Thomas Schwarz

Thomas Schwarz, Ryann Sim, Chun Kai Ling

The study of learning in games typically assumes that each player always has access to all of their actions. However, in many practical scenarios, players' available actions might be restricted due to exogenous stochasticity. To model this setting, for a game $\mathcal{G}_{\mathrm{orig}}$ with action set $A_i$ for each player $i$, we introduce the corresponding Game with Stochastic Action Sets (GSAS) which is parametrized by a probability distribution over the players' set of possible action subsets $\mathcal{S}_i \subseteq 2^{\vert A_i\vert}\backslash\{\varnothing\}$. In a GSAS, players' strategies and Nash equilibria (NE) admit prohibitively large representations, and existing algorithms for NE computation scale poorly. Under the assumption that action availabilities are independent between players, we show that NE in two-player zero-sum (2p0s) GSAS can be compactly represented by a vector of size $\vert A_i\vert$, overcoming the naïve exponential-sized representation. Computationally, we introduce an efficient algorithm called SI-MWU that minimizes sleeping internal regret, converging to NE with high probability in 2p0s-GSAS with rate $O(\sqrt{\log\vert A_i\vert/T})$. Finally, using the SI-MWU iterates, we develop a procedure based on stochastic approximation to recover compactly represented NE.

Thomas Schwarz, Cecilia Casolo, Niki Kilbertus

In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatments within specific budget constraints is critical. Despite recent advancements in estimating counterfactual trajectories, a direct link to optimal treatment selection based on these estimates is missing. This paper introduces a novel method integrating counterfactual estimation techniques and uncertainty quantification to recommend personalized treatment plans adhering to predefined cost constraints. Our approach is distinctive in its handling of continuous treatment variables and its incorporation of uncertainty quantification to improve prediction reliability. We validate our method using two simulated datasets, one focused on the cardiovascular system and the other on COVID-19. Our findings indicate that our method has robust performance across different counterfactual estimation baselines, showing that introducing uncertainty quantification in these settings helps the current baselines in finding more reliable and accurate treatment selection. The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.

Sushil Jajodia, Witold Litwin, Thomas Schwarz

We propose the client-side AES256 encryption for a cloud SQL DB. A column ciphertext is deterministic or probabilistic. We trust the cloud DBMS for security of its run-time values, e.g., through a moving target defense. The client may send AES key(s) with the query. These serve the on-the-fly decryption of selected ciphertext into plaintext for query evaluation. The DBMS clears the key(s) and the plaintext at the query end at latest. It may deliver ciphertext to decryption enabled clients or plaintext otherwise, e.g., to browsers/navigators. The scheme functionally offers to a cloud DBMS capabilities of a plaintext SQL DBMS. AES processing overhead appears negligible for a modern CPU, e.g., a popular Intel I5. The determin-istic encryption may have no storage overhead. The probabilistic one doubles the DB storage. The scheme seems the first generally practical for an outsourced encrypted SQL DB. An implementation sufficient to practice with appears easy. An existing cloud SQL DBMS with UDF support should do.