coverage-Validity-Aware Algorithmic Recourse
Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models.
Efficient Failure Pattern Identification of Predictive Algorithms
The recommendation algorithm is conceptualized as a stochastic sampler that, in each round, queries the annotators a subset of samples for their true labels and obtains the feedback information on whether the samples are misclassified.
Distributionally Robust Recourse Action
To redress this shortcoming, we propose the Distributionally Robust Recourse Action (DiRRAc) framework, which generates a recourse action that has a high probability of being valid under a mixture of model shifts.
Feasible Recourse Plan via Diverse Interpolation
The experimental results show that our method produces a set of recourses that are close to the data manifold while delivering a better cost-diversity trade-off than existing approaches.
Dynamic Flows on Curved Space Generated by Labeled Data
The scarcity of labeled data is a long-standing challenge for many machine learning tasks.
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principled way to regularize learning models.
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks
We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques.
Robust Bayesian Recourse
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision.
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
In this work, we consider probability measures supported on a graph metric space and propose a novel Sobolev transport metric.
Distributionally Robust Fair Principal Components via Geodesic Descents
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines.
Counterfactual Plans under Distributional Ambiguity
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains.
Mean-Covariance Robust Risk Measurement
We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization.
Adversarial Regression with Doubly Non-negative Weighting Matrices
In this paper, we propose a novel and coherent scheme for kernel-reweighted regression by reparametrizing the sample weights using a doubly non-negative matrix.
Gradient flows on the feature-Gaussian manifold
The scarcity of labeled data is a long-standing challenge for cross-domain machine learning tasks.
Statistical Analysis of Wasserstein Distributionally Robust Estimators
We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning problems.
S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging
The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents.
Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions.
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions.
Principal Component Hierarchy for Sparse Quadratic Programs
We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix.
Robustifying Conditional Portfolio Decisions via Optimal Transport
Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.
Wasserstein Robust Classification with Fairness Constraints
We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion.
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task.
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection
In this paper, we propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection.
A Statistical Test for Probabilistic Fairness
Leveraging the geometry of the feature space, the test statistic quantifies the distance of the empirical distribution supported on the test samples to the manifold of distributions that render a pre-trained classifier fair.
Distributionally Robust Local Non-parametric Conditional Estimation
Conditional estimation given specific covariate values (i. e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences.
Distributionally Robust Parametric Maximum Likelihood Estimation
We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions.
Machine Learning's Dropout Training is Distributionally Robust Optimal
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model.
A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity.
Robust Bayesian Classification Using an Optimistic Score Ratio
The optimistic score searches for the distribution that is most plausible to explain the observed outcomes in the testing sample among all distributions belonging to the contextual ambiguity set which is prescribed using a limited structural constraint on the mean vector and the covariance matrix of the underlying contextual distribution.
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation -- and a fictitious adversary choosing a prior -- that is, a pair of signal and noise distributions ranging over independent Wasserstein balls -- with the goal to minimize and maximize the expected squared estimation error, respectively.
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
The likelihood function is a fundamental component in Bayesian statistics.
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions.
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples.
Wasserstein Distributionally Robust Kalman Filtering
Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program.
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a $p$-dimensional Gaussian random vector from $n$ independent samples.
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems for compressive sensing.
