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Title: A kernel for time series based on global alignments Abstract: We propose in this paper a new family of kernels to handle times series, notably speech data, within the framework of kernel methods which includes popular algorithms such as the Support Vector Machine. These kernels elaborate on the well known Dynami... |
Title: Fitness Uniform Optimization 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 progress on the one hand and i... |
Title: Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections Abstract: For dimension reduction in $l_1$, the method of {\em Cauchy random projections} multiplies the original data matrix $\mathbf{A} \in\mathbb{R}^{n\times D}$ with a random matrix $\mathbf{R} \in \mathbb{R... |
Title: Considering users' behaviours in improving the responses of an information base Abstract: In this paper, our aim is to propose a model that helps in the efficient use of an information system by users, within the organization represented by the IS, in order to resolve their decisional problems. In other words we... |
Title: Low-complexity modular policies: learning to play Pac-Man and a new framework beyond MDPs Abstract: In this paper we propose a method that learns to play Pac-Man. We define a set of high-level observation and action modules. Actions are temporally extended, and multiple action modules may be in effect concurrent... |
Title: Evolving controllers for simulated car racing Abstract: This paper describes the evolution of controllers for racing a simulated radio-controlled car around a track, modelled on a real physical track. Five different controller architectures were compared, based on neural networks, force fields and action sequenc... |
Title: Hedging predictions in machine learning Abstract: Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This paper describes a new technique for "hedging" the predictions output by many such algorithms, including support vect... |
Title: A Relational Approach to Functional Decomposition of Logic Circuits Abstract: Functional decomposition of logic circuits has profound influence on all quality aspects of the cost-effective implementation of modern digital systems. In this paper, a relational approach to the decomposition of logic circuits is pro... |
Title: CSCR:Computer Supported Collaborative Research Abstract: It is suggested that a new area of CSCR (Computer Supported Collaborative Research) is distinguished from CSCW and CSCL and that the demarcation between the three areas could do with greater clarification and prescription. |
Title: How Random is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos Abstract: Symbolic dynamics has proven to be an invaluable tool in analyzing the mechanisms that lead to unpredictability and random behavior in nonlinear dynamical systems. Surprisingly, a discrete partition of contin... |
Title: Very Sparse Stable Random Projections, Estimators and Tail Bounds for Stable Random Projections Abstract: This paper will focus on three different aspects in improving the current practice of stable random projections. Firstly, we propose {\em very sparse stable random projections} to significantly reduce the pr... |
Title: Functional Bregman Divergence and Bayesian Estimation of Distributions Abstract: A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence acts on functions or distributions, and generalizes the standard Bregman div... |
Title: Low-rank matrix factorization with attributes Abstract: We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user's interest in a particular prod... |
Title: A Unified View of TD Algorithms; Introducing Full-Gradient TD and Equi-Gradient Descent TD Abstract: This paper addresses the issue of policy evaluation in Markov Decision Processes, using linear function approximation. It provides a unified view of algorithms such as TD(lambda), LSTD(lambda), iLSTD, residual-gr... |
Title: A Novel Bayesian Classifier using Copula Functions Abstract: A useful method for representing Bayesian classifiers is through \emph{discriminant functions}. Here, using copula functions, we propose a new model for discriminants. This model provides a rich and generalized class of decision boundaries. These decis... |
Title: Player co-modelling in a strategy board game: discovering how to play fast Abstract: In this paper we experiment with a 2-player strategy board game where playing models are evolved using reinforcement learning and neural networks. The models are evolved to speed up automatic game development based on human invo... |
Title: Loop corrections for approximate inference Abstract: We propose a method for improving approximate inference methods that corrects for the influence of loops in the graphical model. The method is applicable to arbitrary factor graphs, provided that the size of the Markov blankets is not too large. It is an alter... |
Title: Approximation of the Two-Part MDL Code Abstract: Approximation of the optimal two-part MDL code for given data, through successive monotonically length-decreasing two-part MDL codes, has the following properties: (i) computation of each step may take arbitrarily long; (ii) we may not know when we reach the optim... |
Title: Using state space differential geometry for nonlinear blind source separation Abstract: Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) seeks to find a "source" time series, comprised of statistically independent combinations of the measured com... |
Title: Statistical Mechanics of On-line Learning when a Moving Teacher Goes around an Unlearnable True Teacher Abstract: In the framework of on-line learning, a learning machine might move around a teacher due to the differences in structures or output functions between the teacher and the learning machine. In this pap... |
Title: Time Series Forecasting: Obtaining Long Term Trends with Self-Organizing Maps Abstract: Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method... |
Title: A Delta Debugger for ILP Query Execution Abstract: Because query execution is the most crucial part of Inductive Logic Programming (ILP) algorithms, a lot of effort is invested in developing faster execution mechanisms. These execution mechanisms typically have a low-level implementation, making them hard to deb... |
