Michael Christoph Gastpar

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A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes tra ...

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, ou ...

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the high-dimensional setting, it is known that recovery with a vanishing fraction ...

It is well known and surprising that the uncoded transmission of an independent and identically distributed Gaussian source across an additive white Gaussian noise channel is optimal: No amount of sophistication in the coding strategy can ever perform better. What makes uncoded transmission optimal? In this thesis, it is shown that the optimality of uncoded transmission can be understood as the perfect match of four involved measures: the probability distribution of the source, its distortion measure, the c

Algebraic network information theory is an emerging facet of network information theory, studying the achievable rates of random code ensembles that have algebraic structure, such as random linear codes. A distinguishing feature is that linear combinations ...

The distributed remote source coding (the so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for t ...

Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, ‘MC’), and using a brain-m

The classical problem in network coding theory considers communication over multicast networks. Multiple transmitters send independent messages to multiple receivers which decode the same set of messages. In this work, computation over multicast networks is considered: each receiver decodes an identical function of the original messages. For a countably infinite class of two-transmitter two-receiver single-hop linear deterministic networks, the computing capacity is characterized for a linear function (modu

The classical distributed storage problem can be modeled by a k-uniform complete hyper-graph where vertices represent servers and hyper-edges represent users. Hence each hyper-edge should be able to recover the full file using only the memories of the vert ...

In the celebrated coded caching problem studied by Maddah-Ali and Niesen, the peak-traffic network load is to be reduced by first caching some information about contents into individual memories of end users during the off-peak hours and then upon user req ...

The goal of a class of sensor networks is to monitor an underlying physical reality at the highest possible fidelity. Sensors acquire noisy measurements and have to communicate them over a power- and possibly bandwidth-constrained interference channel to a set of base stations. The goal of this paper is to analyze, as a function of the number of sensors, the trade-offs between the degrees of freedom of the underlying physical reality, the communication resources (power, temporal and spatial bandwidth), and

This paper presents a joint typicality framework for encoding and decoding nested linear codes in multi-user networks. This framework provides a new perspective on compute–forward within the context of discrete memoryless networks. In particular, it establ ...

The classical problem in a network coding theory considers communication over multicast networks. Multiple transmitters send independent messages to multiple receivers that decode the same set of messages. In this paper, computation over multicast networks ...

In this paper, we consider communication on a two-hop channel in which a source wants to send information reliably and securely to the destination via a relay. We consider both the untrusted relay case and the external eavesdropper case. In the untrusted r ...

This paper considers the sample-efficiency of preference learning, which models and predicts human choices based on comparative judgments. The minimax optimal estimation error rate Θ(d/n) in classical estimation theory requires that the number of samples n scales linearly with the dimensionality of the feature space d. However, the high dimensionality of the feature space and the high cost of collecting human-annotated data challenge the efficiency of traditional estimation methods. To remedy this, we lever

We consider a physical-layer network coding strategy for the random-access channel, based on compute-and-forward. When packets collide, it is possible to reliably recover a linear combination of the packets at the receiver. Over many rounds of transmission ...

The coded cooperative data exchange problem is studied for the fully connected network. In this problem, each node initially only possesses a subset of the K packets making up the file. Nodes make broadcast transmissions that are received by all other node ...

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve informatio ...

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the diff ...

Michael Christoph Gastpar

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Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

To code or not to code

Compute-Forward for DMCs: Simultaneous Decoding of Multiple Combinations

Remote Source Coding Under Gaussian Noise: Dueling Roles of Power and Entropy Power

Redundant information encoding in primary motor cortex during natural and prosthetic motor control

Computation in Multicast Networks: Function Alignment and Converse Theorems

GDSP: A graphical perspective on the distributed storage systems

Successive Refinement to Caching for Dynamic Requests

Power-bandwidth-distortion scaling laws for sensor networks

A Joint Typicality Approach to Compute–Forward

Computation in Multicast Networks: Function Alignment and Converse Theorems

Secure Transmission on the Two-Hop Relay Channel With Scaled Compute-and-Forward

Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective

Random Access With Physical-Layer Network Coding

Cooperative data exchange based on MDS codes

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Differential Entropy of the Conditional Expectation under Gaussian Noise