We study a novel variant of the multi-armed bandit problem, where at each time step, the player observes a context that determines the arms' mean rewards. However, playing an arm blocks it (across all contexts) for a fixed number of future time steps. This model extends the blocking bandits model (Basu et al., NeurIPS19) to a contextual setting, and captures important scenarios such as recommendation systems or ad placement with diverse users, and processing diverse pool of jobs. This contextual setting, however, invalidates greedy solution techniques that are effective for its non-contextual counterpart. Assuming knowledge of the mean reward for each arm-context pair, we design a randomized LP-based algorithm which is $\alpha$-optimal in (large enough) $T$ time steps, where $\alpha = \tfrac{d_{\max}}{2d_{\max}-1}\left(1- \epsilon\right)$ for any $\epsilon >0$, and $d_{max}$ is the maximum delay of the arms. In the bandit setting, we show that a UCB based variant of the above online policy guarantees $\mathcal{O}\left(\log T\right)$ regret w.r.t. the $\alpha$-optimal strategy in $T$ time steps, which matches the $\Omega(\log(T))$ regret lower bound in this setting. Due to the time correlation caused by the blocking of arms, existing techniques for upper bounding regret fail. As a first, in the presence of such temporal correlations, we combine ideas from coupling of non-stationary Markov chains and opportunistic sub-sampling with suboptimality charging techniques from combinatorial bandits to prove our regret upper bounds.

This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to modeling and integrating dynamical uncertainties into a safe planning framework, and finding a solution in a computationally tractable way. In this work, we first develop a probabilistic model for dynamical uncertainties. Then, we provide a framework to generate a path that maximizes safety for complex missions by incorporating the uncertainty model. We also devise a Monte Carlo method to obtain a safe path efficiently. Finally, we evaluate the performance of our approach and compare it to potential alternatives in several case studies.

Probability models have been proposed in the literature to account for "intelligent" behavior in many contexts. In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles. The backward flow provides precious background information to the agent's behavior, viz., inferences coming from the future determine the agent's actions. Probability tensors are layered in time in both directions in a manner similar to convolutional neural networks. The discussion is carried out with reference to a set of simulated grids where, despite the apparent task complexity, a solution, if feasible, is always found. The original model proposed by Attias has been extended to include non-absorbing obstacles, multiple goals and multiple agents. The emerging behaviors are very realistic and demonstrate great potentials of the application of this framework to real environments.

We devise a novel neural network-based universal denoiser for the finite-input, general-output (FIGO) channel. Based on the assumption of known noisy channel densities, which is realistic in many practical scenarios, we train the network such that it can denoise as well as the best sliding window denoiser for any given underlying clean source data. Our algorithm, dubbed as Generalized CUDE (Gen-CUDE), enjoys several desirable properties; it can be trained in an unsupervised manner (solely based on the noisy observation data), has much smaller computational complexity compared to the previously developed universal denoiser for the same setting, and has much tighter upper bound on the denoising performance, which is obtained by a theoretical analysis. In our experiments, we show such tighter upper bound is also realized in practice by showing that Gen-CUDE achieves much better denoising results compared to other strong baselines for both synthetic and real underlying clean sequences.

Distributionally Robust Optimization (DRO) has enabled to prove the equivalence between robustness and regularization in classification and regression, thus providing an analytical reason why regularization generalizes well in statistical learning. Although DRO's extension to sequential decision-making overcomes $\textit{external uncertainty}$ through the robust Markov Decision Process (MDP) setting, the resulting formulation is hard to solve, especially on large domains. On the other hand, existing regularization methods in reinforcement learning only address $\textit{internal uncertainty}$ due to stochasticity. Our study aims to facilitate robust reinforcement learning by establishing a dual relation between robust MDPs and regularization. We introduce Wasserstein distributionally robust MDPs and prove that they hold out-of-sample performance guarantees. Then, we introduce a new regularizer for empirical value functions and show that it lower bounds the Wasserstein distributionally robust value function. We extend the result to linear value function approximation for large state spaces. Our approach provides an alternative formulation of robustness with guaranteed finite-sample performance. Moreover, it suggests using regularization as a practical tool for dealing with $\textit{external uncertainty}$ in reinforcement learning methods.

