… strategic agents may respond to such scoring tools to receive favorable assessments. While
prior work has focused on the short-term strategic … we establish that in our stateful setting, the …

… We formalize the notion of an optimal strategy within our stateful … provides a means to
approximate such optimal strategies. … We investigate the same strategy in our stateful framework. …

… We will refer to this setting as “causal strategic learning”, in … Thus, as we test different decision
rules and observe strategic … setting of causal strategic linear regression in the realizable …

… with shifts due to strategic responses: We show that we can use strategic responses effectively
to … between strategic responses to ML models and instrumental variable (IV) regression by …

… For generating inputs (sequences of HTTP requests) to services, we use RESTler, a stateful
… a simpler and less expensive strategy for finding service and specification regressions: …

… -stateful greybox fuzzer. In order to achieve higher code coverage, we design stateful protocol
fuzzing strategies … engine decides to regress the fuzzing state). In summary, our proposed …

… In MRCNN, region proposals -- that are vetted via classification and regression in R-CNNs
-… a combined CNN strategy for both proposal generation, scoring and position regression. …

… agents’ best responses in strategic classification de… strategic regression, the best responses
are the same for all agents and only depend on the decision rule. In this strategic regression …

… Even if stateless computations are common in embedded software, a number of components
are also stateful. Addressing stateful components requires extending our strategies and …

… filtering, logistic regression and a key/value store. We use SEEP [4], a stateful data-parallel …
We find that our strategy for doing this automatically performs well for many practical big …