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Publications
Cross-layer communication over fading channels with adaptive decision feedback
In this paper, cross-layer design of transmitting data packets over AWGN fading channel with adaptive decision feedback is considered. The t… (voir plus)ransmitter decides the number of packets to transmit and the threshold of the decision feedback based on the queue length and the channel state. The transmit power is chosen such that the probability of error is below a pre-specified threshold. We model the system as a Markov decision process and use ideas from lattice theory to establish qualitative properties of optimal transmission strategies. In particular, we show that: (i) if the channel state remains the same and the number of packets in the queue increase, then the optimal policy either transmits more packets or uses a smaller decision feedback threshold or both; and (ii) if the number of packets in the queue remain the same and the channel quality deteriorates, then the optimal policy either transmits fewer packets or uses a larger threshold for the decision feedback or both. We also show under rate constraints that if the channel gains for all channel states are above a threshold, then the “or” in the above characterization can be replaced by “and”. Finally, we present a numerical example showing that adaptive decision feedback significantly improves the power-delay trade-off as compared with the case of no feedback.
Discovering causal relationships in data is a challenging task that involves solving a combinatorial problem for which the solution is not a… (voir plus)lways identifiable. A new line of work reformulates the combinatorial problem as a continuous constrained optimization one, enabling the use of different powerful optimization techniques. However, methods based on this idea do not yet make use of interventional data, which can significantly alleviate identifiability issues. In this work, we propose a neural network-based method for this task that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
17 The human brain differs from that of other primates, but the genetic basis of these differences 18 remains unclear. We investigated the e… (voir plus)volutionary pressures acting on almost all human 19 protein-coding genes ( N =11,667; 1:1 orthologs in primates) on the basis of their divergence 20 from those of early hominins, such as Neanderthals, and non-human primates. We confirm 21 that genes encoding brain-related proteins are among the most strongly conserved protein- 22 coding genes in the human genome. Combining our evolutionary pressure metrics for the 23 protein-coding genome with recent datasets, we found that this conservation applied to genes 24 functionally associated with the synapse and expressed in brain structures such as the 25 prefrontal cortex and the cerebellum. Conversely, several of the protein-coding genes that 26 diverge most in hominins relative to other primates are associated with brain-associated 27 diseases, such as micro/macrocephaly, dyslexia, and autism. We also showed that cerebellum 28 granule neurons express a set of divergent protein-coding genes that may have contributed to 29 the emergence of fine motor skills and social cognition in humans. This resource is available 30 from http://neanderthal.pasteur.fr and can be used to estimate evolutionary constraints acting 31 on a set of genes and to explore their relative contributions to human traits. 32
On Efficiency in Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, bo… (voir plus)th in terms of statistical as well as computational efficiency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-efits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRL methods. Specifically, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efficient, as established through a finite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efficient.
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (voir plus)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
AN ENSEMBLE APPROACH FOR DETECTING MACHINE FAILURE FROM SOUND Technical
We develop an ensemble-based approach for our submission to the anomaly detection challenge at DCASE 2020. The main members of our ensemble … (voir plus)are auto-encoders (with reconstruction error as the signal), classifiers (with negative predictive confidence as the signal), mismatch of the time-shifted signal with its Fourier-phase-shifted version, and a Gaussian mixture model on a set of common short-term features extracted from the waveform. The scores are passed through an exponential non-linearity and weighted to provide the final score, where the weighting and scaling hyper-parameters are learned on the development set. Our ensemble improves over the baseline on the development set.
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (voir plus)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (voir plus)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (voir plus)such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied b… (voir plus)y over-parameterized models. Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size. By growing the batch size for both the subsampled gradient and Hessian, we show that R-SSN can converge at a quadratic rate in a local neighbourhood of the solution. We also show that R-SSN attains local linear convergence for the family of self-concordant functions. Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global linear convergence. We empirically evaluate stochastic L-BFGS and a "Hessian-free" implementation of R-SSN for binary classification on synthetic, linearly-separable datasets and real datasets under a kernel mapping. Our experimental results demonstrate the fast convergence of these methods, both in terms of the number of iterations and wall-clock time.
We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent … (voir plus)can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.
