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Gabriele Farina is a computer scientist who studies machine learning, optimization and game theory, especially decision-making with incomplete information. His research connects individual learning rules with the collective behaviour of interacting decision-makers.^[ref]

One focus is no-regret learning: can repeated local adaptation lead to a useful game-theoretic equilibrium? The computational question matters because a multi-agent system must find strategies through available observations and calculations. His doctoral work studied learning and equilibrium computation in imperfect-information games.^[ref]

Homepage: www.eecs.mit.edu/people/gabriele-farina/

In online learning, a learner chooses $a_t$ before observing the round's loss function ${\ell }_t$. Its external cumulative regret against a fixed action set $A$ isNo-regret learning seeks a guarantee $R_T=o(T)$, often in expectation for a randomized algorithm, under stated feedback and adversary assumptions. The average excess loss then tends to zero. The comparator is the best single action in hindsight; it does not choose the best action separately on every round.

For example, against two prediction rules, an exponential-weights learner starts with equal weights and multiplies rule $i$'s weight by $\exp (-\eta {\ell }_t(i))$ after seeing all rule losses. Sampling in proportion to the weights gradually shifts probability toward better rules. For losses in $[0,1]$, an appropriate learning rate gives an order-$\sqrt{T}$ expected bound for this fixed two-rule setting. This compares performance even without independent observations from one fixed distribution; it does not guarantee small absolute loss when every rule predicts badly.