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Title: Simple and Efficient Architectures for Semantic Segmentation Abstract: Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use o... |
Title: Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Abstract: We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts. We propose a novel and simple accuracy metric which can be used to evaluate intr... |
Title: Catastrophic overfitting is a bug but also a feature Abstract: Despite clear computational advantages in building robust neural networks, adversarial training (AT) using single-step methods is unstable as it suffers from catastrophic overfitting (CO): Networks gain non-trivial robustness during the first stages ... |
Title: On the Surprising Behaviour of node2vec Abstract: Graph embedding techniques are a staple of modern graph learning research. When using embeddings for downstream tasks such as classification, information about their stability and robustness, i.e., their susceptibility to sources of noise, stochastic effects, or ... |
Title: Gradient-Based Adversarial and Out-of-Distribution Detection Abstract: We propose to utilize gradients for detecting adversarial and out-of-distribution samples. We introduce confounding labels -- labels that differ from normal labels seen during training -- in gradient generation to probe the effective expressi... |
Title: Gradient Descent for Low-Rank Functions Abstract: Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies significantly in only a few directions of the input space. In this paper, we levera... |
Title: ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics Abstract: Graph Neural Networks (GNN) show great promise in problems dealing with graph-structured data. One of the unique points of GNNs is their flexibility to adapt to multiple problems, which not only leads to wide applicabil... |
Title: Attention-wise masked graph contrastive learning for predicting molecular property Abstract: Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improv... |
Title: Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching Abstract: Score-based generative models have excellent performance in terms of generation quality and likelihood. They model the data distribution by matching a parameterized score network with first-order data scor... |
Title: Learning with little mixing Abstract: We study square loss in a realizable time-series framework with martingale difference noise. Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data ... |
Title: Concentration of Data Encoding in Parameterized Quantum Circuits Abstract: Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical ... |
Title: Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images Abstract: The aetiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco and infection with human papillomav... |
Title: A machine-generated catalogue of Charon's craters and implications for the Kuiper belt Abstract: In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size di... |
Title: Switchable Representation Learning Framework with Self-compatibility Abstract: Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected ... |
Title: GoodBye WaveNet -- A Language Model for Raw Audio with Context of 1/2 Million Samples Abstract: Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural archi... |
Title: On Scaled Methods for Saddle Point Problems Abstract: Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning problems, including GANS training. This paper carries out a theoretical analysis ... |
Title: Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey Abstract: Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models. Yet the more primitive a... |
Title: Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Abstract: We study the asynchronous stochastic gradient descent algorithm for distributed training over $n$ workers which have varying computation and communication frequency over time. In this algorithm, workers compute s... |
Title: Deepfake histological images for enhancing digital pathology Abstract: An optical microscopic examination of thinly cut stained tissue on glass slides prepared from a FFPE tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of any pathologist is dependen... |
Title: Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case Abstract: In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to ... |
Title: Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations Abstract: Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference approaches typicall... |
Title: Boosting the Adversarial Transferability of Surrogate Model with Dark Knowledge Abstract: Deep neural networks (DNNs) for image classification are known to be vulnerable to adversarial examples. And, the adversarial examples have transferability, which means an adversarial example for a DNN model can fool anothe... |
Title: Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data Abstract: We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaboratively train a model on their respective feat... |
Title: BYOL-Explore: Exploration by Bootstrapped Prediction Abstract: We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizin... |
Title: Constrained Submodular Optimization for Vaccine Design Abstract: Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the hum... |
Title: iBoot: Image-bootstrapped Self-Supervised Video Representation Learning Abstract: Learning visual representations through self-supervision is an extremely challenging task as the network needs to sieve relevant patterns from spurious distractors without the active guidance provided by supervision. This is achiev... |
