VLDB 2026 Research / reviewers in the wild / expert
Alexander Sauter
dblp:287/7636
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 10 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes
error detection |
0.9 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Coding theory › channel coding › finite blocklength coding
finite blocklength bounds |
0.9 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Coding theory › error-correcting codes › error detection
undetected error probability |
0.9 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference |
0.6 | 1 | 2022 | A Deep Variational Approach to Clustering Survival Data · ICLR 2022 |
Data mining
clustering |
0.6 | 1 | 2022 | A Deep Variational Approach to Clustering Survival Data · ICLR 2022 |
Coding theory › error-correcting codes › decoding
channel decoding |
0.3 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Coding theory › error-correcting codes › code construction › concatenated code construction
CRC-aided polar codes |
0.3 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Coding theory › channel coding
polar codes |
0.3 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Coding theory › error-correcting codes › decoding › list decoding
successive cancellation list decoding |
0.3 | 1 | 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar Codes · IEEE Trans. Commun. 2025 |
Bioinformatics and computational biology
survival analysis |
0.2 | 1 | 2022 | A Deep Variational Approach to Clustering Survival Data · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
deep variational inference · 1.7threshold test · 0.9forney's optimal rule · 0.9finite blocklength achievability bounds · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Undetected Error Probability in the Short Blocklength Regime: Approaching Finite-Blocklength Bounds With Polar CodesabstractWe analyze the trade-off between the undetected error probability (i.e., the probability that the channel decoder outputs an erroneous message without detecting the error) and the total error probability in the short blocklength regime. We address the problem by developing two new finite blocklength achievability bounds, which we use to benchmark the performance of two coding schemes based on polar codes with outer cyclic redundancy check (CRC) codes—also referred to as CRC-aided (CA) polar codes. The first bound is obtained by considering an outer detection code, whereas the second bound relies on a threshold test applied to the generalized information density. Similarly, in the first CA polar code scheme, we reserve a fraction of the outer CRC parity bits for error detection, whereas in the second scheme, we apply a threshold test (specifically, Forney’s optimal rule) to the output of the successive cancellation list decoder. Numerical simulations performed on the binary-input AWGN channel reveal that, in the short-blocklength regime, the threshold-based approach is superior to the CRC-based approach, both in terms of bounds and performance of CA polar code schemes. We also consider the case of decoding with noisy channel-state information, which leads to a mismatched decoding setting. Our results illustrate that, differently from the previous case, in this scenario, the CRC-based approach outperforms the threshold-based approach, which is more sensitive to the mismatch. Alexander Sauter, Ahmet Oguz Kislal, Giuseppe Durisi, Gianluigi Liva, Balázs Matuz, Erik G. Ström |
IEEE Trans. Commun. | 1 |
| 2022 | Coherent Communications for Free Space Optical Low-Earth Orbit DownlinksabstractThis work addresses physical layer design aspects of coherent free-space optical downlinks from low-earth orbit satellites to ground. Achievable information rates are derived and assessed that include the availability of diversity, shaping, bit-metric decoding, repetition coding and automatic repeat request with maximum-ratio combining. A channel coding scheme is presented that approaches the theoretic limits within 1 dB. Extrinsic information transfer analysis for the free-space optical fading channel shows that a code design tailored to the additive white Gaussian noise channel is robust for fading channels with various parameters. Balázs Matuz, Ayman Zahr, Alexander Sauter |
GLOBECOM | 3 |
| 2022 | A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Thomas J. Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt |
ICLR | 5 |