Search Results for author:
Found 23 papers, 6 papers with code
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains.
Decoding Attention from Gaze: A Benchmark Dataset and End-to-End Models
Eye-tracking has potential to provide rich behavioral data about human cognition in ecologically valid environments.
Probable Domain Generalization via Quantile Risk Minimization
By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.
Indirect Active Learning
Traditional models of active learning assume a learner can directly manipulate or query a covariate $X$ in order to study its relationship with a response $Y$.
Optimal Binary Classification Beyond Accuracy
The vast majority of statistical theory on binary classification characterizes performance in terms of accuracy.
Continuum-Armed Bandits: A Function Space Perspective
In both noiseless and noisy conditions, we derive minimax rates under simple and cumulative regrets.
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19.
Robust Density Estimation under Besov IPM Losses
We study minimax convergence rates of nonparametric density estimation in the Huber contamination model, in which a proportion of the data comes from an unknown outlier distribution.
Multiclass Classification via Class-Weighted Nearest Neighbors
We study statistical properties of the k-nearest neighbors algorithm for multiclass classification, with a focus on settings where the number of classes may be large and/or classes may be highly imbalanced.
Differentiable Architecture Compression
In many learning situations, resources at inference time are significantly more constrained than resources at training time.
DARC: Differentiable ARchitecture Compression
In many learning situations, resources at inference time are significantly more constrained than resources at training time.
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses
Thus, we show how our results imply bounds on the statistical error of a GAN, showing, for example, that GANs can strictly outperform the best linear estimator.
Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation under a large class of loss functions called "adversarial losses", which, besides classical $\mathcal{L}^p$ losses, includes maximum mean discrepancy (MMD), Wasserstein distance, and total variation distance.
Minimax Estimation of Quadratic Fourier Functionals
We study estimation of (semi-)inner products between two nonparametric probability distributions, given IID samples from each distribution.
Minimax Distribution Estimation in Wasserstein Distance
The Wasserstein metric is an important measure of distance between probability distributions, with applications in machine learning, statistics, probability theory, and data analysis.
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning
Sparse dictionary learning (SDL) has become a popular method for adaptively identifying parsimonious representations of a dataset, a fundamental problem in machine learning and signal processing.
Nonparanormal Information Estimation
To address this, we propose estimators for mutual information when $p$ is assumed to be a nonparanormal (a. k. a., Gaussian copula) model, a semiparametric compromise between Gaussian and nonparametric extremes.
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences.
Efficient Nonparametric Smoothness Estimation
Sobolev quantities (norms, inner products, and distances) of probability density functions are important in the theory of nonparametric statistics, but have rarely been used in practice, partly due to a lack of practical estimators.
Distributed Gradient Descent in Bacterial Food Search
Communication and coordination play a major role in the ability of bacterial cells to adapt to ever changing environments and conditions.
Quantitative Methods
Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation
Estimating entropy and mutual information consistently is important for many machine learning applications.
Exponential Concentration of a Density Functional Estimator
We analyze a plug-in estimator for a large class of integral functionals of one or more continuous probability densities.
Generalized Exponential Concentration Inequality for Rényi Divergence Estimation
Estimating divergences in a consistent way is of great importance in many machine learning tasks.
