Searching for Nearest Strings with Neural-Like String Embedding

We analyze an approach to a similarity preserving coding of symbol sequences based on neural distributed representations and show that it can be viewed as a metric embedding process

Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system. A challenge arises by the fact that risk-averse commercial SMT systems deterministically log the most probable translation. The lack of sufficient exploration of the SMT output space seemingly contradicts the theoretical requirements for counterfactual learning. We show that counterfactual learning from deterministic bandit logs is possible nevertheless by smoothing out deterministic components in learning. This can be achieved by additive and multiplicative control variates that avoid degenerate behavior in empirical risk minimization. Our simulation experiments show improvements of up to 2 BLEU points by counterfactual learning from deterministic bandit feedback.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017, Copenhagen, Denmar

Abuse of Public Use? Exploring the SmithKline v. Apotex Decision and the Future of Public Use, 4 J. Marshall Rev. Intell. Prop. L. 559 (2005)

In SmithKline Beecham Corp. v. Apotx Corp., the court incorrectly applied the statutory public use bar and held the clinical trials did not constitute an experimental use. This ruling set the bar too high. Applying a narrow construction of the law, the CAFC invalidated a claim in a clear case of experimental use. The decision not only misapplied the precedent defining an “inherent” feature of the invention, but also essentially eliminated the need for applying the policies that underlie and define the public use bar under 35 U.S.C. § 102(b)

On Handling Replay Attacks in Intrusion Detection Systems

We propose a method for detecting and analyzing the so-called replay attacks in intrusion detection systems, when an intruder contributes a small amount of hostile actions to a recorded session of a legitimate user or process, and replays this session back to the system. The proposed approach can be applied if an automata-based model is used to describe behavior of active entities in a computer system

Approaches to Sequence Similarity Representation

We discuss several approaches to similarity preserving coding of symbol sequences and possible connections of their distributed versions to metric embeddings. Interpreting sequence representation methods with embeddings can help develop an approach to their analysis and may lead to discovering useful properties