Amr Ahmed
Amr Ahmed is a Senior Staff Research Scientist at Google. He received his M.Sc and PhD degrees from the School of Computer Science, Carnegie Mellon University in 2009 and 2011, respectively. He received the best paper award at KDD 2014 , the best Paper Award at WSDM 2014, the 2012 ACM SIGKDD Doctoral Dissertation Award, and a best paper award (runner-up) at WSDM 2012. He co-chaired the WWW'18 track on Web Content Analysis and served as an Area Chair for IJCAI 2019, SIGIR 2019, SIGIR 2018, ICML 2018, ICML 2017, KDD 2016, WSDM 2015, ICML 2014, and ICDM 2014. His research interests include large-scale machine learning, data/web mining, user modeling, personalization, social networks and content analysis.
Authored Publications
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Scalable Hierarchical Agglomerative Clustering
Nick Monath
Guru Prashanth Guruganesh
Andrew McCallum
Gokhan Mergen
Mert Terzihan
Bryon Tjanaka
Yuchen Wu
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021), 1245–1255
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The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective [32]. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method’s scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art.
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Exact and Approximate Hierarchical Clustering Using A*
Craig Greenberg
Sebastian Macaluso
Nicholas Monath
Patrick Flaherty
Kyle Cranmer
Andrew McCallum
Uncertainty in Artificial Intelligence (2021)
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Hierarchical clustering is known to be broadly applicable in myriad domains. Despite its extensive use, existing approximate inference methods are insufficient for applications that require either exact or high-quality approximate solutions.For example, in high energy physics, we are interested in discovering high-quality jet structures, which are hierarchical clusterings of particles.In this paper we view inference as a search problem and focus on inferring high-quality hierarchies for a given cost function, rather than using ad hoc (e.g., greedy or beam) methods. This leads naturally to the use of A*, which has seldom been used for clustering (with the notable exception of \citep{daume2007fast}). Unlike ad hoc search methods, A* carries with it optimality guarantees. However, applying A* search naively leads to a large space and time complexity. To address this challenge, we develop a novel augmented trellis data structure and dynamic programming algorithm for A* that result in substantially improved time and space complexity bounds while still computing the globally optimal hierarchical clustering. We demonstrate that our proposed method increases the number of points for which an exact solution can be found by 25$\%$ compared with previous work \cite{greenberg2020compact}. Furthermore, our approach yields a natural approximation that scales to larger datasets and achieves substantially higher quality results than ad hoc search baselines, motivating its use in applications demanding exact or high-quality approximations.
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DAG-structured Clustering by Nearest-Neighbors
Nicholas Monath
Andrew McCallum
International Conference on Artificial Intelligence and Statistics (2021)
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Hierarchical clusterings compactly encode multiple granularities of clusters within a tree structure. Hierarchies, by definition, fail to capture different flat partitions that are not subsumed in one another. In this paper, we advocate for an alternative structure for representing multiple alternative clusterings, a directed acyclic graph (DAG). By allowing nodes to have multiple parents, DAG structure is not only more flexible than a tree but also allows for points to be members of multiple clusters. We describe a large-scale, map-reduce-based algorithm to infer these DAGs. Our algorithm works by simply merging nearest neighbor substructures to form a DAG structure. Our algorithm is supported with theoretical guarantees showing its representational capacity over tree-based algorithms. Further, we provide comprehensive empirical experiments on large-scale clustering benchmarks and entity resolution datasets. Our results show that our method is as scalable as and more accurate than state-of-the-art tree-based techniques.
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Non-Stationary Off-policy Optimization
Joey Hong
Branislav Kveton
International Conference on Artificial Intelligence and Statistics (AISTATS) (2021)
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Off-policy learning is a framework for estimating the value of and optimizing policies offline from logged data without deploying them. Real-world environments are nonstationary, and the optimized policies should be able to adapt to these changes. To address this challenge, we study the novel problem of off-policy optimization in piecewise-stationary environments. Our key idea is to use a change-point detector to partition the logged data into categorical latent states, then find a near-optimal policy conditioned on latent state. We derive high-probability bounds on our off-policy estimates and optimization. Furthermore, we also propose a practical approach to deploy our policy online and evaluate our approach comprehensively on a real-world clickstream dataset.
