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Found 18 papers, 1 papers with code
Regression with Sensor Data Containing Incomplete Observations
This leads to a bias toward lower values in labels and the resultant learning because labels may have lower values due to incomplete observations, even if the actual magnitude of the phenomenon was high.
Online Learning in Supply-Chain Games
We study a repeated game between a supplier and a retailer who want to maximize their respective profits without full knowledge of the problem parameters.
Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology
We are interested in in silico evaluation methodology for molecular optimization methods.
Proofs and additional experiments on Second order techniques for learning time-series with structural breaks
We provide complete proofs of the lemmas about the properties of the regularized loss function that is used in the second order techniques for learning time-series with structural breaks in Osogami (2021).
Supplementary material for Uncorrected least-squares temporal difference with lambda-return
Here, we provide a supplementary material for Takayuki Osogami, "Uncorrected least-squares temporal difference with lambda-return," which appears in {\it Proceedings of the 34th AAAI Conference on Artificial Intelligence} (AAAI-20).
Visual analytics for team-based invasion sports with significant events and Markov reward process
To solve this problem, we consider a whole match as a Markov chain of significant events, so that event values can be estimated with a continuous parameter space by solving the Markov chain with a machine learning model.
Real-time tree search with pessimistic scenarios
Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave.
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps
This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution.
Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions
Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed.
Boltzmann machines for time-series
We then review dynamic Boltzmann machines (DyBMs), whose learning rule is local in time.
Bidirectional learning for time-series models with hidden units
Hidden units can play essential roles in modeling time-series having long-term dependency or on-linearity but make it difficult to learn associated parameters.
Learning binary or real-valued time-series via spike-timing dependent plasticity
A dynamic Boltzmann machine (DyBM) has been proposed as a model of a spiking neural network, and its learning rule of maximizing the log-likelihood of given time-series has been shown to exhibit key properties of spike-timing dependent plasticity (STDP), which had been postulated and experimentally confirmed in the field of neuroscience as a learning rule that refines the Hebbian rule.
Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences
We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM).
Learning dynamic Boltzmann machines with spike-timing dependent plasticity
We propose a particularly structured Boltzmann machine, which we refer to as a dynamic Boltzmann machine (DyBM), as a stochastic model of a multi-dimensional time-series.
Restricted Boltzmann machines modeling human choice
We extend the multinomial logit model to represent some of the empirical phenomena that are frequently observed in the choices made by humans.
Solving inverse problem of Markov chain with partial observations
The Markov chain is a convenient tool to represent the dynamics of complex systems such as traffic and social systems, where probabilistic transition takes place between internal states.
Robustness and risk-sensitivity in Markov decision processes
We also show that a risk-sensitive MDP of minimizing an iterated risk measure that is composed of certain coherent risk measures is equivalent to a robust MDP of minimizing the worst-case expectation when the possible deviations of uncertain parameters from their nominal values are characterized with a concave function.
