HW1-3 are up! Check them in Lectures. Deadline for each HW is 7 days.
CAs 1-3 are up. Deadline for each CA is 14 days.
Readings
Bubeck, Sébastien. "Convex optimization: Algorithms and complexity." Foundations and Trends in Machine Learning, vol. 8, no.3-4 (2015): 231-357.
L. Bottou, F. Curtis, J. Norcedal, “Optimization Methods for Large-Scale Machine Learning”, SIAM Rev., 60(2), 223–311.
Boyd, Stephen, et al. "Distributed optimization and statistical learning via the alternating direction method of multipliers." Foundations and Trends® in Machine learning 3.1 (2011): 1-122.
Jordan, Michael I., Jason D. Lee, and Yun Yang. "Communication-efficient distributed statistical inference," Journal of the American Statistical Association, 2018.
Smith, Virginia, et al. "CoCoA: A general framework for communication-efficient distributed optimization." Journal of Machine Learning Research 18 (2018): 230.
Alistarh, Dan, et al. "QSGD: Communication-efficient SGD via gradient quantization and encoding." Advances in Neural Information Processing Systems. 2017.
Schmidt, Mark, Nicolas Le Roux, and Francis Bach. "Minimizing finite sums with the stochastic average gradient." Mathematical Programming 162.1-2 (2017): 83-112.
Boyd, Stephen, et al. "Randomized gossip algorithms," IEEE Transactions on Information Theory, 2006.
Scaman, Kevin, et al. "Optimal algorithms for smooth and strongly convex distributed optimization in networks," ICML, 2017.
Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, MIT press 2016
Fast Decentralized Optimization over Networks, https://arxiv.org/pdf/1804.02425.pdf