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Big Bird: Transformers for Longer Sequences
Guru Prashanth Guruganesh
Joshua Ainslie
Anirudh Ravula
Qifan Wang
Li Yang
NeurIPS (2020)
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Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (in terms of memory mainly) on the sequence length due to their full attention mechanism. To remedy this, we propose, \emph{BigBird}, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that \emph{BigBird} is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis demonstrates the need for having an O(1) global tokens, such as CLS, that attend to the entire sequence as part of the sparse attentions. We show that the proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, \emph{BigBird} drastically improves performance on various NLP tasks such as question answering.
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Latent Bandits Revisited
Joey Hong
Branislav Kveton
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 13423-13433
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A latent bandit is a bandit problem where the learning agent knows reward distributions of arms conditioned on an unknown discrete latent state. The goal of the agent is to identify the latent state, after which it can act optimally. This setting is a natural midpoint between online and offline learning, where complex models can be learned offline and the agent identifies the latent state online. This is of high practical relevance, for instance in recommender systems. In this work, we propose general algorithms for latent bandits, based on both upper confidence bounds and Thompson sampling. The algorithms are contextual, and aware of model uncertainty and misspecification. We provide a unified theoretical analysis of our algorithms, which have lower regret than classic bandit policies when the number of latent states is smaller than actions. A comprehensive empirical study showcases the advantages of our approach.
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Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space
Nick Monath
Daniel Silva
Andrew McCallum
The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19) (2019)
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Hierarchical clustering is typically performed using algorithmic-based optimization searching over the discrete space of trees. While these optimization methods are often effective, their discreteness restricts them from many of the benefits of their continuous counterparts, such as scalable stochastic optimization and the joint optimization of multiple objectives or components of a model (e.g.end-to-end training). In this paper, we present an approach for hierarchical clustering that searches over continuous representations of trees in hyperbolic space by running gradient descent. We compactly represent uncertainty over tree structures with vectors in the Poincaré ball. We show how the vectors can be optimized using an objective related to recently proposed cost functions for hierarchical clustering. Using our method with a mini-batch stochastic gradient descent inference procedure, we are able to outperform prior work on clustering millions of ImageNet images by 15 points of dendrogram purity. Further, our continuous tree representation can be jointly optimized in multi-task learning applications offering a 9 point improvement over baseline methods
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Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models
Daniel Silva
Yuchen Wu
Shibani Sanan
Surojit Chatterjee
Proceedings of the 12 ACM International Conference on Web Search and Data Mining (2019), pp. 186-194
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Long Short-Term Memory (LSTM) is one of the most powerful sequence models for user browsing history [17, 22] or natural language text [19]. Despite the strong performance, it has not gained popularity for user-facing applications, mainly owing to a large number of parameters and lack of interpretability. Recently Zaheer et al. [25] introduced latent LSTM Allocation (LLA) to address these problems by incorporating topic models with LSTM, where the topic model maps observed words in each sequence to topics that evolve using an LSTM model. In our experiments, we found the resulting model, although powerful and interpretable, to show shortcomings when applied to sequence data that exhibit multi-modes of behaviors with abrupt dynamic changes. To address this problem we introduce thLLA: a threading LLA model. thLLA has the ability to break each sequence into a set of segments and then model the dynamic in each segment using an LSTM mixture. In that way, thLLA can model abrupt changes in sequence dynamics and provides a better fit for sequence data while still being interpretable and requiring fewer parameters. In addition, thLLA uncovers hidden themes in the data via its dynamic mixture components. However, such generalization and interpretability come at a cost of complex dependence structure, for which inference would be extremely non-trivial. To remedy this, we present an efficient sampler based on particle MCMC method for inference that can draw from the joint posterior directly. Experimental results confirm the superiority of thLLA and the stability of the new inference algorithm on a variety of domains.
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Learning continuous representations of discrete objects such as text, sentences, users, and movies lies at the heart of many applications including involving text and user modeling. Unfortunately, traditional methods that embed all objects do not scale to large vocabulary sizes and embedding dimensions. In this paper, we propose a general method, Anchor & Transform (ANT) that learns sparse representations of discrete objects by jointly learning a small set of \textit{anchor embeddings} and a \textit{sparse transformation} from anchor objects to all objects. ANT is scalable, flexible, end-to-end trainable, and allows the user to easily incorporate domain knowledge about object relationships. ANT also recovers several task-specific baselines under certain structural assumptions on the anchor embeddings and transformation matrices. On several benchmarks involving text and user modeling, ANT demonstrates strong performance with respect to accuracy and sparsity.
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Recurrent Recommender Networks
Chao-Yuan Wu
Alex Beutel
Alexander J. Smola
How Jing
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (2017), pp. 495-503
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Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.
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