744 research outputs found
A Note on Batch and Incremental Learnability
AbstractAccording to Gold's criterion of identification in the limit, a learner, presented with data about a concept, is allowed to make a finite number of incorrect hypotheses before converging to a correct hypothesis. If, on the other hand, the learner is allowed to make only one conjecture which has to be correct, the resulting criterion of success is known as finite identification Identification in the limit may be viewed as an idealized model for incremental learning whereas finite identification may be viewed as an idealized model for batch learning. The present paper establishes a surprising fact that the collections of recursively enumerable languages that can be finite identified (batch learned in the ideal case) from both positive and negative data can also be identified in the limit (incrementally learned in the ideal case) from only positive data. It is often difficult to extract insights about practical learning systems from abstract theorems in inductive inference. However, this result may be seen as carrying a moral for the design of learning systems, as it yields, in theidealcase of no inaccuracies, an algorithm for converting batch systems that learn from both positive and negative data into incremental systems that learn from only positive data without any loss in learning power. This is achieved by the incremental system simulating the batch system in incremental fashion and using the heuristic of âlocalized closed-world assumptionâ to generate negative data
Algorithm selection on data streams
We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability
Learnability and Algorithm for Continual Learning
This paper studies the challenging continual learning (CL) setting of Class
Incremental Learning (CIL). CIL learns a sequence of tasks consisting of
disjoint sets of concepts or classes. At any time, a single model is built that
can be applied to predict/classify test instances of any classes learned thus
far without providing any task related information for each test instance.
Although many techniques have been proposed for CIL, they are mostly empirical.
It has been shown recently that a strong CIL system needs a strong within-task
prediction (WP) and a strong out-of-distribution (OOD) detection for each task.
However, it is still not known whether CIL is actually learnable. This paper
shows that CIL is learnable. Based on the theory, a new CIL algorithm is also
proposed. Experimental results demonstrate its effectiveness.Comment: ICML 202
Predicting and Explaining Human Semantic Search in a Cognitive Model
Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive plausibility of their implementation. Existing work has also
neglected to consider the constraints that the incremental process of language
acquisition must place on the structure of semantic memory. Here we present a
model that incrementally updates a semantic network, with limited computational
steps, and replicates many patterns found in human semantic fluency using a
simple random walk. We also perform thorough analyses showing that a
combination of both structural and semantic features are correlated with human
performance patterns.Comment: To appear in proceedings for CMCL 201
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
There have been several attempts at modeling context in robots. However,
either these attempts assume a fixed number of contexts or use a rule-based
approach to determine when to increment the number of contexts. In this paper,
we pose the task of when to increment as a learning problem, which we solve
using a Recurrent Neural Network. We show that the network successfully (with
98\% testing accuracy) learns to predict when to increment, and demonstrate, in
a scene modeling problem (where the correct number of contexts is not known),
that the robot increments the number of contexts in an expected manner (i.e.,
the entropy of the system is reduced). We also present how the incremental
model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures,
International Conference on Intelligent Robots (IROS 2018
Evaluating system utility and conceptual fit using CASSM
There is a wealth of user-centred evaluation methods (UEMs) to support the analyst in assessing interactive systems. Many of these support detailed aspects of use â for example: Is the feedback helpful? Are labels appropriate? Is the task structure optimal? Few UEMs encourage the analyst to step back and consider how well a system supports usersâ conceptual understandings and system utility. In this paper, we present CASSM, a method which focuses on the quality of âfitâ between users and an interactive system. We describe the methodology of conducting a CASSM analysis and illustrate the approach with three contrasting worked examples (a robotic arm, a digital library system and a drawing tool) that demonstrate different depths of analysis. We show how CASSM can help identify re-design possibilities to improve system utility. CASSM complements established evaluation methods by focusing on conceptual structures rather than procedures. Prototype tool support for completing a CASSM analysis is provided by Cassata, an open source development
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The Credit Problem in parametric stress: A probabilistic approach
In this paper, we introduce a novel domain-general, statistical learning model for P&P grammars: the Expectation Driven Parameter Learner (EDPL). We show that the EDPL provides a mathematically principled solution to the Credit Problem (Dresher 1999). We present the first systematic tests of the EDPL and an existing and closely related model, the NaĂŻve Parameter Learner (NPL), on a full stress typology, the one generated by Dresher & Kayeâs (1990) stress parameter framework. This framework has figured prominently in the debate about the necessity of domain-specific mechanisms for learning of parametric stress. The essential difference between the two learning models is that the EDPL incorporates a mechanism that directly tackles the Credit Problem, while the NPL does not. We find that the NPL fails to cope with the ambiguity of this stress system both in terms of learning success and data complexity, while the EDPL performs well on both metrics. Based on these results, we argue that probabilistic inference provides a viable domain-general approach to parametric stress learning, but only when learning involves an inferential process that directly addresses the Credit Problem. We also present in-depth analyses of the learning outcomes, showing how learning outcomes depend crucially on the structural ambiguities posited by a particular phonological theory, and how these learning difficulties correspond to typological gaps
Private Incremental Regression
Data is continuously generated by modern data sources, and a recent challenge
in machine learning has been to develop techniques that perform well in an
incremental (streaming) setting. In this paper, we investigate the problem of
private machine learning, where as common in practice, the data is not given at
once, but rather arrives incrementally over time.
We introduce the problems of private incremental ERM and private incremental
regression where the general goal is to always maintain a good empirical risk
minimizer for the history observed under differential privacy. Our first
contribution is a generic transformation of private batch ERM mechanisms into
private incremental ERM mechanisms, based on a simple idea of invoking the
private batch ERM procedure at some regular time intervals. We take this
construction as a baseline for comparison. We then provide two mechanisms for
the private incremental regression problem. Our first mechanism is based on
privately constructing a noisy incremental gradient function, which is then
used in a modified projected gradient procedure at every timestep. This
mechanism has an excess empirical risk of , where is the
dimensionality of the data. While from the results of [Bassily et al. 2014]
this bound is tight in the worst-case, we show that certain geometric
properties of the input and constraint set can be used to derive significantly
better results for certain interesting regression problems.Comment: To appear in PODS 201
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