Author name cluster

Armen Allahverdyan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Resolution of Simpson's paradox via the common cause principle

Arshak Hovhannisyan
Armen Allahverdyan

Simpson's paradox poses a challenge in probabilistic inference and decision-making. Our study revisits the paradox by re-estimating its frequency with an unbiased data generation process and reaffirms that it is not an artifact of deficient data collection. Thus, it can lead to incorrect recommendations in fields as diverse as statistics, psychology, and artificial intelligence. We show that the paradox can be resolved by assuming a minimal — though not necessarily observed — common cause (or screening) variable for the involved random variables. In our approach, conditioning on this minimal common cause establishes the correct association between events, which coincides with the conditioning (i. e. , fine-grained) option of the original Simpson paradox. This resolution applies to both discrete cases of binary variables and continuous settings modeled by Gaussian variables. For a non-minimal common cause, the resolution of the paradox is possible, but detailed knowledge of the common cause is required. Our findings extend traditional understandings of the paradox and offer practical guidance for resolving apparent contradictions in probabilistic inference, ultimately enhancing decision-making processes. This point is illustrated by several examples.

PDF Details

AAMAS Conference 2012 Conference Paper

Adaptive Agents on Evolving Networks

Ardeshir Kianercy
Aram Galstyan
Armen Allahverdyan

We propose a model of strategic network formation in repeated games where players adopt actions and connections simultaneously using a simple reinforcement learning scheme. We demonstrate that under certain plausible assumptions the dynamics of such systems can be described by so called replicator equations that characterize the co-evolution of agent strategies and network topology. Within this framework, the network structures emerging as a result of the game-dynamical interactions are described by the stable rest points of the replicator dynamics. In particular, we show using both simulations and analytical methods that for certain N-agent games the stable equilibria consist of star motifs as the main building blocks of the network.

PDF

NeurIPS Conference 2011 Conference Paper

Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs

Armen Allahverdyan
Aram Galstyan

We present an asymptotic analysis of Viterbi Training (VT) and contrast it with a more conventional Maximum Likelihood (ML) approach to parameter estimation in Hidden Markov Models. While ML estimator works by (locally) maximizing the likelihood of the observed data, VT seeks to maximize the probability of the most likely hidden state sequence. We develop an analytical framework based on a generating function formalism and illustrate it on an exactly solvable model of HMM with one unambiguous symbol. For this particular model the ML objective function is continuously degenerate. VT objective, in contrast, is shown to have only finite degeneracy. Furthermore, VT converges faster and results in sparser (simpler) models, thus realizing an automatic Occam's razor for HMM learning. For more general scenario VT can be worse compared to ML but still capable of correctly recovering most of the parameters.

PDF Details