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Found 10 papers, 3 papers with code
The global economic impact of AI technologies in the fight against financial crime
Is the rapid adoption of Artificial Intelligence a sign that creative destruction (a capitalist innovation process first theorised in 1942) is occurring?
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters.
Reinforcement Learning in Newcomblike Environments
This gives us a powerful tool for reasoning about the limit behaviour of agents -- for example, it lets us show that there are Newcomblike environments in which a reinforcement learning agent cannot converge to any optimal policy.
MPC-Friendly Commitments for Publicly Verifiable Covert Security
We address the problem of efficiently verifying a commitment in a two-party computation.
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis
We address this need by creating products for MSLWEB that use novelty detection to help operations staff identify unusual data that might be diagnostic of new or atypical compositions or mineralogies detected within an imaging scene.
Beyond COVID-19: Network science and sustainable exit strategies
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society
Differentially Private Health Tokens for Estimating COVID-19 Risk
Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0. 03 on average and thus the aggregated results can be useful in a number of identity-free contexts.
Cryptography and Security Computers and Society
Private Protocols for U-Statistics in the Local Model and Beyond
In this paper, we study the problem of computing $U$-statistics of degree $2$, i. e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP).
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party.
The Privacy Blanket of the Shuffle Model
Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user.
