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Found 13 papers, 0 papers with code
Selling to a No-Regret Buyer
- There exists a learning algorithm $\mathcal{A}$ such that if the buyer bids according to $\mathcal{A}$ then the optimal strategy for the seller is simply to post the Myerson reserve for $D$ every round.
Multi-armed Bandit Problems with Strategic Arms
We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round.
Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality
We study the tradeoff between the statistical error and communication cost of distributed statistical estimation problems in high dimensions.
Sorted Top-k in Rounds
When the comparisons are noiseless, we characterize how the optimal sample complexity depends on the number of rounds (up to a polylogarithmic factor for general $r$ and up to a constant factor for $r=1$ or 2).
Calibration, Entropy Rates, and Memory in Language Models
Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing.
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility
For Convex Set Disjointness (and the equivalent task of distributed LP feasibility) we derive upper and lower bounds of $\tilde O(d^2\log n)$ and~$\Omega(d\log n)$.
The gradient complexity of linear regression
We investigate the computational complexity of several basic linear algebra primitives, including largest eigenvector computation and linear regression, in the computational model that allows access to the data via a matrix-vector product oracle.
The Role of Randomness and Noise in Strategic Classification
Showing that if the objective is to maximize the efficiency of the classification process (defined as the accuracy of the outcome minus the sunk cost of the qualified players manipulating their features to gain a better outcome), then using randomized classifiers (that is, ones where the probability of a given feature vector to be accepted by the classifier is strictly between 0 and 1) is necessary.
Prior-free Dynamic Mechanism Design With Limited Liability
We show that under these constraints, the auctioneer can attain a constant fraction of the "sell the business" benchmark, but no more than $2/e$ of this benchmark.
Computer Science and Game Theory Theoretical Economics
Optimization-friendly generic mechanisms without money
The framework is sufficiently general to be combined with any optimization algorithm that is based on local search.
Statistically Near-Optimal Hypothesis Selection
We derive an optimal $2$-approximation learning strategy for the Hypothesis Selection problem, outputting $q$ such that $\mathsf{TV}(p, q) \leq2 \cdot opt + \eps$, with a (nearly) optimal sample complexity of~$\tilde O(\log n/\epsilon^2)$.
Understanding Influence Functions and Datamodels via Harmonic Analysis
Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017].
Welfare Distribution in Two-sided Random Matching Markets
In the model, each agent has a latent personal score for every agent on the other side of the market and her preferences follow a logit model based on these scores.
