Search Results for author:
Found 45 papers, 19 papers with code
Bias in Evaluation Processes: An Optimization-Based Model
We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution.
Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations
We present and analyze a complex variant of the Gaussian mechanism and show that the Frobenius norm of the difference between the matrix output by this mechanism and the best rank-$k$ approximation to $M$ is bounded by roughly $\tilde{O}(\sqrt{kd})$, whenever there is an appropriately large gap between the $k$'th and the $k+1$'th eigenvalues of $M$.
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset.
Maximizing Submodular Functions for Recommendation in the Presence of Biases
In empirical evaluation, with both synthetic and real-world data, we observe that this algorithm improves the utility of the output subset for this family of submodular functions over baselines.
Fair Ranking with Noisy Protected Attributes
The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information retrieval, and machine learning literature.
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
These equations allow us to bound the utility as the square-root of a sum-of-squares of perturbations to the eigenvectors, as opposed to a sum of perturbation bounds obtained via Davis-Kahan-type theorems.
Private Matrix Approximation and Geometry of Unitary Orbits
Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$.
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk
Given a Lipschitz or smooth convex function $\, f:K \to \mathbb{R}$ for a bounded polytope $K \subseteq \mathbb{R}^d$ defined by $m$ inequalities, we consider the problem of sampling from the log-concave distribution $\pi(\theta) \propto e^{-f(\theta)}$ constrained to $K$.
Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints
Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection.
Fairness for AUC via Feature Augmentation
We evaluate fairAUC on synthetic and real-world datasets and find that it significantly improves AUC for the disadvantaged group relative to benchmarks maximizing overall AUC and minimizing bias between groups.
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees
For a $d$-dimensional log-concave distribution $\pi(\theta) \propto e^{-f(\theta)}$ constrained to a convex body $K$, the problem of outputting samples from a distribution $\nu$ which is $\varepsilon$-close in infinity-distance $\sup_{\theta \in K} |\log \frac{\nu(\theta)}{\pi(\theta)}|$ to $\pi$ arises in differentially private optimization.
Coresets for Time Series Clustering
In particular, we consider the setting where the time series data on $N$ entities is generated from a Gaussian mixture model with autocorrelations over $k$ clusters in $\mathbb{R}^d$.
Optimization and Sampling Under Continuous Symmetry: Examples and Lie Theory
In the last few years, the notion of symmetry has provided a powerful and essential lens to view several optimization or sampling problems that arise in areas such as theoretical computer science, statistics, machine learning, quantum inference, and privacy.
An Introduction to Hamiltonian Monte Carlo Method for Sampling
The goal of this article is to introduce the Hamiltonian Monte Carlo (HMC) method -- a Hamiltonian dynamics-inspired algorithm for sampling from a Gibbs density $\pi(x) \propto e^{-f(x)}$.
Fair Classification with Adversarial Perturbations
Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness.
Coresets for Regressions with Panel Data
We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for.
The Effect of the Rooney Rule on Implicit Bias in the Long Term
Our main result is that, when the panel is constrained by the Rooney Rule, their implicit bias roughly reduces at a rate that is the inverse of the size of the shortlist--independent of the number of candidates, whereas without the Rooney Rule, the rate is inversely proportional to the number of candidates.
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
The equilibrium point found by our algorithm depends on the proposal distribution, and when applying our algorithm to train GANs we choose the proposal distribution to be a distribution of stochastic gradients.
Greedy Adversarial Equilibrium: An Efficient Alternative to Nonconvex-Nonconcave Min-Max Optimization
We propose an optimization model, the $\varepsilon$-greedy adversarial equilibrium, and show that it can serve as a computationally tractable alternative to the min-max optimization model.
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes.
Interventions for Ranking in the Presence of Implicit Bias
Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group (e. g., defined by gender or race).
Coresets for Clustering with Fairness Constraints
Our approach is based on novel constructions of coresets: for the $k$-median objective, we construct an $\varepsilon$-coreset of size $O(\Gamma k^2 \varepsilon^{-d})$ where $\Gamma$ is the number of distinct collections of groups that a point may belong to, and for the $k$-means objective, we show how to construct an $\varepsilon$-coreset of size $O(\Gamma k^3\varepsilon^{-d-1})$.
Data preprocessing to mitigate bias: A maximum entropy based approach
Unlike prior work, it can efficiently learn distributions over large domains, controllably adjust the representation rates of protected groups and achieve target fairness metrics such as statistical parity, yet remains close to the empirical distribution induced by the given dataset.
Faster polytope rounding, sampling, and volume computation via a sublinear "Ball Walk"
We achieve this improvement by a novel method of computing polytope membership, where one avoids checking inequalities estimated to have a very low probability of being violated.
Nonconvex sampling with the Metropolis-adjusted Langevin algorithm
The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applications.
Online Sampling from Log-Concave Distributions
Given a sequence of convex functions $f_0, f_1, \ldots, f_T$, we study the problem of sampling from the Gibbs distribution $\pi_t \propto e^{-\sum_{k=0}^tf_k}$ for each epoch $t$ in an online manner.
Stable and Fair Classification
Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework.
Toward Controlling Discrimination in Online Ad Auctions
To prevent this, we propose a constrained ad auction framework that maximizes the platform's revenue conditioned on ensuring that the audience seeing an advertiser's ad is distributed appropriately across sensitive types such as gender or race.
Balanced News Using Constrained Bandit-based Personalization
We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of de-polarizing content and allowing users to escape their filter bubble.
Geodesic Convex Optimization: Differentiation on Manifolds, Geodesics, and Convexity
We first present a variety of notions from differential and Riemannian geometry such as differentiation on manifolds, geodesics, and then introduce geodesic convexity.
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
The main contribution of this paper is a new meta-algorithm for classification that takes as input a large class of fairness constraints, with respect to multiple non-disjoint sensitive attributes, and which comes with provable guarantees.
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning.
An Algorithmic Framework to Control Bias in Bandit-based Personalization
Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user.
Fair and Diverse DPP-based Data Summarization
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization.
Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing
In this paper we study the more general case when the noise has magnitude $\alpha F(x) + \beta$ for some $\alpha, \beta > 0$, and present a polynomial time algorithm that finds an approximate minimizer of $F$ for this noise model.
Maximum Entropy Distributions: Bit Complexity and Stability
Our main result shows a ${\rm poly}(m, \log 1/\varepsilon)$ bound on the bit complexity of $\varepsilon$-optimal dual solutions to the maximum entropy convex program -- for very general support sets and with no restriction on the marginal vector.
Multiwinner Voting with Fairness Constraints
Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters.
Belief Propagation, Bethe Approximation and Polynomials
While it is known that the stationary points of the Bethe approximation coincide with the fixed points of belief propagation, in general, the relation between the Bethe approximation and the partition function is not well understood.
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration
The volume of a set of vectors is used as a measure of their diversity, and partition or matroid constraints over $[m]$ are imposed in order to ensure resource or fairness constraints.
Fair Personalization
Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user.
A Distributed Learning Dynamics in Social Groups
Finally, we observe that our infinite population dynamics is a stochastic variant of the classic multiplicative weights update (MWU) method.
How to be Fair and Diverse?
However, in doing so, a question that seems to be overlooked is whether it is possible to produce fair subsamples that are also adequately representative of the feature space of the data set - an important and classic requirement in machine learning.
On the Complexity of Constrained Determinantal Point Processes
Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.
IRLS and Slime Mold: Equivalence and Convergence
In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology.
