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  1. ; ; ( , Journal of machine learning research)
    Fukumizu, Kenji (Ed.)
    While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i.i.d. random inputs. Nonetheless, many interesting dynamical systems have continuous states and actions and can only be identified through a judicious choice of inputs. Motivated by practical settings, we study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. To estimate such systems in finitemore »time identification methods must explore all directions in feature space. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.« less
    Free, publicly-accessible full text available February 22, 2023
  2. ; ; ; ( , Proceedings of The 24th International Conference on Artificial Intelligence and Statistics)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    We study a constrained contextual linear bandit setting, where the goal of the agent is to produce a sequence of policies, whose expected cumulative reward over the course of multiple rounds is maximum, and each one of them has an expected cost below a certain threshold. We propose an upper-confidence bound algorithm for this problem, called optimistic pessimistic linear bandit (OPLB), and prove a sublinear bound on its regret that is inversely proportional to the difference between the constraint threshold and the cost of a known feasible action. Our algorithm balances exploration and constraint satisfaction using a novel idea thatmore »scales the radii of the reward and cost confidence sets with different scaling factors. We further specialize our results to multi-armed bandits and propose a computationally efficient algorithm for this setting and prove a a regret bound that is better than simply casting multi-armed bandits as an instance of linear bandits and using the regret bound of OPLB. We also prove a lower-bound for the problem studied in the paper and provide simulations to validate our theoretical results. Finally, we show how our algorithm and analysis can be extended to multiple constraints and to the case when the cost of the feasible action is unknown.« less
    Accepted Manuscript Full Text Available
  3. ; ; ; ( , Proceedings of Machine Learning Research)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ( , Proceedings of The 24th International Conference on Artificial Intelligence and Statistics)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ; ( , Proceedings of Machine Learning Research)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ( , Proceedings of Machine Learning Research)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Numerous tasks in machine learning and artificial intelligence have been modeled as submodular maximization problems. These problems usually involve sensitive data about individuals, and in addition to maximizing the utility, privacy concerns should be considered. In this paper, we study the general framework of non-negative monotone submodular maximization subject to matroid or knapsack constraints in both offline and online settings. For the offline setting, we propose a differentially private $(1-\frac{\kappa}{e})$-approximation algorithm, where $\kappa\in[0,1]$ is the total curvature of the submodular set function, which improves upon prior works in terms of approximation guarantee and query complexity under the same privacy budget.more »In the online setting, we propose the first differentially private algorithm, and we specify the conditions under which the regret bound scales as $Ø(\sqrt{T})$, i.e., privacy could be ensured while maintaining the same regret bound as the optimal regret guarantee in the non-private setting.« less
    Accepted Manuscript Full Text Available
  7. ; ; ( , The 24th International Conference on Artificial Intelligence and Statistics)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Couplings play a central role in the analysis of Markov chain Monte Carlo algorithms and appear increasingly often in the algorithms themselves, e.g. in convergence diagnostics, parallelization, and variance reduction techniques. Existing couplings of the Metropolis-Hastings algorithm handle the proposal and acceptance steps separately and fall short of the upper bound on one-step meeting probabilities given by the coupling inequality. This paper introduces maximal couplings which achieve this bound while retaining the practical advantages of current methods. We consider the properties of these couplings and examine their behavior on a selection of numerical examples.
    Accepted Manuscript Full Text Available
  8. ; ; ( , Proceedings of Machine Learning Research)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Accepted Manuscript Full Text Available
  9. ; ; ( , International Conference on Artificial Intelligence and Statistics (AISTATS))
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Principal Component Regression (PCR) is a popular method for prediction from data, and is one way to address the so-called multi-collinearity problem in regression. It was shown recently that algorithms for PCR such as hard singular value thresholding (HSVT) are also quite robust, in that they can handle data that has missing or noisy covariates. However, such spectral approaches require strong distributional assumptions on which entries are observed. Specifically, every covariate is assumed to be observed with probability (exactly) p, for some value of p. Our goal in this work is to weaken this requirement, and as a step towardsmore »this, we study a "semi-random" model. In this model, every covariate is revealed with probability p, and then an adversary comes in and reveals additional covariates. While the model seems intuitively easier, it is well known that algorithms such as HSVT perform poorly. Our approach is based on studying the closely related problem of Noisy Matrix Completion in a semi-random setting. By considering a new semidefinite programming relaxation, we develop new guarantees for matrix completion, which is our core technical contribution.« less
    Accepted Manuscript Full Text Available
  10. ; ; ( , Proceedings of Machine Learning Research)
    Banerjee, Arindam ; Fukumizu, Kenji (Ed.)
    Accepted Manuscript Full Text Available