Title: Algorithmic Complexity Bounds on Future Prediction Errors Abstract: We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor $M$ from the true distribution $mu$ by the algorithmic complexity of $mu$. Here we a... |
Title: Universal Algorithmic Intelligence: A mathematical top->down approach Abstract: Sequential 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 o... |
Title: Support and Quantile Tubes Abstract: This correspondence studies an estimator of the conditional support of a distribution underlying a set of i.i.d. observations. The relation with mutual information is shown via an extension of Fano's theorem in combination with a generalization bound based on a compression ar... |
Title: Bandit Algorithms for Tree Search Abstract: Bandit based methods for tree search have recently gained popularity when applied to huge trees, e.g. in the game of go (Gelly et al., 2006). The UCT algorithm (Kocsis and Szepesvari, 2006), a tree search method based on Upper Confidence Bounds (UCB) (Auer et al., 2002... |
Title: Intrinsic dimension of a dataset: what properties does one expect? Abstract: We propose an axiomatic approach to the concept of an intrinsic dimension of a dataset, based on a viewpoint of geometry of high-dimensional structures. Our first axiom postulates that high values of dimension be indicative of the prese... |
Title: Structure induction by lossless graph compression Abstract: This work is motivated by the necessity to automate the discovery of structure in vast and evergrowing collection of relational data commonly represented as graphs, for example genomic networks. A novel algorithm, dubbed Graphitour, for structure induct... |
Title: Reinforcement Learning for Adaptive Routing Abstract: Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. W... |
Title: Similarity-Based Models of Word Cooccurrence Probabilities Abstract: In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations ``eat a peach'' and ``e... |
Title: Evolution of Neural Networks to Play the Game of Dots-and-Boxes Abstract: Dots-and-Boxes is a child's game which remains analytically unsolved. We implement and evolve artificial neural networks to play this game, evaluating them against simple heuristic players. Our networks do not evaluate or predict the final... |
Title: Practical algorithms for on-line sampling Abstract: One of the core applications of machine learning to knowledge discovery consists on building a function (a hypothesis) from a given amount of data (for instance a decision tree or a neural network) such that we can use it afterwards to predict new instances of ... |
Title: A Winnow-Based Approach to Context-Sensitive Spelling Correction Abstract: A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their targe... |
Title: Machine Learning of Generic and User-Focused Summarization Abstract: A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their a... |
Title: Learning to Resolve Natural Language Ambiguities: A Unified Approach Abstract: We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can be re-cast as learning linear separators in the feature space. Each of the meth... |
Title: Forgetting Exceptions is Harmful in Language Learning Abstract: We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language p... |
Title: TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search Abstract: In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with minimax search. We present some experiments in both chess and backgammon which demonstrate its uti... |
Title: KnightCap: A chess program that learns by combining TD(lambda) with game-tree search Abstract: In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our chess program ``KnightCap'' use... |
Title: Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity Abstract: The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify the general modeling principles MDL and MML, abstracted as the ideal MDL principle and defined f... |
Title: A Discipline of Evolutionary Programming Abstract: Genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions. We propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on ... |
Title: Probabilistic Inductive Inference:a Survey Abstract: Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm ... |
Title: Using Collective Intelligence to Route Internet Traffic Abstract: A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present ... |
Title: General Principles of Learning-Based Multi-Agent Systems Abstract: We consider the problem of how to design large decentralized multi-agent systems (MAS's) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into on... |
Title: An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery Abstract: This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that... |
Title: Inducing a Semantically Annotated Lexicon via EM-Based Clustering Abstract: We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation. The models are empirically evalutated by a general decis... |
Title: Inside-Outside Estimation of a Lexicalized PCFG for German Abstract: The paper describes an extensive experiment in inside-outside estimation of a lexicalized probabilistic context free grammar for German verb-final clauses. Grammar and formalism features which make the experiment feasible are described. Success... |
Title: Statistical Inference and Probabilistic Modelling for Constraint-Based NLP Abstract: We present a probabilistic model for constraint-based grammars and a method for estimating the parameters of such models from incomplete, i.e., unparsed data. Whereas methods exist to estimate the parameters of probabilistic con... |
Title: Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer Abstract: The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurre... |
Title: Linear and Order Statistics Combiners for Pattern Classification Abstract: Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical fra... |
Title: Robust Combining of Disparate Classifiers through Order Statistics Abstract: Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In the typical setting investigated till now, each classifier is train... |
Title: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition Abstract: This paper presents the MAXQ approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP... |