Title: Know your audience: specializing grounded language models with the game of Dixit Abstract: Effective communication requires adapting to the idiosyncratic common ground shared with each communicative partner. We study a particularly challenging instantiation of this problem: the popular game Dixit. We formulate a... |
Title: Towards Understanding How Machines Can Learn Causal Overhypotheses Abstract: Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature in cognitive science using the ``blicket detector'' envi... |
Title: OmniMAE: Single Model Masked Pretraining on Images and Videos Abstract: Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work has studied these modalities in isolation, having a common architecture suggests that one can train ... |
Title: Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing Abstract: Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, ... |
Title: MixGen: A New Multi-Modal Data Augmentation Abstract: Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-lang... |
Title: Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability Abstract: Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatment effect estimation gave rise to ... |
Title: Interaction-Grounded Learning with Action-inclusive Feedback Abstract: Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context vector, takes an action,... |
Title: Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation Abstract: Bayesian Optimization (BO) has shown great promise for the global optimization of functions that are expensive to evaluate, but despite many successes, standard approaches can struggle in high dimensions. To improve the... |
Title: SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation Abstract: Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video driving datasets, however, fall short of capturing the mutable natur... |
Title: How accurate are the time delay estimates in gravitational lensing? Abstract: We present a novel approach to estimate the time delay between light curves of multiple images in a gravitationally lensed system, based on Kernel methods in the context of machine learning. We perform various experiments with artifici... |
Title: Machine Learning of User Profiles: Representational Issues Abstract: As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles th... |
Title: Occam factors and model-independent Bayesian learning of continuous distributions Abstract: Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phas... |
Title: Extended Comment on Language Trees and Zipping Abstract: This is the extended version of a Comment submitted to Physical Review Letters. I first point out the inappropriateness of publishing a Letter unrelated to physics. Next, I give experimental results showing that the technique used in the Letter is 3 times ... |
Title: Learning by message-passing in networks of discrete synapses Abstract: We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide rang... |
Title: Nonlinear parametric model for Granger causality of time series Abstract: We generalize a previously proposed approach for nonlinear Granger causality of time series, based on radial basis function. The proposed model is not constrained to be additive in variables from the two time series and can approximate any... |
Title: Finite size scaling of the bayesian perceptron Abstract: We study numerically the properties of the bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space... |
Title: Multiplicative Algorithm for Orthgonal Groups and Independent Component Analysis Abstract: The multiplicative Newton-like method developed by the author et al. is extended to the situation where the dynamics is restricted to the orthogonal group. A general framework is constructed without specifying the cost fun... |
Title: Predicting the expected behavior of agents that learn about agents: the CLRI framework Abstract: We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in it... |
Title: Pattern Discovery and Computational Mechanics Abstract: Computational mechanics is a method for discovering, describing and quantifying patterns, using tools from statistical physics. It constructs optimal, minimal models of stochastic processes and their underlying causal structures. These models tell us about ... |
Title: Multiplicative Nonholonomic/Newton -like Algorithm Abstract: We construct new algorithms from scratch, which use the fourth order cumulant of stochastic variables for the cost function. The multiplicative updating rule here constructed is natural from the homogeneous nature of the Lie group and has numerous meri... |
Title: MOO: A Methodology for Online Optimization through Mining the Offline Optimum Abstract: Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; ca... |
Title: A Theory of Universal Artificial Intelligence based on Algorithmic Complexity Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of s... |
Title: Modeling the Uncertainty in Complex Engineering Systems Abstract: Existing procedures for model validation have been deemed inadequate for many engineering systems. The reason of this inadequacy is due to the high degree of complexity of the mechanisms that govern these systems. It is proposed in this paper to s... |
Title: A Bayesian Reflection on Surfaces Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a c... |
Title: Integrating E-Commerce and Data Mining: Architecture and Challenges Abstract: We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini ... |
Title: Data Mining to Measure and Improve the Success of Web Sites Abstract: For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company's image, to promote and sell goods and to provide customer support. The success of a web site affects and ... |
Title: An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages Abstract: The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manual... |