Unlike conventional techniques for speaker adaptation, which attempt to improve the accuracy of the segmentation using acoustic models that are more robust in the face of the speaker's characteristics, we aim to use only context dependent characteristics extrapolated with linguistic analysis techniques. In simple terms, we use the intuitive idea that context dependent information is tightly correlated with the related acoustic waveform. We propose a statistical model, which predicts correcting values to reduce the systematic error produced by a state-of-the-art Hidden Markov Model (HMM) based speech segmentation. In other words, we can predict how HMM-based Automatic Speech Recognition (ASR) systems interpret the waveform signal determining the systematic error in different contextual scenarios. Our approach consists of two phases: (1) identifying contextdependent phonetic unit classes (for instance, the class which identifies vowels as being the nucleus of monosyllabic words); and (2) building a regression model that associates the mean error value made by the ASR during the segmentation of a single speaker corpus to each class. The success of the approach is evaluated by comparing the corrected boundaries of units and the state-of-the-art HHM segmentation against a reference alignment, which is supposed to be the optimal solution. The results of this study show that the contextdependent correction of units' boundaries has a positive influence on the forced alignment, especially when the misinterpretation of the phone is driven by acoustic properties linked to the speaker's phonetic characteristics. In conclusion, our work supplies a first analysis of a model sensitive to speaker-dependent characteristics, robust to defective and noisy information, and a very simple implementation which could be utilized as an alternative to either more expensive speaker-adaptation systems or of numerous manual correction sessions.

Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the existence of the discrete random variables prohibits many basic gradient-based inference engines, which makes the inference procedure on such models particularly challenging. Existing PPSs either require the user to manually marginalize out the discrete variables or to perform a composing inference by running inference separately on discrete and continuous variables. The former is infeasible in most cases whereas the latter has some fundamental shortcomings. We present a novel approach to run inference efficiently and robustly in such programs using stochastic gradient Markov Chain Monte Carlo family of algorithms. We compare our stochastic gradient-based inference algorithm against conventional baselines in several important cases of probabilistic programs with mixed support, and demonstrate that it outperforms existing composing inference baselines and works almost as well as inference in marginalized versions of the programs, but with less programming effort and at a lower computation cost.

In many sequential decision-making problems, the goal is to optimize a utility function while satisfying a set of constraints on different utilities. This learning problem is formalized through Constrained Markov Decision Processes (CMDPs). In this paper, we investigate the exploration-exploitation dilemma in CMDPs. While learning in an unknown CMDP, an agent should trade-off exploration to discover new information about the MDP, and exploitation of the current knowledge to maximize the reward while satisfying the constraints. While the agent will eventually learn a good or optimal policy, we do not want the agent to violate the constraints too often during the learning process. In this work, we analyze two approaches for learning in CMDPs. The first approach leverages the linear formulation of CMDP to perform optimistic planning at each episode. The second approach leverages the dual formulation (or saddle-point formulation) of CMDP to perform incremental, optimistic updates of the primal and dual variables. We show that both achieves sublinear regret w.r.t.\ the main utility while having a sublinear regret on the constraint violations. That being said, we highlight a crucial difference between the two approaches; the linear programming approach results in stronger guarantees than in the dual formulation based approach.

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first and second order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real-world phenomena, we introduce an approach that merges higher order spectral analysis (HOSA) with deep convolutional neural networks (CNNs) for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.

Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we study the privacy-utility trade-off (PUT) in time-series data sharing. Existing approaches to PUT mainly focus on a single data point; however, temporal correlations in time-series data introduce new challenges. Methods that preserve the privacy for the current time may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We consider sharing the distorted version of a user's true data sequence with an untrusted third party. We measure the privacy leakage by the mutual information between the user's true data sequence and shared version. We consider both instantaneous and average distortion between the two sequences, under a given distortion measure, as the utility loss metric. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL). We apply our optimal data release policies to location trace privacy scenario, and evaluate the performance of the proposed policy numerically.