Title: State Abstraction in MAXQ Hierarchical Reinforcement Learning Abstract: Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstract actions are defined that can perform many primitive actions before terminating. However, little is known about ... |
Title: Cascaded Grammatical Relation Assignment Abstract: In this paper we discuss cascaded Memory-Based grammatical relations assignment. In the first stages of the cascade, we find chunks of several types (NP,VP,ADJP,ADVP,PP) and label them with their adverbial function (e.g. local, temporal). In the last stage, we a... |
Title: Memory-Based Shallow Parsing Abstract: We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F-value for the Wall S... |
Title: Automatically Selecting Useful Phrases for Dialogue Act Tagging Abstract: We present an empirical investigation of various ways to automatically identify phrases in a tagged corpus that are useful for dialogue act tagging. We found that a new method (which measures a phrase's deviation from an optimally-predicti... |
Title: MAP Lexicon is useful for segmentation and word discovery in child-directed speech Abstract: Because of rather fundamental changes to the underlying model proposed in the paper, it has been withdrawn from the archive. |
Title: Collective Intelligence for Control of Distributed Dynamical Systems Abstract: We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, ``The American Economic Review'', 84(2): 406--411 (1994), D. Challet and Y.C. Zhang, ``Physica A'', 256:514 (1998)). We view it as an instance of the... |
Title: An Introduction to Collective Intelligence Abstract: This paper surveys the emerging science of how to design a ``COllective INtelligence'' (COIN). A COIN is a large multi-agent system where: (i) There is little to no centralized communication or control; and (ii) There is a provided world utility function that ... |
Title: A statistical model for word discovery in child directed speech Abstract: A statistical model for segmentation and word discovery in child directed speech is presented. An incremental unsupervised learning algorithm to infer word boundaries based on this model is described and results of empirical tests showing ... |
Title: New Error Bounds for Solomonoff Prediction Abstract: Solomonoff sequence prediction is a scheme to predict digits of binary strings without knowing the underlying probability distribution. We call a prediction scheme informed when it knows the true probability distribution of the sequence. Several new relations ... |
Title: HMM Specialization with Selective Lexicalization Abstract: We present a technique which complements Hidden Markov Models by incorporating some lexicalized states representing syntactically uncommon words. Our approach examines the distribution of transitions, selects the uncommon words, and makes lexicalized sta... |
Title: Algorithmic Statistics Abstract: While Kolmogorov complexity is the accepted absolute measure of information content of an individual finite object, a similarly absolute notion is needed for the relation between an individual data sample and an individual model summarizing the information in the data, for exampl... |
Title: Learning Complexity Dimensions for a Continuous-Time Control System Abstract: This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that input signals have only a finite number k of frequency components, and systems to be identified have dimension ... |
Title: Mathematics of learning Abstract: We study the convergence properties of a pair of learning algorithms (learning with and without memory). This leads us to study the dominant eigenvalue of a class of random matrices. This turns out to be related to the roots of the derivative of random polynomials (generated by ... |
Title: Harmonic mean, random polynomials and stochastic matrices Abstract: Motivated by a problem in learning theory, we are led to study the dominant eigenvalue of a class of random matrices. This turns out to be related to the roots of the derivative of random polynomials (generated by picking their roots uniformly a... |
Title: Robust Estimators under the Imprecise Dirichlet Model Abstract: Walley's Imprecise Dirichlet Model (IDM) for categorical data overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets o... |
Title: A tutorial introduction to the minimum description length principle Abstract: This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the ... |
Title: Suboptimal behaviour of Bayes and MDL in classification under misspecification Abstract: We show that forms of Bayesian and MDL inference that are often applied to classification problems can be *inconsistent*. This means there exists a learning problem such that for all amounts of data the generalization errors... |
Title: Learning a Machine for the Decision in a Partially Observable Markov Universe Abstract: In this paper, we are interested in optimal decisions in a partially observable Markov universe. Our viewpoint departs from the dynamic programming viewpoint: we are directly approximating an optimal strategic tree depending ... |
Title: Fast Non-Parametric Bayesian Inference on Infinite Trees Abstract: Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesia... |
Title: Strong Asymptotic Assertions for Discrete MDL in Regression and Classification Abstract: We study the properties of the MDL (or maximum penalized complexity) estimator for Regression and Classification, where the underlying model class is countable. We show in particular a finite bound on the Hellinger losses un... |
Title: Combinations and Mixtures of Optimal Policies in Unichain Markov Decision Processes are Optimal Abstract: We show that combinations of optimal (stationary) policies in unichain Markov decision processes are optimal. That is, let M be a unichain Markov decision process with state space S, action space A and polic... |
Title: MDL Convergence Speed for Bernoulli Sequences Abstract: The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) t... |
Title: Generalization error bounds in semi-supervised classification under the cluster assumption Abstract: We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumption relating the behavior of the r... |
Title: Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe Abstract: Revision of the paper previously entitled "Learning a Machine for the Decision in a Partially Observable Markov Universe" In this paper, we are interested in optimal decisions in a partially observable universe. Ou... |