Title: A Learning Approach to Shallow Parsing Abstract: A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and ... |
Title: Complexity analysis for algorithmically simple strings Abstract: Given a reference computer, Kolmogorov complexity is a well defined function on all binary strings. In the standard approach, however, only the asymptotic properties of such functions are considered because they do not depend on the reference compu... |
Title: Robust Classification for Imprecise Environments Abstract: In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to bu... |
Title: Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach Abstract: We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filte... |
Title: A Classification Approach to Word Prediction Abstract: The eventual goal of a language model is to accurately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a representation for each word as a function of words and linguistics predicates... |
Title: Design of an Electro-Hydraulic System Using Neuro-Fuzzy Techniques Abstract: Increasing demands in performance and quality make drive systems fundamental parts in the progressive automation of industrial processes. Their conventional models become inappropriate and have limited scope if one requires a precise an... |
Title: Noise Effects in Fuzzy Modelling Systems Abstract: Noise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modeling have been little investigated. This paper presents a set of tests using three well-known fuzzy modeling algorithms. These evaluate perturba... |
Title: Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-Fuzzy Compensation Abstract: Simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited its use to special applications. It is well known that controlling ... |
Title: A Fuzzy Relational Identification Algorithm and Its Application to Predict The Behaviour of a Motor Drive System Abstract: Fuzzy relational identification builds a relational model describing systems behaviour by a nonlinear mapping between its variables. In this paper, we propose a new fuzzy relational algorith... |
Title: Applications of Data Mining to Electronic Commerce Abstract: Electronic commerce is emerging as the killer domain for data mining technology. The following are five desiderata for success. Seldom are they they all present in one data mining application. 1. Data with rich descriptions. For example, wide customer ... |
Title: Fault Detection using Immune-Based Systems and Formal Language Algorithms Abstract: This paper describes two approaches for fault detection: an immune-based mechanism and a formal language algorithm. The first one is based on the feature of immune systems in distinguish any foreign cell from the body own cell. T... |
Title: Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model Abstract: We describe a slightly sub-exponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomial-time algorithm for the case of parity functions that depend on on... |
Title: Top-down induction of clustering trees Abstract: An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction ... |
Title: Web Mining Research: A Survey Abstract: With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especial... |
Title: Provably Fast and Accurate Recovery of Evolutionary Trees through Harmonic Greedy Triplets Abstract: We give a greedy learning algorithm for reconstructing an evolutionary tree based on a certain harmonic average on triplets of terminal taxa. After the pairwise distances between terminal taxa are estimated from ... |
Title: Scaling Up Inductive Logic Programming by Learning from Interpretations Abstract: When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also l... |
Title: Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal inductio... |
Title: General Loss Bounds for Universal Sequence Prediction Abstract: The Bayesian framework is ideally suited for induction problems. The probability of observing $x_t$ at time $t$, given past observations $x_1...x_{t-1}$ can be computed with Bayes' rule if the true distribution $\mu$ of the sequences $x_1x_2x_3...$ ... |
Title: Non-convex cost functionals in boosting algorithms and methods for panel selection Abstract: In this document we propose a new improvement for boosting techniques as proposed in Friedman '99 by the use of non-convex cost functional. The idea is to introduce a correlation term to better deal with forecasting of a... |
Title: An effective Procedure for Speeding up Algorithms Abstract: The provably asymptotically fastest algorithm within a factor of 5 for formally described problems will be constructed. The main idea is to enumerate all programs provably equivalent to the original problem by enumerating all proofs. The algorithm could... |
Title: Learning Policies with External Memory Abstract: In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend on the history of observations. In this paper, we explore a {\it stigmergic} approach, in which the agent's actions include the ability to set and ... |
Title: Fitness Uniform Selection to Preserve Genetic Diversity Abstract: In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization pro... |
Title: Bootstrapping Structure using Similarity Abstract: In this paper a new similarity-based learning algorithm, inspired by string edit-distance (Wagner and Fischer, 1974), is applied to the problem of bootstrapping structure from scratch. The algorithm takes a corpus of unannotated sentences as input and returns a ... |
Title: ABL: Alignment-Based Learning Abstract: This paper introduces a new type of grammar learning algorithm, inspired by string edit distance (Wagner and Fischer, 1974). The algorithm takes a corpus of flat sentences as input and returns a corpus of labelled, bracketed sentences. The method works on pairs of unstruct... |
