An Experimental Study of the Learnability of Congestion Control

When designing a distributed network protocol, typically it is infeasible to fully define the target network where the protocol is intended to be used. It is therefore natural to ask: How faithfully do protocol designers really need to understand the networks they design for? What are the important signals that endpoints should listen to? How can researchers gain confidence that systems that work well on well-characterized test networks during development will also perform adequately on real networks that are inevitably more complex, or future networks yet to be developed? Is there a tradeoff between the performance of a protocol and the breadth of its intended operating range of networks? What is the cost of playing fairly with cross-traffic that is governed by another protocol? We examine these questions quantitatively in the context of congestion control, by using an automated protocol-design tool to approximate the best possible congestion-control scheme given imperfect prior knowledge about the network. We found only weak evidence of a tradeoff between operating range in link speeds and performance, even when the operating range was extended to cover a thousand-fold range of link speeds. We found that it may be acceptable to simplify some characteristics of the network—such as its topology—when modeling for design purposes. Some other features, such as the degree of multiplexing and the aggressiveness of contending endpoints, are important to capture in a model.National Science Foundation (U.S.) (Grant CNS-1040072

Toward a Probabilistic Approach to Acquiring Information from Human Partners Using Language

Our goal is to build robots that can robustly interact with humans using natural language. This problem is extremely challenging because human language is filled with ambiguity, and furthermore, the robot's model of the environment might be much more limited than the human partner. When humans encounter ambiguity in dialog with each other, a key strategy to resolve it is to ask clarifying questions about whatthey do not understand. This paper describes an approach for enabling robots to take the same approach: asking the human partner clarifying questions about ambiguous commands in order to infer better actions. The robot fuses information from the command, the question, and the answer by creating a joint probabilistic graphical model in the Generalized Grounding Graph framework. We demonstrate that by performing inference using information from the command, question and answer, the robot is able to infer object groundings and follow commands with higher accuracythan by using the command alone

Learning perceptually grounded word meanings from unaligned parallel data

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as “Pick up the tire pallet,” as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words “the tire pallet” and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.U.S. Army Research Laboratory (Collaborative Technology Alliance Program, Cooperative Agreement W911NF-10-2-0016)United States. Office of Naval Research (MURIs N00014-07-1-0749)United States. Army Research Office (MURI N00014-11-1-0688)United States. Defense Advanced Research Projects Agency (DARPA BOLT program under contract HR0011-11-2-0008

Investigations into the robustness of computer-synthesized congestion control

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 51-53).Recent work has shown that computer-synthesized TCP congestion control protocols can outperform the state of the art. However, these protocols are generally too complex to reason about. Human engineers therefore might not trust them enough to deploy them in real networks. This thesis presents two contributions toward the practical deployment of computer-synthesized congestion-control algorithms. First, we describe a simple, human-designed protocol that performs comparably to computer-optimized protocols using only 10 lines of code, suggesting that it may be feasible to optimize for interpretability in addition to performance. Second, we introduce techniques for reasoning about the behavior of black-box protocols via extensive simulation, which reveal regions of potentially undesirable behavior in both computer-optimized protocols and a NewReno-like TCP implementation, highlighting areas to focus further engineering effort.by Pratiksha Ranjit Thaker.M. Eng

An experimental study of the learnability of congestion control

When designing a distributed network protocol, typically it is infeasible to fully define the target network where the protocol is intended to be used. It is therefore natural to ask: How faithfully do protocol designers really need to understand the networks they design for? What are the important signals that endpoints should listen to? How can researchers gain confidence that systems that work well on well-characterized test networks during development will also perform adequately on real networks that are inevitably more complex, or future networks yet to be developed? Is there a tradeoff between the performance of a protocol and the breadth of its intended operating range of networks? What is the cost of playing fairly with cross-traffic that is governed by another protocol? We examine these questions quantitatively in the context of congestion control, by using an automated protocol-design tool to approximate the best possible congestion-control scheme given imperfect prior knowledge about the network. We found only weak evidence of a tradeoff between operating range in link speeds and performance, even when the operating range was extended to cover a thousand-fold range of link speeds. We found that it may be acceptable to simplify some characteristics of the network—such as its topology—when modeling for design purposes. Some other features, such as the degree of multiplexing and the aggressiveness of contending endpoints, are important to capture in a model.National Science Foundation (U.S.) (Grant CNS-1040072

Toward information theoretic human-robot dialog

Abstract—Our goal is to build robots that can robustly interact with humans using natural language. This problem is challenging because human language is filled with ambiguity, and furthermore, due to limitations in sensing, the robot’s perception of its environment might be much more limited than that of its human partner. To enable a robot to recover from a failure to understand a natural language utterance, this paper describes an information-theoretic strategy for asking targeted clarifying questions and using information from the answer to disambiguate the language. To identify good questions, we derive anestimate of therobot’s uncertaintyaboutthemappingbetween specific phrases in the language and aspects of the external world. This metric enables the robot to ask a targeted question about the parts of the language for which it is most uncertain. After receiving an answer, the robot fuses information from the command, the question, and the answer in a joint probabilistic graphical model in the G 3 framework. When using answers to questions, we show the robot is able to infer mappings between parts of the language and concrete object groundings in the external world with higher accuracy than by using information from the command alone. Furthermore, we demonstrate that by effectively selecting which questions to ask, the robot is able to achieve significant performance gains while asking many fewer questions than baseline metrics. I

On Noisy Evaluation in Federated Hyperparameter Tuning

Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Issues of scale, privacy, and heterogeneity introduce noise in the tuning process and make it difficult to evaluate the performance of various hyperparameters. In this work, we perform the first systematic study on the effect of noisy evaluation in federated hyperparameter tuning. We first identify and rigorously explore key sources of noise, including client subsampling, data and systems heterogeneity, and data privacy. Surprisingly, our results indicate that even small amounts of noise can significantly impact tuning methods-reducing the performance of state-of-the-art approaches to that of naive baselines. To address noisy evaluation in such scenarios, we propose a simple and effective approach that leverages public proxy data to boost the evaluation signal. Our work establishes general challenges, baselines, and best practices for future work in federated hyperparameter tuning.Comment: v1: 19 pages, 15 figures, submitted to MLSys2023; v2: Fixed citation formatting; v3: Fixed typo, update acks v4: MLSys2023 camera-read