Title: Bayesian Regression of Piecewise Constant Functions Abstract: We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive ... |
Title: Entropy And Vision Abstract: In vector quantization the number of vectors used to construct the codebook is always an undefined problem, there is always a compromise between the number of vectors and the quantity of information lost during the compression. In this text we present a minimum of Entropy principle t... |
Title: Graph Laplacians and their convergence on random neighborhood graphs Abstract: Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in... |
Title: Occam's hammer: a link between randomized learning and multiple testing FDR control Abstract: We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or more generally, a probability distribution on said ... |
Title: Cross-Entropy method: convergence issues for extended implementation Abstract: The cross-entropy method (CE) developed by R. Rubinstein is an elegant practical principle for simulating rare events. The method approximates the probability of the rare event by means of a family of probabilistic models. The method ... |
Title: Strategies for prediction under imperfect monitoring Abstract: We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense ... |
Title: The Loss Rank Principle for Model Selection Abstract: We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) ... |
Title: Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction Abstract: Discovering relevant, but possibly hidden, variables is a key step in constructing useful and predictive theories about the natural world. This brief note explains t... |
Title: A Model for Prejudiced Learning in Noisy Environments Abstract: Based on the heuristics that maintaining presumptions can be beneficial in uncertain environments, we propose a set of basic axioms for learning systems to incorporate the concept of prejudice. The simplest, memoryless model of a deterministic learn... |
Title: Metrics for more than two points at once Abstract: The conventional definition of a topological metric over a space specifies properties that must be obeyed by any measure of "how separated" two points in that space are. Here it is shown how to extend that definition, and in particular the triangle inequality, t... |
Title: Combinatorial Approach to Object Analysis Abstract: We present a perceptional mathematical model for image and signal analysis. A resemblance measure is defined, and submitted to an innovating combinatorial optimization algorithm. Numerical Simulations are also presented |
Title: The information bottleneck method Abstract: We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal $y\in \Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech ... |
Title: Predictability, complexity and learning Abstract: We define {\em predictive information} $I_{\rm pred} (T)$ as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times $T$: $I_{\rm pred} (T)$ can remain fini... |
Title: Information theory and learning: a physical approach Abstract: We try to establish a unified information theoretic approach to learning and to explore some of its applications. First, we define {\em predictive information} as the mutual information between the past and the future of a time series, discuss its be... |
Title: Structural Inference of Hierarchies in Networks Abstract: One property of networks that has received comparatively little attention is hierarchy, i.e., the property of having vertices that cluster together in groups, which then join to form groups of groups, and so forth, up through all levels of organization in... |
Title: Parametric Inference for Biological Sequence Analysis Abstract: One of the major successes in computational biology has been the unification, using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied towards these ... |
Title: Fluctuation-dissipation theorem and models of learning Abstract: Advances in statistical learning theory have resulted in a multitude of different designs of learning machines. But which ones are implemented by brains and other biological information processors? We analyze how various abstract Bayesian learners ... |
Title: Metric learning pairwise kernel for graph inference Abstract: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a super... |
Title: Algorithmic Theories of Everything Abstract: The probability distribution P from which the history of our universe is sampled represents a theory of everything or TOE. We assume P is formally describable. Since most (uncountably many) distributions are not, this imposes a strong inductive bias. We show that P(x)... |
Title: Improved Bounds on Quantum Learning Algorithms Abstract: In this article we give several new results on the complexity of algorithms that learn Boolean functions from quantum queries and quantum examples. Hunziker et al. conjectured that for any class C of Boolean functions, the number of quantum black-box queri... |
Title: ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings Abstract: We present a scalable approach for learning open-world object-goal navigation (ObjectNav) -- the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e.g., "find a sink"). Our appro... |
Title: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks Abstract: Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations. Here we propose a convolutional neura... |
Title: Debiasing Learning for Membership Inference Attacks Against Recommender Systems Abstract: Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. ... |
Title: Data Leakage in Federated Averaging Abstract: Recent attacks have shown that user data can be reconstructed from FedSGD updates, thus breaking privacy. However, these attacks are of limited practical relevance as federated learning typically uses the FedAvg algorithm. It is generally accepted that reconstructing... |
Title: On Certifying and Improving Generalization to Unseen Domains Abstract: Domain Generalization (DG) aims to learn models whose performance remains high on unseen domains encountered at test-time by using data from multiple related source domains. Many existing DG algorithms reduce the divergence between source dis... |
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