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Found 20 papers, 5 papers with code
A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary classification setting.
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions.
Off-policy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered set of items (e. g., a ranking) based on some context---a common scenario in web search, ads, and recommendation.
Provably efficient RL with Rich Observations via Latent State Decoding
We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states.
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
Large-scale machine learning systems often involve data distributed across a collection of users.
Hierarchical Imitation and Reinforcement Learning
We study how to effectively leverage expert feedback to learn sequential decision-making policies.
Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded.
Oracle-Efficient Online Learning and Auction Design
We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle.
Arbitrage-Free Combinatorial Market Making via Integer Programming
We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs.
Budget Constraints in Prediction Markets
We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker.
Convex Risk Minimization and Conditional Probability Estimation
This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem.
Contextual Dueling Bandits
The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space.
Doubly Robust Policy Evaluation and Optimization
As such, we expect the doubly robust approach to become common practice in policy evaluation and optimization.
Market Making with Decreasing Utility for Information
We study information elicitation in cost-function-based combinatorial prediction markets when the market maker's utility for information decreases over time.
Generative and Discriminative Learning with Unknown Labeling Bias
For the generative case, we derive an entropy-based weighting that maximizes expected log likelihood under the worst-case true class proportions.
Doubly robust off-policy evaluation with shrinkage
We propose a new framework for designing estimators for off-policy evaluation in contextual bandits.
Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error, but also exhibit an implicit bias.
Bayesian decision-making under misspecified priors with applications to meta-learning
We prove that the expected reward accrued by Thompson sampling (TS) with a misspecified prior differs by at most $\tilde{\mathcal{O}}(H^2 \epsilon)$ from TS with a well specified prior, where $\epsilon$ is the total-variation distance between priors and $H$ is the learning horizon.
Convex Analysis at Infinity: An Introduction to Astral Space
Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity.
Fairlearn: Assessing and Improving Fairness of AI Systems
Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.
