7 accepted papers
We address differentially private stochastic bandit problems by leveraging Thompson Sampling with Gaussian priors and Gaussian differential privacy (GDP). We propose DP-TS-UCB, a novel parametrized private algorithm that enables trading off privacy and regret. DP-TS-UCB satisfies $ \tilde{O} \l…
The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. While Vision Transformer (ViT) based models scale effectively to large data regimes, they fail to capitalize on this inher…
We propose two Thompson Sampling-like, model-based learning algorithms for episodic Markov decision processes (MDPs) with a finite time horizon. Our proposed algorithms are inspired by Optimistic Thompson Sampling (O-TS), empirically studied in Chapelle and Li [2011], May et al. [2012] for stochas…
We address differentially private stochastic bandits. We present two (near)-optimal Thompson Sampling-based learning algorithms: DP-TS and Lazy-DP-TS. The core idea in achieving optimality is the principle of optimism in the face of uncertainty. We reshape the posterior distribution in an optimis…
Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated input samples such as continual learning and reinforcement learning. Existing work has shown that SGDm with a de…
We consider differentially private algorithms for reinforcement learning in continuous spaces, such that neighboring reward functions are indistinguishable. This protects the reward information from being exploited by methods such as inverse reinforcement learning. Existing studies that guarantee di…
We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses. The decision maker may choose for a set of actions, where the outcome of an action is corrupted by independent noise. In this paper we consider…