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02/08/2023
Fairness in Matching under Uncertainty
The prevalence and importance of algorithmic two-sided marketplaces has ...
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11/20/2022
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks
Range aggregate queries (RAQs) are an integral part of many real-world a...
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03/29/2022
Efficient Convex Optimization Requires Superlinear Memory
We show that any memory-constrained, first-order algorithm which minimiz...
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02/28/2022
KL Divergence Estimation with Multi-group Attribution
Estimating the Kullback-Leibler (KL) divergence between two distribution...
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01/12/2022
On the Statistical Complexity of Sample Amplification
Given n i.i.d. samples drawn from an unknown distribution P, when is it ...
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09/11/2021
Omnipredictors
Loss minimization is a dominant paradigm in machine learning, where a pr...
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03/29/2021
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Can deep learning solve multiple tasks simultaneously, even when they ar...
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03/10/2021
Multicalibrated Partitions for Importance Weights
The ratio between the probability that two distributions R and P give to...
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12/08/2019
PIDForest: Anomaly Detection via Partial Identification
We consider the problem of detecting anomalies in a large dataset. We pr...
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04/26/2019
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Given data drawn from an unknown distribution, D, to what extent is it p...
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04/18/2019
Memory-Sample Tradeoffs for Linear Regression with Small Error
We consider the problem of performing linear regression over a stream of...
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11/15/2018
A Spectral View of Adversarially Robust Features
Given the apparent difficulty of learning models that are robust to adve...
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10/31/2018
Recovery Guarantees for Quadratic Tensors with Limited Observations
We consider the tensor completion problem of predicting the missing entr...
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04/09/2018
Faster Anomaly Detection via Matrix Sketching
We present efficient streaming algorithms to compute two commonly used a...
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03/06/2018
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Interactive analytics increasingly involves querying for quantiles over ...
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11/07/2017
Learning Overcomplete HMMs
We study the problem of learning overcomplete HMMs---those that have man...
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11/07/2017
Finding Heavily-Weighted Features in Data Streams
We introduce a new sub-linear space data structure---the Weight-Median S...
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06/25/2017
There and Back Again: A General Approach to Learning Sparse Models
We propose a simple and efficient approach to learning sparse models. Ou...
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03/06/2017
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
The popular Alternating Least Squares (ALS) algorithm for tensor decompo...
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12/08/2016