Title: Bootstrapping Syntax and Recursion using Alignment-Based Learning Abstract: This paper introduces a new type of unsupervised learning algorithm, based on the alignment of sentences and Harris's (1951) notion of interchangeability. The algorithm is applied to an untagged, unstructured corpus of natural language s... |
Title: Market-Based Reinforcement Learning in Partially Observable Worlds Abstract: Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant pre... |
Title: Bounds on sample size for policy evaluation in Markov environments Abstract: Reinforcement learning means finding the optimal course of action in Markovian environments without knowledge of the environment's dynamics. Stochastic optimization algorithms used in the field rely on estimates of the value of a policy... |
Title: Learning to Cooperate via Policy Search Abstract: Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely... |
Title: File mapping Rule-based DBMS and Natural Language Processing Abstract: This paper describes the system of storage, extract and processing of information structured similarly to the natural language. For recursive inference the system uses the rules having the same representation, as the data. The environment of ... |
Title: Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences Abstract: Solomonoff's uncomputable universal prediction scheme $\xi$ allows to predict the next symbol $x_k$ of a sequence $x_1...x_{k-1}$ for any Turing computable, but otherwise unknown, probabilistic environment $\mu$. This scheme w... |
Title: A Sequential Model for Multi-Class Classification Abstract: Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a s... |
Title: Coupled Clustering: a Method for Detecting Structural Correspondence Abstract: This paper proposes a new paradigm and computational framework for identification of correspondences between sub-structures of distinct composite systems. For this, we define and investigate a variant of traditional data clustering, t... |
Title: Yet another zeta function and learning Abstract: We study the convergence speed of the batch learning algorithm, and compare its speed to that of the memoryless learning algorithm and of learning with memory (as analyzed in joint work with N. Komarova). We obtain precise results and show in particular that the b... |
Title: Bipartite graph partitioning and data clustering Abstract: Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender syst... |
Title: Relevant Knowledge First - Reinforcement Learning and Forgetting in Knowledge Based Configuration Abstract: In order to solve complex configuration tasks in technical domains, various knowledge based methods have been developed. However their applicability is often unsuccessful due to their low efficiency. One o... |
Title: Efficient algorithms for decision tree cross-validation Abstract: Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In t... |
Title: Machine Learning in Automated Text Categorization Abstract: The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the res... |
Title: The Use of Classifiers in Sequential Inference Abstract: We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem-identifying phrase str... |
Title: Sharpening Occam's Razor Abstract: We provide a new representation-independent formulation of Occam's razor theorem, based on Kolmogorov complexity. This new formulation allows us to: (i) Obtain better sample complexity than both length-based and VC-based versions of Occam's razor theorem, in many applications. ... |
Title: The performance of the batch learner algorithm Abstract: We analyze completely the convergence speed of the \emph{batch learning algorithm}, and compare its speed to that of the memoryless learning algorithm and of learning with memory. We show that the batch learning algorithm is never worse than the memoryless... |
Title: The Dynamics of AdaBoost Weights Tells You What's Hard to Classify Abstract: The dynamical evolution of weights in the Adaboost algorithm contains useful information about the role that the associated data points play in the built of the Adaboost model. In particular, the dynamics induces a bipartition of the da... |
Title: Learning to Play Games in Extensive Form by Valuation Abstract: A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can als... |
Title: On Learning by Exchanging Advice Abstract: One of the main questions concerning learning in Multi-Agent Systems is: (How) can agents benefit from mutual interaction during the learning process?. This paper describes the study of an interactive advice-exchange mechanism as a possible way to improve agents' learni... |
Title: Capturing Knowledge of User Preferences: ontologies on recommender systems Abstract: Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing... |
Title: Interface agents: A review of the field Abstract: This paper reviews the origins of interface agents, discusses challenges that exist within the interface agent field and presents a survey of current attempts to find solutions to these challenges. A history of agent systems from their birth in the 1960's to the ... |
Title: Exploiting Synergy Between Ontologies and Recommender Systems Abstract: Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no ini... |
Title: Self-Optimizing and Pareto-Optimal Policies in General Environments based on Bayes-Mixtures Abstract: The problem of making sequential decisions in unknown probabilistic environments is studied. In cycle $t$ action $y_t$ results in perception $x_t$ and reward $r_t$, where all quantities in general may depend on ... |
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