Search Results for author:
Found 14 papers, 4 papers with code
From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs
This yields, inter alia, multicalibrated predictions of edge formation with respect to pairs of demographic groups, and the ability to simultaneously optimize loss as measured by a variety of social welfare functions.
Large Scale Transfer Learning for Tabular Data via Language Modeling
In this work, we seek to narrow this gap and present TabuLa-8B, a language model for tabular prediction.
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix.
Difficult Lessons on Social Prediction from Wisconsin Public Schools
These systems assist in targeting interventions to individual students by predicting which students are at risk of dropping out.
Making Decisions under Outcome Performativity
This performative prediction setting raises new challenges for learning "optimal" decision rules.
A Complete Characterization of Linear Estimators for Offline Policy Evaluation
Offline policy evaluation is a fundamental statistical problem in reinforcement learning that involves estimating the value function of some decision-making policy given data collected by a potentially different policy.
Globally Convergent Policy Search over Dynamic Filters for Output Estimation
In this paper, we provide a new perspective on this challenging problem based on the notion of $\textit{informativity}$, which intuitively requires that all components of a filter's internal state are representative of the true state of the underlying dynamical system.
Stabilizing Dynamical Systems via Policy Gradient Methods
Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering.
Towards a Dimension-Free Understanding of Adaptive Linear Control
We study the problem of adaptive control of the linear quadratic regulator for systems in very high, or even infinite dimension.
Outside the Echo Chamber: Optimizing the Performative Risk
In performative prediction, predictions guide decision-making and hence can influence the distribution of future data.
Stochastic Optimization for Performative Prediction
In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions.
Performative Prediction
When predictions support decisions they may influence the outcome they aim to predict.
Robust Attacks against Multiple Classifiers
We address the challenge of designing optimal adversarial noise algorithms for settings where a learner has access to multiple classifiers.
Optimal Attacks against Multiple Classifiers
The main technical challenge we consider is the design of best response oracles that can be implemented in a Multiplicative Weight Updates framework to find equilibrium strategies in the zero-sum game.
