Debabrota Basu

Dri-MED adapts to user preferences and context drifts, achieving significantly lower regret than traditional methods in dynamic environments.

Database tuning just got easier: DOT dynamically identifies and optimizes key parameters on-the-fly, outperforming existing methods without the need for costly warm-up phases.

Optimal sequential hypothesis testing with Markovian data is now possible, thanks to a new lower bound that incorporates both stationary distribution and transition structure, leading to an asymptotically optimal test.

Knowing the sequence of model updates lets you pinpoint when a specific data point was added to the training set, making membership inference attacks far more potent than previously thought.