Javed A. Aslam

ICML Conference 2019 Conference Paper

Scaling Up Ordinal Embedding: A Landmark Approach

• Jesse Anderton
• Javed A. Aslam

Ordinal Embedding is the problem of placing n objects into R^d to satisfy constraints like "object a is closer to b than to c. " It can accommodate data that embeddings from features or distances cannot, but is a more difficult problem. We propose a novel landmark-based method as a partial solution. At small to medium scales, we present a novel combination of existing methods with some new theoretical justification. For very large values of n optimizing over an entire embedding breaks down, so we propose a novel method which first embeds a subset of m << n objects and then embeds the remaining objects independently and in parallel. We prove a distance error bound for our method in terms of m and that it has O(dn log m) time complexity, and show empirically that it is able to produce high quality embeddings in a fraction of the time needed for any published method.

ICML Conference 2016 Conference Paper

Conditional Bernoulli Mixtures for Multi-label Classification

• Cheng Li 0051
• Bingyu Wang
• Virgil Pavlu
• Javed A. Aslam

Multi-label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi-label classification method based on Conditional Bernoulli Mixtures. Our proposed method has several attractive properties: it captures label dependencies; it reduces the multi-label problem to several standard binary and multi-class problems; it subsumes the classic independent binary prediction and power-set subset prediction methods as special cases; and it exhibits accuracy and/or computational complexity advantages over existing approaches. We demonstrate two implementations of our method using logistic regressions and gradient boosted trees, together with a simple training procedure based on Expectation Maximization. We further derive an efficient prediction procedure based on dynamic programming, thus avoiding the cost of examining an exponential number of potential label subsets. Experimental results show the effectiveness of the proposed method against competitive alternatives on benchmark datasets.

IROS Conference 2015 Conference Paper

Anytime planning of optimal schedules for a mobile sensing robot

• Jingjin Yu
• Javed A. Aslam
• Sertac Karaman
• Daniela Rus

We study the problem in which a mobile sensing robot is tasked to travel among and gather intelligence at a set of spatially distributed points-of-interest (POIs). The quality of the information collected at a POI is characterized by some sensory (reward) function of time. With limited fuel, the robot must balance between spending time traveling to more POIs and performing time-consuming sensing activities at POIs to maximize the overall reward. In a dual formulation, the robot is required to acquire a minimum amount of reward with the least amount of time. We propose an anytime planning algorithm for solving these two NP-hard problems to arbitrary precision for arbitrary reward functions. The algorithm is effective on large instances with tens to hundreds of POIs, as demonstrated with an extensive set of computational experiments. Besides mobile sensor scheduling, our algorithm also applies to automation scenarios such as intelligent and optimal itinerary planning.

YNIMG Journal 2001 Journal Article

The fMRI data center: An introduction

• Jeffrey S. Grethe
• John D. Van Horn
• Jeffrey B. Woodward
• Souheil Inati
• Peter J. Kostelec
• Javed A. Aslam
• Daniel Rockmore
• Daniela Rus

SODA Conference 1999 Conference Paper

A Practical Clustering Algorithm for Static and Dynamic Information Organization

• Javed A. Aslam
• Katya Pelekhov
• Daniela Rus

SODA Conference 1999 Conference Paper

Improved Bicriteria Existence Theorems for Scheduling

• Javed A. Aslam
• April Rasala
• Cliff Stein 0001
• Neal E. Young

I&C Journal 1998 Journal Article

General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting

• Javed A. Aslam
• Scott E. Decatur

We derive general bounds on the complexity of learning in the statistical query (SQ) model and in the PAC model with classification noise. We do so by considering the problem of boosting the accuracy of weak learning algorithms which fall within the SQ model. This new model was introduced by Kearns to provide a general framework for efficient PAC learning in the presence of classification noise. We first show a general scheme for boosting the accuracy of weak SQ learning algorithms, proving that weak SQ learning is equivalent to strong SQ learning. The boosting is efficient and is used to show our main result of the first general upper bounds on the complexity of strong SQ learning. Since all SQ algorithms can be simulated in the PAC model with classification noise, we also obtain general upper bounds on learning in the presence of classification noise for classes which can be learned in the SQ model.

FOCS Conference 1993 Conference Paper

General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Bounding

• Javed A. Aslam
• Scott E. Decatur

We derive general bounds on the complexity of learning in the statistical query model and in the PAC model with classification noise. We do so by considering the problem of boosting the accuracy of weak learning algorithms which fall within the statistical query model. This new model was introduced by M. Kearns (1993) to provide a general framework for efficient PAC learning in the presence of classification noise. >

TCS Journal 1993 Journal Article

On-line algorithms for 2-coloring hypergraphs via chip games

• Javed A. Aslam
• Aditi Dhagat

Erdős has shown that, for all k-hypergraphs with fewer than 2 k−1 edges, there exists a 2-coloring of the nodes so that no edge is monochromatic. Erdős has also shown that, when the number of edges is greater than k 22 k+1, there exist k-hypergraphs with no such 2-coloring. These bounds are not constructive, however. In this paper, we taken an “on-line” look at this problem, showing constructive upper and lower bounds on the number of edges of a hypergraph which allow it to be 2-colored on-line. These bounds become particularly interesting for degree-k k-hypergraphs, which always have a good 2-coloring for all k ⩾10 by the Lovász Local Lemma. In this case, our upper bound demonstrates an inherent weakness of on-line strategies by constructing an adversary which defeats any on-line 2-coloring algorithm using degree-k k-hypergraphs with (3 + 2 √2) k edges.

STOC Conference 1991 Conference Paper

Searching in the Presence of Linearly Bounded Errors (Extended Abstract)

• Javed A. Aslam
• Aditi Dhagat